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Meta-Analysis
. 2024 Oct 17;10(10):CD015522.
doi: 10.1002/14651858.CD015522.pub2.

Artificial intelligence for diagnosing exudative age-related macular degeneration

Chaerim Kang  1 , Jui-En Lo  2 , Helen Zhang  3 , Sueko M Ng  4 , John C Lin  5 , Ingrid U Scott  6 , Jayashree Kalpathy-Cramer  4 , Su-Hsun Alison Liu  4   7 , Paul B Greenberg  1   8
Affiliations
Meta-Analysis

Artificial intelligence for diagnosing exudative age-related macular degeneration

Chaerim Kang et al. Cochrane Database Syst Rev. .

Abstract

Background: Age-related macular degeneration (AMD) is a retinal disorder characterized by central retinal (macular) damage. Approximately 10% to 20% of non-exudative AMD cases progress to the exudative form, which may result in rapid deterioration of central vision. Individuals with exudative AMD (eAMD) need prompt consultation with retinal specialists to minimize the risk and extent of vision loss. Traditional methods of diagnosing ophthalmic disease rely on clinical evaluation and multiple imaging techniques, which can be resource-consuming. Tests leveraging artificial intelligence (AI) hold the promise of automatically identifying and categorizing pathological features, enabling the timely diagnosis and treatment of eAMD.

Objectives: To determine the diagnostic accuracy of artificial intelligence (AI) as a triaging tool for exudative age-related macular degeneration (eAMD).

Search methods: We searched CENTRAL, MEDLINE, Embase, three clinical trials registries, and Data Archiving and Networked Services (DANS) for gray literature. We did not restrict searches by language or publication date. The date of the last search was April 2024.

Selection criteria: Included studies compared the test performance of algorithms with that of human readers to detect eAMD on retinal images collected from people with AMD who were evaluated at eye clinics in community or academic medical centers, and who were not receiving treatment for eAMD when the images were taken. We included algorithms that were either internally or externally validated or both.

Data collection and analysis: Pairs of review authors independently extracted data and assessed study quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool with revised signaling questions. For studies that reported more than one set of performance results, we extracted only one set of diagnostic accuracy data per study based on the last development stage or the optimal algorithm as indicated by the study authors. For two-class algorithms, we collected data from the 2x2 table whenever feasible. For multi-class algorithms, we first consolidated data from all classes other than eAMD before constructing the corresponding 2x2 tables. Assuming a common positivity threshold applied by the included studies, we chose random-effects, bivariate logistic models to estimate summary sensitivity and specificity as the primary performance metrics.

Main results: We identified 36 eligible studies that reported 40 sets of algorithm performance data, encompassing over 16,000 participants and 62,000 images. We included 28 studies (78%) that reported 31 algorithms with performance data in the meta-analysis. The remaining nine studies (25%) reported eight algorithms that lacked usable performance data; we reported them in the qualitative synthesis. Study characteristics and risk of bias Most studies were conducted in Asia, followed by Europe, the USA, and collaborative efforts spanning multiple countries. Most studies identified study participants from the hospital setting, while others used retinal images from public repositories; a few studies did not specify image sources. Based on four of the 36 studies reporting demographic information, the age of the study participants ranged from 62 to 82 years. The included algorithms used various retinal image types as model input, such as optical coherence tomography (OCT) images (N = 15), fundus images (N = 6), and multi-modal imaging (N = 7). The predominant core method used was deep neural networks. All studies that reported externally validated algorithms were at high risk of bias mainly due to potential selection bias from either a two-gate design or the inappropriate exclusion of potentially eligible retinal images (or participants). Findings Only three of the 40 included algorithms were externally validated (7.5%, 3/40). The summary sensitivity and specificity were 0.94 (95% confidence interval (CI) 0.90 to 0.97) and 0.99 (95% CI 0.76 to 1.00), respectively, when compared to human graders (3 studies; 27,872 images; low-certainty evidence). The prevalence of images with eAMD ranged from 0.3% to 49%. Twenty-eight algorithms were reportedly either internally validated (20%, 8/40) or tested on a development set (50%, 20/40); the pooled sensitivity and specificity were 0.93 (95% CI 0.89 to 0.96) and 0.96 (95% CI 0.94 to 0.98), respectively, when compared to human graders (28 studies; 33,409 images; low-certainty evidence). We did not identify significant sources of heterogeneity among these 28 algorithms. Although algorithms using OCT images appeared more homogeneous and had the highest summary specificity (0.97, 95% CI 0.93 to 0.98), they were not superior to algorithms using fundus images alone (0.94, 95% CI 0.89 to 0.97) or multimodal imaging (0.96, 95% CI 0.88 to 0.99; P for meta-regression = 0.239). The median prevalence of images with eAMD was 30% (interquartile range [IQR] 22% to 39%). We did not include eight studies that described nine algorithms (one study reported two sets of algorithm results) to distinguish eAMD from normal images, images of other AMD, or other non-AMD retinal lesions in the meta-analysis. Five of these algorithms were generally based on smaller datasets (range 21 to 218 participants per study) yet with a higher prevalence of eAMD images (range 33% to 66%). Relative to human graders, the reported sensitivity in these studies ranged from 0.95 and 0.97, while the specificity ranged from 0.94 to 0.99. Similarly, using small datasets (range 46 to 106), an additional four algorithms for detecting eAMD from other retinal lesions showed high sensitivity (range 0.96 to 1.00) and specificity (range 0.77 to 1.00).

