- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs
摘要： In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. Design: DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0e5) using bilateral CFP. Participants: DeepSeeNet was trained on 58 402 and tested on 900 images from the longitudinal follow-up of 4549 participants from AREDS. Gold standard labels were obtained using reading center grades. Methods: DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. Main Outcome Measures: Overall accuracy, specificity, sensitivity, Cohen’s kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. Results: DeepSeeNet performed better on patient-based classification (accuracy ? 0.671; kappa ? 0.558) than retinal specialists (accuracy ? 0.599; kappa ? 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). Conclusions: By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/DeepSeeNet.
关键词： deep learning,age-related macular degeneration,automated classification,AREDS Simplified Severity Scale,color fundus photographs
更新于2025-09-23 15:22:29
-
Automatic Computer-Aided Diagnosis of Retinal Nerve Fiber Layer Defects Using Fundus Photographs in Optic Neuropathy
摘要： PURPOSE. To evaluate the validity of an automatic computer-aided diagnosis (CAD) system for detection of retinal nerve fiber layer (RNFL) defects on fundus photographs of glaucomatous and nonglaucomatous optic neuropathy. METHODS. We have proposed an automatic detection method for RNFL defects on fundus photographs in various cases of glaucomatous and nonglaucomatous optic neuropathy. In order to detect the vertical dark bands as candidate RNFL defects, the nonuniform illumination of the fundus image was corrected, the blood vessels were removed, and the images were converted to polar coordinates with the center of the optic disc. False positives (FPs) were reduced by using knowledge-based rules. The sensitivity and FP rates for all images were calculated. RESULTS. We tested 98 fundus photographs with 140 RNFL defects and 100 fundus photographs of healthy normal subjects. The proposed method achieved a sensitivity of 90% and a 0.67 FP rate per image and worked well with RNFL defects with variable depths and widths, with uniformly high detection rates regardless of the angular widths of the RNFL defects. The average detection accuracy was approximately 0.94. The overall diagnostic accuracy of the proposed algorithm for detecting RNFL defects among 98 patients and 100 healthy individuals was 86% sensitivity and 75% specificity. CONCLUSIONS. The proposed CAD system successfully detected RNFL defects in optic neuropathies. Thus, the proposed algorithm is useful for the detection of RNFL defects.
关键词： computer-aided diagnosis,glaucoma,optic neuropathy,fundus photographs,retinal nerve fiber layer
更新于2025-09-19 17:15:36
-
Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs
摘要： BACKGROUND Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities.
关键词： optic-disk abnormalities,fundus photographs,deep-learning system,artificial intelligence,papilledema
更新于2025-09-19 17:13:59