Background
Previous retrospective results are evaluated prospectively and blinded.

Methods
A total of 221 eyes previously classified as normal (G1),279 as moderate risk of glaucoma (G2) and 217 as high risk (G3) according to the Globin Discriminant Function (GDF) Laguna-ONhE index were examined with OCT Spectralis.

Results
In G1, the Bruch’s Membrane Opening Minimum Rim Width (BMO-MRW) was 332 ± 55 microns; in G2, it was 252 ± 47 (p < 0.0001); and in G3, 23144 (p < 0.0001). In G1, the 1% and 5% percentiles were 233 and 248, respectively; in G2, they were lower in 28.80% and 42.29% of cases, respectively; and in G3, in 50.23% and 63.59% of cases, respectively. Most of the cases were normal-tension glaucomas. Laguna-ONhE indices showed a curvilinear correlation with BMO-MRW results. The Retinal Nerve Fibre Layer (RNFL) showed a poor relationship with BMO. Assuming G1 to be truly normal,BMO-MRW would have a Receiver operating characteristic (ROC) curve area of 0.901 for G2 and G3 and 0.651 for RNFL. A significant reduction in pixels corresponding to vessels was found in G2 and G3 vs. G1 (p < 0.0001).

Conclusions
In some cases, these defects appear to be mainly glaucomatous, and in others, they are associated with diabetic microangiopathy. In normal tension glaucoma, RNFL defects may be less severe than those inside the nerve.

‍

PURPOSE
To examine the real-world performance of a support vector machine learning software (RetinaLyze) in order to identify the possible presence of diabetic retinopathy (DR) in patients with diabetes via software implementation in clinical practice.

METHODS
1001 eyes from 1001 patients-one eye per patient-participating in the Danish National Screening Programme were included. Three independent ophthalmologists graded all eyes according to the International Clinical Diabetic Retinopathy Disease Severity Scale with the exact level of disease being determined by majority decision. The software detected DR and no DR and was compared to the ophthalmologists' gradings.

RESULTS
At a clinical chosen threshold, the software showed a sensitivity, specificity, positive predictive value and negative predictive value of 84.9% (95% CI: 81.8-87.9), 89.9% (95% CI: 86.8-92.7), 92.1% (95% CI: 89.7-94.4), and 81.0% (95% CI: 77.2-84.7), respectively, when compared to human grading. The results from the routine screening were 87.0% (95% CI: 84.2-89.7), 85.3% (95% CI: 81.8-88.6), 89.2% (95% CI: 86.3-91.7), and 82.5% (95% CI: 78.5-86.0), respectively. AUC was 93.4%. The reference graders Conger's Exact Kappa was 0.827.

CONCLUSION
The software performed similarly to routine grading with overlapping confidence intervals, indicating comparable performance between the two groups. The intergrader agreement was satisfactory. However, evaluating the updated software alongside updated clinical procedures is crucial. It is therefore recommended that further clinical testing before implementation of the software as a decision support tool is conducted.

Background
Optic nerve head (ONH) interpretation is a glaucoma screening method which may be influenced by criteria variability. Laguna ONhE software (also known as RetinaLyze Glaucoma) is a low-cost and non-invasive method of ONH analysis.

Methods
We analysed the results of the Laguna ONhE application, interpreting 552 ONH images from the ACRIMA database, publicly available on the Internet, and compared them with the opinion of five experts. Diagnostic agreement was investigated using Cohen’s kappa () with 95% confidence. Results: The kappa concordance index obtained with Laguna ONhE and the majority of the experts’ criterion (0.77) was significantly higher compared to that obtained with ACRIMA and the majority of the experts’ criterion (0.61). In 44.7% of the cases there was absolute agreement among the 5 experts and the Laguna ONhE program. Removing borderline cases from the analysis yielded increased diagnostic agreement (0.81). The area under the receiver operating characteristic (AUROC) of the Laguna ONhE program (0.953, p < 0.001) was not significantly different than AUROC of the majority of the experts’ criterion (0.925, p < 0.001), p = 0.052. Individually obtained expert’s AUROCs were significantly lower (0.636 to 0.913; p < 0.01).

Conclusions
Laguna ONhE’s agreement with the experts is high, particularly where the diagnosis may be more obvious by the appearance of the ONH.

Précis
The Laguna ONhE, a software that measures the hemoglobin (Hb) concentration of the optic nerve head (ONH) from fundus photographs, demonstrated good accuracy in discriminating healthy eyes from eyes with mild glaucoma.

Purpose

The aim was to evaluate Hb concentration of the optic nerve to distinguish between healthy eyes and eyes with mild glaucoma.

Methods
Eyes from patients with mild primary open angle glaucoma (MD > −6 dB) (n=58) and from healthy subjects (n=64) were selected. Retinal nerve fiber layer thickness measurements of all eyes were acquired with optical coherence tomography. Optic disc photographs were also obtained, and the images were analyzed using the Laguna ONhE software, which measures the amount of Hb in 24 sectors of the ONH. The software also calculates the Glaucoma Discriminant Function (GDF), an index that expresses the chance of the ONH being compatible with glaucoma. Areas under the receiver operating characteristic curve and sensitivities at fixed specificities of 90% and 95% of each Laguna ONhE parameter were calculated.

Results
The mean retinal nerve fiber layer thickness and vertical cup/disc ratio of the control and glaucoma groups were 90.0±10.6 μm versus 66.28±9.85 μm (P<0.001) and 0.5±0.09 versus 0.65±0.09 (P<0.001), respectively. Total Hb (67.9±4.45 vs. 62.89±4.89, P<0.001) and GDF (11.57±15.34 vs. −27.67±20.94, P<0.001) were significantly higher in the control group. The Hb concentration was also significantly higher in 21 of the 24 sectors in the control group compared with the glaucoma group (P<0.05). The GDF had the largest areas under the receiver operating characteristic curve (0.93), with 79.3% sensitivity at a fixed specificity of 95%.

Conclusion
‍Measurements of optic nerve Hb concentration using a colorimetry photographic device demonstrated good accuracy in discriminating healthy eyes from eyes with mild glaucoma. Further studies are need to understand vascular factors implicated in the development of glaucoma.

Background
Laguna ONhE provides a globin distribution function (GDF), in which a glaucoma discriminator based on deep learning plays an important role, and there is also an optimized globin individual pointer (GIP) for progression analysis.