Authors' conclusions: Low- to very low-certainty evidence suggests that an algorithm-based test may correctly identify most individuals with eAMD without increasing unnecessary referrals (false positives) in either the primary or the specialty care settings. There were significant concerns for applying the review findings due to variations in the eAMD prevalence in the included studies. In addition, among the included algorithm-based tests, diagnostic accuracy estimates were at risk of bias due to study participants not reflecting real-world characteristics, inadequate model validation, and the likelihood of selective results reporting. Limited quality and quantity of externally validated algorithms highlighted the need for high-certainty evidence. This evidence will require a standardized definition for eAMD on different imaging modalities and external validation of the algorithm to assess generalizability.

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Conflict of interest statement

CK: no conflicts of interest to declare JL: no conflicts of interest to declare HZ: no conflicts of interest to declare SMN: grant from the National Eye Institute: National Institutes of Health, USA; payment to the institution. JCL: no conflicts of interest to declare IUS: no conflicts of interest to declare JKC: Siloam Vision funding by Genentech Foundation SL: grant from the National Eye Institute: National Institutes of Health, USA; payment to the institution; was Managing Editor of CEV@US but was not involved in the editorial process of this review PBG: no conflicts of interest to declare

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  • doi: 10.1002/14651858.CD015522

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References

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ChacinRuiz 2023 {published data only}
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Chen Q 2019 {published data only}
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Chetoui 2020 {published data only}
    1. Chetoui M, Akhloufi MA. Deep retinal diseases detection and explainability using OCT images. In: Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science. Campilho A, Karray F, Wang Z, editor(s). Vol. 12132. 2020:358-66.
Choudhary 2023 {published data only}
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Coleman 2014 {published data only}
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Das 2019 {published data only}
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Deak 2023 {published data only}
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De Sisternes 2017 {published data only}
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El‐Den 2023 {published data only}
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Fazekas 2022 {unpublished data only}
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Feng 2023 {published data only}
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Ganjdanesh 2022 {published data only}
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Goldbach 2023 {published data only}
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Gomariz 2022 {published data only}
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Gong 2019 {published data only}
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Goriya 2023 {published data only}
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Grewal 2020 {published data only}
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Haddad 2023 {published data only}
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Han 2023a {published data only}
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Haq 2021 {published data only}
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Hartmann 2023 {published data only}
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Hernandez 2024 {published data only}
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Hogg 2023 {published data only}
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Holland 2022 {published data only}
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Holland 2023 {published data only}
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Hossain 2023 {published data only}
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Huang 2023 {published data only}
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Interlenghi 2023 {published data only}
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Jang 2023 {published data only}
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Jin 2022 {published data only}
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Kang 2023 {published data only}
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Khalaf 2023 {published data only}
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Kim 2023 {published data only}
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Liu 2023 {published data only}
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Lopez‐Varela 2022 {published data only}
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Maurya 2024 {published data only}
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Mhmud 2023 {published data only}
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Michl 2023 {published data only}
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Mittal 2022 {published data only}
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Morano 2022 {published data only}
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Naick 2022 {published data only}
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Ogundokun 2023 {published data only}
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Opoku 2023a {published data only}
    1. Opoku M, Weyori BA, Adekoya AF, Adu K. CLAHE-CapsNet: efficient retina optical coherence tomography classification using capsule networks with contrast limited adaptive histogram equalization. PloS One 2023;18(11):e0288663. - PMC - PubMed
Opoku 2023b {published data only}
    1. Opoku M, Weyori BA, Adekoya AF, Adu K. SFFT-CapsNet: stacked fast fourier transform for retina optical coherence tomography image classification using capsule network. International Journal of Advanced Computer Science and Applications 2023;14(9):294-306.
Ortiz 2023 {published data only}
    1. Ortiz S, Goenaga J, Miguel A. Deep learning-based ocular disease classification in fundus images. In: 1st IEEE Colombian Caribbean Conference, 2023 November 22-25; Barranquilla, Colombia. 2023.
Pan 2023 {published data only}
    1. Pan Y, Liu J, Cai Y, Yang X, Zhang Z, Long H, et al. Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases. Frontiers in Physiology 2023;14:1126780. - PMC - PubMed
Parthasarathy 2022 {published data only}
    1. Parthasarathy DR, Toh JK, Savoy F. Performance of an automated, deep learning-based tool to screen for age-related macular degeneration (AMD). Investigative Ophthalmology & Visual Science 2022;63(7):3021-F0291.
Parthasarathy 2023 {published data only}
    1. Parthasarathy DR, Baskaran P, Savoy FM, Maitray A, Negiloni K, Rajendran A, et al. Performance evaluation of an automated, offline artificial intelligence system integrated on a smartphone fundus camera for age-related macular degeneration (AMD) screening: an interim analysis. Investigative Ophthalmology & Visual Science 64;8:2153.