Methods
Signs of optic nerve glaucoma were identified in 1,124,885 fundus images from 203,115 diabetics obtained over 15 years and 117,813 control images.

Results
A total of 743,696 images from 313,040 eyes of 173,661 diabetics were analysed. Some exclusions occurred due to excessive illumination, poor quality, or the absence of optic discs. Suspicion of glaucoma was reported in 6.70%, for an intended specificity of 99% (GDF < -15). More signs of glaucoma occur in diabetics as their years of disease increase, and after age 60, compared to controls. The GIP detected progression (p < 0.01) in 2.59% of cases with 4 controls and in 42.6% with 14 controls was higher in cases with lower GDF values. The GDF was corrected for the disc area and proved to be independent of it (r = 0.001925; p = 0.2814).

Conclusions
The GDF index suggests a higher and increasing glaucoma probability in diabetics over time. Doubling the number of check-ups from four to eight increases the ability to detect GIP index progression by a factor of 5.

Objective
‍To identify age-related vascular changes in the optic discs of patients with diabetes with and without signs of glaucoma.

‍Methods and analysis
‍A total of 2153 eyes of 1797 patients with diabetes without significant retinopathy were monitored with 10 Topcon-NW400 images obtained over 10.27±1.58 years. 571 non-diabetics eyes were selected as controls. Laguna ONhE uses convolutional neural networks to identify optic disc edges, vessels, cup and rim, and provides a glaucoma assessment index—Globin Distribution Function (GDF).

Results
In the first image, vessel pixels accounted for 33.88% of the disc area (SD=3.72) in non-glaucoma (DN) and 31.35% (SD=4.05; p<0.0001) in glaucoma cases (DG). This number of pixels was reduced by −0.55% each year (SD=0.77) in the DN and −0.76% (SD=0.86; p=0.0014) in the DG. In the first image, 76.55% of the disc pixels (SD=11.13) belonged to the rim in the DN and 62.05% (SD=11.00; p=0.0014) in the DG, decreasing annually by −0.33% (SD=0.99) in the DN and −0.68% (SD=1.08; p<0.00001) in the DG groups. All rim sectors were reduced over time in the DG group, particularly superotemporal (41°–80°) and inferotemporal (271°–310°). The reduction was smaller in DN, presenting as progressive thickening of the temporal sector (311°–40°). No changes in age were observed in healthy controls.

‍Conclusion
Patients with diabetes show progressive reduction of vessels and neuroretinal rim at the optic disc, which is more intense in association with glaucoma. In the absence of glaucoma, the temporal sector of the diabetic rim was not reduced but thickened, displacing the cup nasally.

Objective
‍To describe a new method to estimate the frequency distribution of optic nerve disc area, using digital retinographic images.

‍Methods and analysis
‍We analysed 492 023 fundus images obtained with seven fundus cameras, mainly in Caucasian subjects. They were grouped by resolution and zoom. They were automatically segmented by identifying the inner edge of the Elschnig scleral ring. For this purpose, a neural network trained by deep learning previously described was used. The number of pixels contained within the segmentation and their frequency distribution were calculated. The results of each camera, using different number of images, were compared with the global results using the Kolmogorov-Smirnov test to confront frequency distributions.

‍Results
‍The frequency distribution was non-Gaussian, more limited in small sizes than in large ones. If the median is assigned a theoretical value of 1.95 mm2, the 1th, 5th, 25th, 50th, 75th, 95th and 99th percentiles would correspond to 1.29, 1.46, 1.73, 1.95, 2.20, 2.64 and 3.03 mm2 in all the dataset. The overall differences were significant for the smaller series, but for each percentile their mean value was only 0.01 mm2 and the maximum 0.10 mm2, so they can be considered similar for practical purposes in all cameras.

‍Conclusion
‍By automatically segmenting the edges of the optic nerve and observing the frequency distribution of the number of pixels it delimits, it is possible to estimate the frequency distribution of the disc area in the population as a whole and that of each individual case.

Purpose
To investigate structural and functional correlations in glaucoma patients using optic nerve head hemoglobin (ONH Hb) measurements as determined by automated colorimetric analysis of conventional retinography.

Methods
We prospectively enrolled healthy participants and glaucomatous patients with a wide range of disease stages. All participants underwent visual field (VF) testing (standard automated perimetry, SAP), color fundus imaging (mydriatic retinography), and peripapillary retinal nerve fiber layer (pRNFL) assessment through spectral-domain optical coherence tomography (SD-OCT). Software Laguna ONhE was used to estimate the amount of ONH Hb and to determine the glaucoma discriminant function (GDF) index. Scatter plots were constructed, and regression analysis was used to investigate the correlations between GDF, average pRNFL thickness, and VF mean deviation (VFMD) index values. A secondary analysis was performed to compare each parameter between three different glaucoma groups divided according to VFMD values (mild, >−6 dB; moderate, −6 to −12 dB; and advanced, <−12 dB).

Results
One hundred ninety-six eyes from 123 participants (69 with glaucoma and 54 controls) were enrolled. Overall, all parameters evaluated differed significantly between glaucomatous and control eyes (p ≤ 0.001). The comparison of each parameter according to groups of disease stages revealed significant differences between controls and each of the glaucomatous groups (p < 0.001). More pronounced changes in GDF values were observed in early disease stages. We found significant nonlinear correlations between GDF and VFMD values (R2 = 0.295, p < 0.001) and between pRNFL thickness and VFMD (R2 = 0.598, p < 0.001). A linear correlation was found between GDF and pRNFL thickness values (R2 = 0.195, p < 0.001).

Conclusion.
Our results showed significant associations between ONH Hb values and both structural and functional damage in glaucoma obtained by SD-OCT and SAP, respectively. Thee nonlinear correlation we found and the GDF behavior along different disease stages suggest that ONH Hb levels’ reduction may precede visual function changes in early glaucoma stages.

Background
Laguna-ONhE is an application for the colorimetric analysis of optic nerve images, which topographically assesses the cup and the presence of haemoglobin. Its latest version has been fully automated with five deep learning models. In this paper, perimetry in combination with Laguna-ONhE or Cirrus-OCT was evaluated.

Methods
The morphology and perfusion estimated by Laguna ONhE were compiled into a “Globin Distribution Function” (GDF). Visual field irregularity was measured with the usual pattern standard deviation (PSD) and the threshold coefficient of variation (TCV), which analyses its harmony without taking into account age-corrected values. In total, 477 normal eyes, 235 confirmed, and 98 suspected glaucoma cases were examined with Cirrus-OCT and different fundus cameras and perimeters.