Pawloff 2023 {published data only}
    1. Pawloff M, Gerendas BS, Deak G, Bogunovic H, Gruber A, Schmidt-Erfurth U. Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD. Eye (London, England) 2023;37(18):3793-800. - PMC - PubMed
Pedersen 2023 {published data only}
    1. Pedersen KE, Wu AK, Maatouk C, Singh RP. Long-term outcomes in eyes with early residual fluid in neovascular age-related macular degeneration. Investigative Ophthalmology & Visual Science 2023;64(8):2197.
Peng 2019 {published data only}
    1. Peng Y, Dharssi S, Chen Q, Keenan TD, Agron E, Wong WT, et al. Deepseenet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 2019;126(4):565-75. - PMC - PubMed
    1. Peng Y, Dharssi S, Chen Q, Keenan TD, Agrón E, Wong WT, et al. DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. arxiv.org/abs/1811.07492 2018. [DOI: 10.48550/arXiv.1811.07492] - DOI - PMC - PubMed
Phan 2016 {published data only}
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Philippi 2023 {published data only}
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Prasad 2018 {published data only}
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Qaddour 2023 {published data only}
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Raen 2022 {published data only}
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Rajan 2023 {published data only}
    1. Rajan R, Kumar SN. OCT DEEPNET a deep learning approach for retinal OCT image classification. In: 3rd Congress on Intelligent Systems, 2022 September 5-6; Bengaluru, India. 2023:689-701.
Ramachandra 2015 {published data only}
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Rapantzikos 2001 {published data only}
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Riazi Esfahani 2023 {published data only}
    1. Riazi Esfahani P, Reddy AJ, Nawathey N, Ghauri MS, Min M, Wagh H, et al. Deep learning classification of drusen, choroidal neovascularization, and diabetic macular edema in optical coherence tomography (OCT) images. Cureus 2023;15(7):e41615. - PMC - PubMed
Ricardi 2024 {published data only}
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Rudas 2023 {published data only}
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Russell‐Puleri 2023 {published data only}
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Saleh 2022 {published data only}
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Sasmannshausen 2023 {published data only}
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Sathyan 2023 {published data only}
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Schranz 2023 {published data only}
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Seebock 2019 {published data only}
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Shen 2023a {published data only}
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Shen 2023b {published data only}
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Shukla 2022 {published data only}
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Skevas 2022 {published data only}
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Sotoudeh‐Paima 2022 {published data only}
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Sun 2023 {published data only}
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Tanachotnarangkun 2022 {published data only}
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Tang 2024 {published data only}
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Tayal 2022 {published data only}
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Thakoor 2022a {published data only}
    1. Thakoor KA, Carter A, Song G, Wax A, Moussa O, Chen RW, et al. Enhancing portable OCT image quality via GANS for AI-based eye disease detection. In: 3rd MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the 2nd MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, Held in Conjunction with the 25th International Conference on Medical Image Computing and Comput 2022; 2022 September 22; Singapore, Singapore. 2022:155-67.
Thee 2020 {published data only}
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Trivizki 2023 {published data only}
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Udayaraju 2022 {published data only}
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Udayaraju 2023 {published data only}
    1. Udayaraju P, Jeyanthi P, Sekhar BV. A hybrid multilayered classification model with VGG-19 net for retinal diseases using optical coherence tomography images. Soft Computing 2023;27(17):12559-70.
Vaghefi 2022 {published data only}
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Vali 2023 {published data only}
    1. Vali M, Nazari B, Sadri S, Pour EK, Riazi-Esfahani H, Faghihi H, et al. CNV-Net: segmentation, classification and activity score measurement of choroidal neovascularization (CNV) using optical coherence tomography angiography (OCTA). Diagnostics 2023;13(7):31. - PMC - PubMed
Vannadil 2023 {published data only}
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Vidal 2023 {published data only}
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von der Emde 2023 {published data only}
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Wang 2023a {published data only}
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Wang 2023b {published data only}
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Wang 2023c {published data only}
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Wang 2024a {published data only}
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Wang 2024b {published data only}
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Wang L 2020 {published data only}
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Wei 2023a {published data only}
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Wei 2023b {published data only}
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Whitney 2023 {published data only}
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Williamson 2024 {published data only}
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Wilson 2021 {published data only}
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Xia 2022 {published data only}
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Xu 2021 {published data only}
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Yan 2021 {published data only}
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Yang XL 2022 {published data only}
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Yellapragada 2022 {published data only}
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Yildirim 2023 {published data only}
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Zhou 2022 {published data only}
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Zhou 2023a {published data only}
    1. Zhou Z, Niu C, Yu H, Zhao J, Wang Y, Dai C. Diagnosis of retinal diseases using the vision transformer model based on optical coherence tomography images. In: SPIE-CLP Conference on Advanced Photonics 2022, 2022 November 21-23; Virtual, Online. 2023:12601.
Zhou 2023b {published data only}
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Zhu 2023 {published data only}
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References to studies awaiting assessment