Results
The best Receiver Operating Characteristic (ROC) analysis results for confirmed and suspected glaucoma were obtained withthe combination of GDF and TCV (AUC: 0.995 and 0.935, respectively. Sensitivities: 94.5% and 45.9%, respectively, for 99% specificity). The best combination of OCT and perimetry was obtained with the vertical cup/disc ratio and PSD (AUC: 0.988 and 0.847, respectively. Sensitivities: 84.7% and 18.4%, respectively, for 99% specificity).

Conclusion
Using Laguna ONhE, morphology, perfusion, and function can be mutually enhanced with the methods described for the purpose of glaucoma assessment, providing early sensitivity.

‍

Purpose
‍The color of the optic nerve's central vessels, visible over the white myelin background, may serve as a reference to evaluate the increase in lens absorption to short wavelength radiation. It would allow observing premature lens aging in diabetic patients.‍

Methods
‍Fundus images were obtained from 354 normal and 307 diabetic eyes with a Topcon TRC-NW300 fundus camera (Topcon, Japan). Image quality, laterality of the eye, nerve position and nerve and vessel segmentation were automatically assessed by using the Deep Learning training for the Laguna ONhE program which is mainly used for glaucoma (1-4). A multiple regression equation was calculated based on the RGB components of the vessels so as to deduce the biological age of normal subjects. This equation was also used for the diabetic population.

Results
‍The biological age of normal subjects was estimated with a standard error (SE) of 5.93 years. Estimated age = 27.25 + (-0.366*R) + (1.556*G) + (-1.59*B) (r= 0.911, p<0.0001). Much more aging was observed in diabetic patients SE=10.556 (r=0.614, p<0.0001), as well as a greater dispersion (Figure).

Conclusions
‍Crystalline lens aging can be estimated by observing the changes in color of the retinal central vessels as they pass through the optic disc. These data confirm previous normal results obtained with the fundus camera DEC-200 (MiiS, Taiwan)(5). At the moment we have not been able to reproduce these results, with equal precision, in all the fundus cameras. We have the hypothesis that some more modern or more sophisticated fundus cameras may have an intense filtering of ultraviolet radiation.

This is a 2021 ARVO Annual Meeting abstract.

Purpose
Laguna ONhE automatically analyzes hemoglobin distribution in optic disc retinographies (1-9). Its main index, called GDF (Globin Distribution Factor) involves a classifier based on Deep Learning that tends to produce extreme values: (1=normal, 0=glaucoma). The consequence is a good detection performance, outweighed by certain variability in the limit range between normality and glaucoma. Therefore, its influence has been reduced in a new index, called Globin Individual Pointer (GIP). It may be useful in the follow-up of cases.

Methods
‍Two retinographies of 78 normal eyes and 59 confirmed or suspected glaucomas were obtained using a simple manual fundus camera (DEC-200, MiiS, Taiwan). The reproducibility and diagnostic capability of both indices were compared.

Results
Analyzing the average of both GDF series, a ROC area of 0.937 (CI=0.882-0.971) and a sensitivity of 67.8%% for 99% specificity were obtained. Its intra-class correlation coefficient was 0.970 (CI 0.958-0.978). GIP achieved a smaller ROC area (0.902, CI=839-0.946, p<0.01), sensitivity of 50.85% for 99% specificity, and a higher intra-class correlation coefficient (0.990, CI=0.985-0.993, p<0.0001).

Conclusions:
Both indices are complementary: GDF useful for diagnostic classification, especially considering that not all glaucomas were confirmed cases but only with signs of suspicion, and GIP for individual progression assessment.

This is a 2021 ARVO Annual Meeting abstract.

Purpose
‍Colorimetric analysis of optic nerve images for assessing their hemoglobin distribution (Laguna ONhE) (1-4) is tested in combination with perimetry.

Methods
‍Deep learning training was used to identify nerve edges, laterality of the eye, image quality, vessel segmentation and classification (normal vs glaucoma). Data was compiled into a "Globin Distribution Function" (GDF), which was also associated with visual field irregularity indices: Pattern Standard Deviation (PSD), square root of loss variance (sLV), and threshold coefficient of variation (TCV) (5).
477 normal eyes and 333 confirmed and suspected glaucoma eyes, which were examined with three fundus cameras, two perimeters and two visual field strategies. The results were compared with Cirrus OCT.‍

Results
‍GDF sensitivity identifying glaucoma was 75.7% for a specificity of 99.0%. The most sensitive OCT index was the Rim Area (sensitivity 67.0%, P=0.0131). Its association with visual field irregularity produced the following AUC's: GDF&PSD-sLV = 0.963-0.986 and GDF&TCV = 0.965-0.987, while Rim Area&PSD = 0.927-0.960, Vertical Cup/Disc&PSD = 0.929-0.961 and RNFLT&PSD = 0.894-0.933 (P<0.0001 in all cases). For 99% specificity, GDF&TCV achieved 80.8% sensitivity and RNFLT&PSD 72.4%.
In cases where the morphological or functional indices had an unusual level in regard to 95% of normal subjects, the GDF&TCV achieved AUC's of 0.99-1.00 and sensitivities of 87.3-96.0% for 99% specificity.‍

Conclusions
‍Laguna ONhE associated to perimetry offers relevant diagnostic results in glaucoma, although new studies might be necessary to consolidate such results.

This is a 2021 ARVO Annual Meeting abstract.

‍

Purpose
‍To assess a mass screening in general population, mainly European, using the Laguna ONhE system to detect glaucoma.‍

Methods
‍285,320 retinographies obtained in numerous locations with various fundus cameras were analyzed fully automatically and unsupervised via Internet, between January 2019 and December 2020. Deep Learning was used for identifying the eye (left or right), segmenting the disc and vessels, detecting image quality and generating a glaucoma classifier. A multi-factor index called Globin Distribution Factor (GDF) described in previous publications (1-9) was used. The amount of hemoglobin, the cup/disc ratios and the areas of the rim sectors were also estimated.

Results
‍6.1% of cases were discarded because the system detected poor image quality, or absent or sectioned optic disc. 4.9% of the cases that could be analysed showed GDF below -15 (percentile 1% of the normal population) and 87.6% above 0 (percentile 5% of the normal population) (Figure). Cases with low GDF showed abnormal data in areas and indices associated with glaucoma (Table).

Conclusions
‍Although the data collection model did not allow individual diagnostic confirmation, GDF scores were consistent with the expected prevalence of glaucoma in the general population (10). Data and rates among such cases differ from normality as might be expected in glaucoma.

This is a 2021 ARVO Annual Meeting abstract.

‍

Purpose
The aim of this study was to assess the optic nerve head (ONH) and macular vessel density with optical coherence tomography angiography (OCT-A) and the ONH haemoglobin (ONH Hb) amount with Laguna ONhE program in open-angle glaucoma (OAG) patients.

Methods
In this prospective observational cross-sectional study, 67 OAG patients and 41 healthy age-sex frequency matched subjects were examined with OCT-A and retinal photos. The circumpapillary (wcpVD), optic nerve head (iVD) and macular (wmVD) capillary vessel density of OCT-A and ONH colorimetric assessment to determine the ONH Hb amount using the Laguna ONhE program were evaluated.

Results
Significant differences between normal subjects and glaucoma patients in the wcpVD (22.18±3.42 vs 16.03±2.89%; p<0.001), iVD (18.31±5.56 vs 12.52±4.67%; p<0.001), wmVD (15.60±2.34 vs 13.34±2.32%; p<0.001) and amount of ONH Hb (71.10±1.67 vs 68.86±2.46%; p<0.001) and in the papillary cup (68.14±5.25 vs 64.77±5.08%; p=0.001) were found. The Laguna ONhE glaucoma discriminant function (GDF) index had a negative value in the OAG patients and normal values in healthy subjects (−18.76±13.31 vs 7.98±14.09; p<0.001). The area under the receiver operating characteristic (ROC) curve (AUROC) for discriminating between healthy and glaucomatous eyes was highest for wcpVD (0.93; 95% CI 0.86 to 0.97, p<0.0001), followed by GDF (0.92; 95% CI 0.86 to 0.97, p<0.0001), iVD (0.79; 95% CI 0.70 to 0.86; p<0.0001) and ONH Hb (0.78; 95% CI 0.69 to 0.85, p<0.0001). Pair wise comparisons showed that the AUROC of wcpVD (0.93) was not significantly different than GDF (0.92) (p=0.855).

‍Conclusion
‍Laguna ONhE program and OCT-A have similar diagnostic validity in open-angle glaucoma patients.

The use of a Gold Standard may be an option acceptable when its precision is well known and not it is expected that the evaluated procedure can be more exactly. But when the Gold Standard is imperfect or worse than the one judged, the conclusions of the evaluation are generally inaccurate. its utilization can not only underestimate the method evaluated, but offer incorrect results, especially when it has been used for selection of the samples.

Purpose
To determine the limits of the optic nerve head (ONH) in color fundus images using Deep learning (DL) for the estimation of its hemoglobin topographic distribution. Also, to evaluate the usefulness of that distribution in glaucoma diagnosis singly or in association with perimetry.‍

Methods
‍A DL method was trained using 40000 fundus images and applied to 89 normal eyes and 77 confirmed or suspect glaucomas. DL and manual segmentation were compared. The eyes were also examined once with TOP perimetry (Octopus 300) and Spectralis-OCT and twice with Cirrus-OCT and Laguna ONhE, a program which estimates hemoglobin from color photographs, using improved criteria from previous studies.

Results
The Sorensen-Dice similarity index between manual and automatic segmentations was 0.993. Intraclass correlation coefficients were similar when comparing the results of the Laguna ONhE indices using the manual and automatic segmentations (confidence intervals: 0.933-0.978). For specificity close to 95%, the GDF index, a factor that measures the distribution of hemoglobin at the nerve, obtained sensitivities between 70.1 and 74.0% (manual vs. automatic segmentations). The retinal nerve fiber layer thickness (RNFLT) of both OCTs provided sensitivities between 67.1 and 68.8% and the BMO-RMW of Spectralis-OCT 69.7%. Associating several normalized indices, e.g. a new visual field harmony index (Threshold Coefficient of Variation, TCV) and GDF, provided 85.7% sensitivity for 97.8% specificity. GDF correlation with Spectralis-OCT BMO-RMW index was similar to that obtained between this index and the RNFLT of the same instrument. For 95% specificity, the diagnostic concordance (kappa value) between both Spectralis-OCT indices was 0.694 and between its BMO-RMW and Laguna ONhE GDF 0.804-0.828. ‍

Conclusion
‍A fully automatic delimitation of the optic nerve head allows the correct, reproducible and efficient use of the Laguna ONhE method, and its effectiveness is greatly increased if associated with a perimetric harmony index.

Purpose
‍To evaluate the effectiveness of quantifying color changes in the optic nerve head in retinal photographs of patients with childhood glaucoma.‍

Methods
‍In this observational study, three photographs of the optic nerve head were obtained in 28 patients with childhood glaucoma and 28 age- and sex-matched healthy participants (the childhood glaucoma and control groups, respectively). The Laguna Optic Nerve Head Hemoglobin (ONhE) software (Insoft SL, Tenerife, Spain) was used to determine hemoglobin levels in the optic nerve head. The following parameters were quantified: the hemoglobin levels in the optic nerve head across the whole disc, in 24 sectors (the optic nerve head divided by two concentric rings and eight 45-degree radial sectors), and in the vertical disc diameter (sectors 8 and 20), and the estimated cup–disc ratio and Glaucoma Discriminant Function, which combines the slope of the hemoglobin amount with the mean vertical disc diameter.‍

Results
‍Patient ages ranged from 9 to 14 years (median: 11 years) in the childhood glaucoma group, and 7 to 13 years (median: 9 years) in the control group (P < .061). Eyes in the childhood glaucoma group showed a significantly higher cup–disc ratio compared to eyes in the control group (0.6 ± 0.2 vs 0.5 ± 0.1, respectively; P < .0001). In the childhood glaucoma group, the Glaucoma Discriminant Function was found to be significantly lower than in the control group (−6.5 ± 31.1 vs 9.4 ± 17.1, respectively; P< .0001). There were no significant differences in the hemoglobin levels in the optic nerve head across the whole disc between eyes in the childhood glaucoma and control groups (58.2% ± 10.9% vs 58.5% ± 6.7%, respectively; P = .847). The Laguna ONhE software showed good reproducibility in measuring percentages of hemoglobin levels in both groups.‍

Conclusions
‍The Laguna ONhE software is useful for patients with childhood glaucoma. However, hemoglobin levels in the optic nerve head across the whole disc may have normal values. This method had good reliability and is easy to implement in routine clinical practice.

Purpose
The Laguna ONhE program divides conventional color images of the optic nerve head (ONH) into 24 sectors using two ellipses, approximately parallel to its edge, and four diametrical lines. It notes the differences between its red and green components and compensates the diversities of spectral composition of the illumination light, the absorption of the lens and the spectral response of the detector used by means a relative measure: the values of the tissue are divided by those obtained in the central vessels.

Previous experience in 700 normal and 494 glaucoma images obtained with Nidek, Kowa and Topcon fundus cameras was used to optimize the Laguna ONhE index “Glaucoma Discriminant Function, GDF“.

Methods
96 healthy subjects and 82 confirmed and suspect glaucoma were examined twice with the Laguna ONhE method (INSOFT, Spain), using images obtained with the Horus DEC- 200 portable fundus camera (MiiS, Taiwan), and once with the Spectralis OCT (Heidelberg, Germany). The images were divided into two groups of better and worst contrast, comparing vessels Vs tissue, using the red and green channels in the optic nerve image. Differences between outcomes were analyzed with the MedCalc 17.9.7 program. 

Results
Both series respectively had contrasts of 1.58±0.33 and 1.95±0.60 (p<0.0001). The Pearson correlation coefficient between GDF and BMO-MRW was 0.827-0.831 in the two groups of images (p<0.0001, Fig 1), between GDF and RNFLT was 0.763-0.766 (p<0.0001) and between BMO-MRW and RNFLT was 0.848 (p<0.0001, Fig 2). Intra-class correlation coefficient between the GDF values of the two exams was 0.957. Using ROC analysis, we calculated the confidence intervals (5%-95%) of the area under the curve, the specificity closest to 95%, and the corresponding sensitivity.

Conclusion
Using a simple manual fundus camera to study the distribution of hemoglobin in the optic nerve achieves a diagnostic capacity of glaucoma almost equivalent, or minimally different, to an OCT, even using images of sub-optimal quality.

Purpose
To compare the diagnostic accuracy and reproducibility of the Laguna ONhE program (with automatic segmentation of the optic nerve head cupping and neuro-retinal rim using new algorithms), with morphological (OCT) and functional (visual field) information.

Methods
96 healthy subjects and 82 glaucomas were examined twice with photographic images obtained with a fundus camera Horus Scope DEC-200 (MiiS) and analyzed with the modified Laguna ONhE (Insoft) software, twice with Cirrus OCT (Zeiss), once with Spectralis OCT (Heidelberg) and once with Octopus 300 TOP-32 (Haag-Streit). Statistics used: Receiver operator characteristic (ROC) curve analysis, Pearson correlations, intra-class correlation coefficients (ICC) and respective confidence intervals (CI), and kappa concordance index.

Results
Laguna ONhE glaucoma discriminant function (GDF) was among the indices of greatest area under the ROC curve (AUROC) (Confidence interval CI=0.87-0.95 in the first examination and 0.86-0.94 in the second), similar to that obtained with Bruch’s membrane opening-minimum rim width (BMO-MRW) of Spectralis (CI=0.91-0.97). Diagnostic concordance between the two was good (kappa =0.639) and similar to that observed, for example, between the set of Spectralis and Cirrus indices (kappa=0.592). Based on hemoglobin information, Laguna ONhE estimation of rim and cup shape and size showed AUROC equivalent to those of Cirrus (Cirrus vertical C/D ratio CI=0.86-0.94, Estimated Laguna ONhE vertical C/D ratio CI=0.83-0.92). For a specificity of 95%, the cut of diagnostic value of the (estimated) Laguna ONhE vertical C/D ratio was 0.59-0.62 and for the (average) Cirrus 0.68-0.75. The reproducibility of Laguna ONhE indices measured with ICC was: GDF (CI=0.88-0.93) and estimated Hb C/D vertical ratio (CI=0.90-0.94). This proved similar to the C/D vertical ratio Cirrus reproducibility (CI=0.95-0.97). Cirrus RNFT reproducibility was slightly better (CI=0.98-0.99). Perimetric indices showed slightly lower diagnostic capacity, but this was not statistically significant with most of others: Mean defect AUROC (CI=0.82-0.91) and square root of loss variance AUROC (CI=0.79-0.89).

Conclusions
Laguna ONhE showed high diagnostic capacity and reproducibility, equivalent to other methods such as OCT. This procedure provides information different from functional or morphological data, related with optic nerve head perfusion. Morphological estimation using Laguna ONhE showed a similar range of diagnostic capacity in the sample analyzed to that measured by OCT.

Purpose
To examine correlations between cup-to-disc (C/D) ratios determined by the new Laguna ONhE (optic nerve hemoglobin) color imaging procedure, spectral domain optical coherence tomography (OCT), confocal scanning laser tomography using Heidelberg retina tomography (HRT), and examining retinal images.

Methods
C/D ratio measurements were made on 154 eyes of 154 subjects (52 healthy controls, 36 with ocular hypertension and 66 with primary open-angle glaucoma) using the Laguna ONhE, HRT-III (Heidelberg Engineering) and OCT Spectralis (Heidelberg Engineering) instruments and photographs of the optic disc were examined by a blinded observer (experienced glaucoma specialist).

Results
Global intraclass correlation coefficients (ICC) were: 0.379 (95% CI: 0.233–0.508) for Laguna ONhE-HRT, 0.621 (95% CI: 0.513–0.709) for Laguna ONhE-OCT, and 0.558 (95% CI: 0.398–0.678) for the Laguna ONhE-observer, indicating significant agreement in each case (p < 0.001). The highest ICC was recorded for OCT-observer (0.715; 95% CI: 0.605–0.795).

Conclusions
C/D ratios measured using the Laguna ONhE procedure correlated well with OCT measurements and retinography measurements made by an experienced observer. Best correlation was observed for OCT versus observer measurements. Agreement was good between the Laguna ONhE, OCT and observer measurements, and was somewhat lower between HRT and the remaining procedures.

Description of the method Laguna ONhE (used by the RetinaLyze Glaucoma algorithm) that measures the amount of hemoglobin at the optic nerve head from color fundus pictures. It shows the basis of the method and its application to glaucoma and other pathologies that affect the optic nerve.

Purpose
To observe the relationship between topographic hemoglobin levels in the optic nerve head (ONH), the rim thickness (BMO-MRW), and retinal nerve fiber layer (RNFL) thickness.

Methods
96 normal eyes and 82 glaucomas were examined using TOP strategy (Octopus 300 perimeter), SPECTRALIS OCT, and Laguna ONhE program which estimates hemoglobin from conventional color photographs (Horus Scope DEC 200 fundus camera). 

Results
The correlation between Laguna ONhE glaucoma discriminant function (GDF) and SPECTRALIS BMO-MRW was R = 0.81 (P < 0.0001), similar to that between the BMO-MRW and BMO-RNFL thicknesses (R = 0.85, P < 0.0001) (P = 0.227 between both R values). GDF correlated well with RNFL thicknesses in the 360 degrees around the nerve, similar to mean perimetric sensitivity (MS) and BMO-MRW. The amount of hemoglobin in the nasal and temporal sectors showed low correlation with superior and inferior RNFL thicknesses. The superotemporal and inferotemporal sectors located on the vertical diameter of the disk showed good intercorrelation but without a clear RNFL topographic relationship. 

Conclusion
GDF showed high correlation with RNFL thickness. Except in the nasal and temporal sectors, ONH hemoglobin correlated well with RNFL thickness.

Purpose
To evaluate intraobserver, interobserver, within-session and between-session reproducibility of the measurement of optic nerve head (ONH) hemoglobin levels by color analysis using Laguna ONhE [optic nerve hemoglobin (ONH Hb)] program.

Methods
This was an observational prospective study of 29 eyes (11 glaucomatous; 18 healthy eyes). Two examiners obtained 2 retinal photographs (Canon non-mydriatic retinal camera CD-DGi, Canon Inc.,Tokyo, Japan) in 2 testing sessions 3 weeks apart and analyzed the images using Laguna ONhE. The following parameters were quantified: ONH hemoglobin amounts across the whole disc (ONH Hb) and in the vertical disc diameter (8&20 Hb), cup-disc ratio (C/D), and the Glaucoma Discriminant Function (GDF). Agreement was illustrated using the Bland-Altman plots and reproducibility was assessed comparing the intraclass correlation coefficients (ICC).

Results
In session 1, examiner 1 found mean levels of ONH hemoglobin of 67.94±8.70% in healthy eyes and of 57.90±5.36% in glaucomatous eyes. Corresponding values for examiner 2 were 68.27±8.52% and 57.83±4.88%, respectively. ONH Hb and 8&20 Hb measurements were lower in glaucomatous eyes (P=0.002 and P=0.001 respectively). GDF was also more pathologic in glaucomatous group. C/D ratio estimation was greater in the glaucoma group (P=0.003). ONH Hb and 8&20 Hb showed the highest ICCs (all above 0.9). Variability was greater for GDF (ICC>0.8) and C/D ratio estimation (ICC>0.71).

Conclusions
Measurement of ONH Hb levels using the Laguna ONhE program shows high reproducibility both in glaucomatous and nonglaucomatous ONHs.

Purpose
To calculate the relative amount of hemoglobin (Hb) in sectors of the optic nerve head (ONH) from stereoscopic color fundus images using the Laguna ONhE method and compare the results with the visual field evaluation and optical coherence tomography (OCT).

Methods
Healthy eyes (n = 87) and glaucoma eyes (n = 71) underwent reliable Oculus Spark perimetry and Cirrus OCT. Optical nerve head color images were acquired with a nonmydriatic stereoscopic Wx Kowa fundus camera. Laguna ONhE program was applied to these images to calculate the relative Hb amount in the cup and six sectors of the rim. Receiver operating characteristic (ROC) analysis and correlations between parameters were calculated.

Results
We did not observe any variations in the relative amount of Hb in relation to age in healthy subjects (R(2) = 0.033, P > 0.05). Maximum ROC area confidence intervals were observed for a combination between perimetric indices and the Laguna ONhE Glaucoma discriminant function (0.970-0.899) followed by rim area (0.960-0.883), and mean deviation (MD; 0.944-0.857). In glaucoma cases, relative Hb amount presented significant reduction in all rim sectors, especially 231° to 270° and 81° to 120° (P < 0.001), except in the temporal 311° to 40° (P = 0.11). Perimetry mean sensitivity by sectors was better correlated with respective Hb levels than with rim areas or the corresponding nerve fiber thickness, especially the superior and inferior sectors (P < 0.05).

Conclusions
Visual field sensitivity was better correlated with Hb levels than with rim sector areas or the corresponding nerve fiber thickness. In many cases the remaining rim show low perfusion, especially in the superior and inferior sectors.

Purpose
We evaluated and compared the ability of a new method for measuring hemoglobin (Hb) levels at the optic nerve head (ONH) to that of visual field evaluation, scanning laser ophthalmoscopy (HRT), scanning laser polarimetry (GDx), and optical coherence tomography (OCT) for diagnosing glaucoma.

Methods
Healthy eyes (n=102) and glaucomatous eyes (n=101) underwent reliable Oculus Spark perimetry, and imaging with the HRT, GDx, and Cirrus OCT. In addition, ONH color images were acquired with a non-mydriatic fundus camera. The Laguna ONhE program then was used to calculate the Hb amount in each of 24 sectors of the ONH. Sensitivities at 95% fixed specificity, diagnostic agreement, and linear correlations between parameters with the best diagnostic ability were calculated.

Results
The glaucoma discriminant function (GDF) of the Laguna program, evaluating Hb in the vertical intermediate sectors and center/periphery Hb amount slope, yielded an 89.1% sensitivity and 95.1% specificity, which was superior or similar to the other tests. The best GDF diagnostic agreement was for the OCT-vertical cup-to-disc (C/D) ratio (kappa = 0.772) and the final phase Spark pattern SD (kappa = 0.672).

Hb levels correlated strongly with the Spark mean sensitivity (first phase 0.70, final phase 0.71). Hb also correlated well with the Reinhard OW Burk discriminant function of the HRT (0.56), nerve fiber indicator of GDx (0.64), and vertical C/D ratio of OCT (0.71).

Purpose
The computer program Laguna ONhE determines optic nerve head hemoglobin (ONH Hb) on retinal photographs based on detecting colour differences. The software provides two diagnostic indices for glaucoma: estimated vertical cup-disc-ratio (C/D) and glaucoma discriminant function GDF). This study examines the amount of ONH Hb in patients with chilhood glaucoma using this new noninvasive technique.

Methods
In this prospective, observational case series study, measurements were made on retinal photographs (Canon CR-Dgi non mydriatic fundus camera) using the Laguna ONhE program in 108 eyes of 63 healthy subjects and 88 eyes of 56 patients with childhood glaucoma. The variables recorded were: C/D, GDF, and ONH Hb across the whole disc, and across the vertical disc diameter (sectors 8 and 20). ONH Hb differences between groups were determined by independent t Student test. U Mann Whitney test was used in non parametric parameters. Pearson’s correlation and lineal regression model were assessed in both childhood glaucoma and control study group.

Results
The median age in childhood glaucoma was 14 years old (P25-P75 10;25) and 9 years old (P25-P75 7;13) in healthy subjects (p 0.000).ONH Hb across vertical disc diameter was higher in controls (64.62 ± 7.52%) than in glaucomatous eyes (59.96 ± 13.07%), p0.002. C/D was higher in glaucomatous eyes (0.61 ± 0.17) than in control eyes (0.52 ± 0.98), p0.000. GDF was lower in glaucoma (-4 P25-P75 -30;20) than in the control group (6 P25-P75 -2;19), p0.001. There were not significant differences in ONH Hb across the whole disc between childhood glaucoma eyes (57.75± 11.24%) and healthy eyes (58.14 ± 7.16%) p 0.770. C/D on glaucoma patients was correlated with ONH Hb across the whole disc (- 0.745, p 0.000), ONH Hb across the vertical disc diameter (- 0.885, p 0.000) and GDF index (- 0.981, p 0.000). Multiple linear regression analysis revealed an effect of age (slope –0.153%/year (95%CI -0.61; -0.02, p = 0.023) on ONH Hb.

Conclusion
Our findings indicate the capacity of this device in childhood glaucoma diagnosis, however ONH Hb across the whole disc may have normal values. Our results will help to make future adjustments to the software of this new program.

Purpose
To calculate the amount of hemoglobin (Hb) in the optic nerve head (ONH), using superimposed color fundus images with disc, rim and cup boundaries obtained by OCT-Cirrus.

Methods
We examined 100 healthy and 121 glaucomatous eyes using Oculus–Spark perimetry, Cirrus-OCT and Visucam (Zeiss) ONH color images. The Laguna ONhE program was then used to calculate the amount of Hb in the cup and six sectors of the rim. Receiver operating characteristic (ROC) analysis was performed and correlations between parameters were calculated.

Results
In suspected and confirmed glaucoma, Hb was significantly lower than controls in all rim sectors, especially the inferior and superonasal (p < 0.0001). Mean deviation (MD) of visual field regions showed greater correlation with the amount of Hb in the superior and inferior sectors of the rim than with rim area (p = 0.02) or nerve fiber layer thickness (p < 0.0001). On ROC analysis, the best diagnostic indicators were OCT rim area, vertical cup/disc ratio (C/D) and Glaucoma Discriminant Function (GDF) of Laguna ONhE, without significant differences.

Conclusions
The amount of Hb in the ONH seems to have an important relationship with glaucomatous visual field sensitivity. The remaining rim has insufficient perfusion in many cases of glaucoma.

Purpose
The computer program laguna onhe determines optic nerve head haemoglobin (ONH Hb) on retinal photographs based on detecting colour differences. This study compares the diagnostic capacity of Laguna ONhE with that of spectral domain optical coherence tomography (OCT) and confocal tomography (HRT III).

Methods
In a prospective, observational, cross-sectional study, glaucomatous (n = 66) and healthy (n = 52) eyes were examined by Spectralis OCT, HRT III and Laguna ONhE. The following Laguna ONhE variables were determined: ONH Hb across the vertical disc diameter (8&20 Hb), estimated cup-disc ratio (C/D) and the glaucoma discriminant function (GDF), which combines the slope of Hb amount with the mean in 8&20 Hb. The three diagnostic methods were compared by calculating areas under ROC curves (AUCs). Correlations between variables were assessed through Spearman's rho coefficient.

Results
Areas under ROC curves (AUCs) were 0.785 (95% CI: 0.700-0.863) for GDF, 0.807 (95% CI: 0.730-0.883) for OCT retinal nerve fibre layer thickness (OCT-RNFL) and 0.714 (95% CI: 0.618-0.810) and 0.721 (95% CI: 0.628-0.815) for the HRT III variable GPS (glaucoma probability score) and vertical C/D ratio, respectively. Glaucoma discriminant function (GDF) was correlated with OCT-RNFL (0.587, p 0.001; 0.507, p 0.045; and -0.119, p 0.713 for mild, moderate and advanced glaucoma, respectively), mostly so with inferior OCT-RNFL (0.622; p < 0.001). Glaucoma discriminant function (GDF)-HRT III correlations were lower (rim area 0.471, p < 0.0001; rim/disc area 0.426, p < 0.0001; vertical C/D -0.413, p < 0.0001; GPS -0.408, p < 0.0001; rim volume 0.341, p < 0.0001).

Conclusion
Similar diagnostic power was observed for Laguna ONhE, Spectralis OCT and HRT III.

Purpose
To investigate the use of automated image analysis for the detection of diabetic retinopathy (DR) in fundus photographs captured with and without pharmacological pupil dilation using a digital non‐mydriatic camera.

Methods
A total of 83 patients (165 eyes) with type 1 or type 2 diabetes, representing the full spectrum of DR, were photographed with and without pharmacological pupil dilation using a digital non‐mydriatic camera. Two sets of five overlapping, non‐stereoscopic, 45‐degree field images of each eye were obtained. All images were graded in a masked fashion by two readers according to ETDRS standards and disagreements were settled by an independent adjudicator. Automated detection of red lesions as well as image quality control was made: detection of a single red lesion or insufficient image quality was categorized as possible DR.

Results
At patient level, the automated red lesion detection and image quality control combined demonstrated a sensitivity of 89.9% and specificity of 85.7% in detecting DR when used on images captured without pupil dilation, and a sensitivity of 97.0% and specificity of 75.0% when used on images captured with pupil dilation. For moderate non‐proliferative or more severe DR the sensitivity was 100% for images captured both with and without pupil dilation.

Conclusion
Our results demonstrate that the described automated image analysis system, which detects the presence or absence of DR, can be used as a first‐step screening tool in DR screening with considerable effectiveness.

Purpose
This study evaluated the operating characteristics of a reading software (Retinalyze® System, Retinalyze A/S, Hørsholm, Denmark) for automated prescreening of digital fundus images for diabetic retinopathy.

Methods
Digital fundus images of patients with diabetes were retrospectively selected from the Bro Taf diabetic retinopathy screening program in Wales, UK in the period of 2002–2004, which has been superseded by the Diabetic Retinopathy Screening Service for Wales. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized images using the Bro Taf reading protocol. Automated grading was applied using automated red or bright lesion detection at varying detection sensitivities and adjusting for image quality. Operating characteristics included sensitivity, specificity, positive predictive values, and negative predictive values (PPV and NPV, respectively).

Results
Automated analysis of four hundred fundus photographs of 192 eyes from 96 patients with diabetes was performed. The automated red lesion detection had a sensitivity of 82%, specificity of 75%, PPV of 41%, and NPV of 95%. Combined automated red and bright lesion detection yielded a sensitivity of 88%, specificity of 52%, PPV of 28%, and NPV of 95%. Performance of the combined red and bright lesion detection at elevated thresholds in images of good quality demonstrated a sensitivity of 93%, specificity of 78%, PPV of 46%, and NPV of 98%.

Conclusions
Prescreening for diabetic retinopathy by automated detection of single fundus lesions seem to be achieved with minimal false negativity and can help to decrease the burden of manual diabetic retinopathy screening.

Purpose
To evaluate the performance of an automated fundus photographic image-analysis algorithm in high-sensitivity and/or high-specificity segregation of patients with diabetes with untreated diabetic retinopathy from those without retinopathy.

Methods
This was a retrospective cross-sectional study of 260 consecutive nonphotocoagulated eyes in 137 diabetic patients attending routine photographic retinopathy screening. Mydriatic 60° fundus photography on 35-mm color transparency film was used, with a single fovea-centered field. Routine grading was based on visual examination of slide-mounted transparencies. Reference grading was performed with specific emphasis on achieving high sensitivity. Computer-assisted automated red lesion detection was performed on digitized transparencies.

Results
When applied in a screening population comprising patients with diabetes with untreated diabetic retinopathy in any eye and patients with diabetes without retinopathy, the automated lesion detection correctly identified 90.1% of patients with retinopathy and 81.3% of patients without retinopathy. A per-eye analysis for methodological purposes demonstrated that the automated lesion detection could be adapted to simulate various visual evaluation strategies. When adapted at high sensitivity, the automated system demonstrated sensitivity at 93.1% and specificity at 71.6%. When adapted at high specificity the automated system demonstrated sensitivity at 76.4% and specificity at 96.6%, closely matching routine visual grading at sensitivity 76.4% and specificity 98.3%.

Conclusions
Automated detection of untreated diabetic retinopathy in fundus photographs from a screening population of patients with diabetes can be made with adjustable priority settings, emphasizing high-sensitivity identification of diabetic retinopathy or high-specificity identification of absence of retinopathy, covering opposing extremes of visual evaluation strategies demonstrated by human observers.

Purpose
To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes.

Methods
Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed.

Results
Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648).

Conclusions
Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

Purpose
To implement an operational economic evaluation of the Retinalyze software system used for the automated detection of the diabetic eye disease, retinopathy, by means of fundus photography.

Methods
Review of the present relevant literature. Comparison and analysis of RetinaLyze-related studies.

Results
The marginal cost per screening is between DKK 13.10 and DKK 27.16 with the automated analysis instead of the visual. At e.g. annual screenings at a typical eye doctor’s practice, the costs total between DKK 60,800 and DKK 87,300, varying with the number of patients screened positive. This presupposes internalization of savings achieved through task-shifting (time release for doctors).
The total costs must be compared to an expected activity progress in the number of eye screenings caused by the task-shifting and the related incomes in the form of e.g. health insurance services. In addition to this, there are non-quantified gains in the form qualitative implications of the screenings, improved opportunities for time-series studies accompanied by image database development as well as improved opportunities for telescreening.
With conservative model assumptions, about 11.1 full-time eye doctor positions are used on examinations per year in connection with diabetic retinopathy, with the present number of diabetics and with the use of ophthalmoscopy and/or visual image analysis.
With the use of the Retinalyze software system for automated image analysis, the task-shifting will influence the number of eye doctor positions used for examinations in connection with diabetic retinopathy on the decline and causes a saving potential in the form of released time corresponding to 8.4 full-time eye doctor position per year, based on conservative conditions.
Sensitivity analysis do not change the conclusion of the analysis significantly, as the saving potential obtained as time release is still estimated to be important in a situation with eye doctor shortage, relatively long waiting lists and an expected increase in the demand for screenings.

Conclusions
With the review of the present literature, it is concluded that the detection of diabetic retinopathy by means of automated image analysis can be achieved with a system performance directly comparable 19 to the one for experienced ophthalmologists with visual image analysis. The results prove further examination of automated fundus image analysis as a screening tool. Automated image analysis instead of visual image analysis causes a task-shifting, with a shift of tasks from ophthalmologists to nurses / specially trained paramedical personnel.

Purpose
To evaluate fundus photographic image analysis combining automated detection of red lesions, bright lesions, and image quality as a means of identifying treatment-requiring diabetic retinopathy in a screening population of diabetic patients. 

Methods
This was a retrospective cross-sectional study of 106 patients from a diabetic retinopathy screening clinic referred for photocoagulation treatment in the period from January 1996 to May 2002 on the basis of mydriatic 60-degree 35-mm color transparency fundus photography. One fovea-centered fundus photograph and one centered nasal of the optic disk from each of a subject’s two eyes was selected for digitization and analyzed using a previously tested computerized red-lesion detection algorithm in combination with a new algorithm for detection of bright lesions and image quality. The algorithm was calibrated on an independent set of fundus photographs. 

Results
Automated red-lesion detection identified 104 of 106 patients requiring photocoagulation treatment, whereas bright-lesion detection identified only 91 of the 106 patients. Two patients who were not identified by either lesion detection algorithm were automatically detected as having poor image quality in one or both eyes. In the study sample, the risk of missing treatment-requiring retinopathy patients from being detected was 0.0% (estimated CI95 0.0–3.4%). 

Conclusions
The combination of automated detection of red lesions and poor image quality identified all treatment-requiring diabetic retinopathy patients in the study sample. No additional information was contributed by the automated bright-lesion detection.