Abd 2024 {published data only}
    1. Abd El-Khalek AA, Balaha HM, Alghamdi NS, Ghazal M, Khalil AT, Abo-Elsoud ME, et al. A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images. Scientific Reports 2024;14(1):2434. [DOI: 10.1038/s41598-024-52131-2] - DOI - PMC - PubMed
Alphy 2023 {published data only}
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Araújo 2023 {published data only}
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Ayhan 2023 {published data only}
    1. Ayhan MS, Faber H, Kuhlewein L, Inhoffen W, Aliyeva G, Ziemssen F, et al. Multitask learning for activity detection in neovascular age-related macular degeneration. Translational Vision Science & Technology 2023;12(4):12. [DOI: 10.1167/tvst.12.4.12] - DOI - PMC - PubMed
Boukadida 2022 {published data only}
    1. Boukadida R, Elloumi Y, Kachouri R, Abdallah AB, Bedoui MH. Automated diagnosis of retinal neovascularization pathologies from color retinal fundus images. In: 39th Computer Graphics International Conference on Advances in Computer Graphics; September 12-16; Online. Vol. 13443 LNCS. Springer Science and Business Media Deutschland GmbH, 2022:451-62. [DOI: 10.1007/978-3-031-23473-6_35] - DOI
Brasil 2022 {published data only}
    1. Brasil BS, De Alexandria AR, De Freitas GG. Artificial Intelligence applied to the classification of retinal diseases in optical coherence tomography images. In: 5th International Conference on Vocational Education and Electrical Engineering; September 10-11; Virtual, Surabaya, Indonesia. Institute of Electrical and Electronics Engineers Inc, 2022:78-83. [DOI: 10.1109/ICVEE57061.2022.9930121] - DOI
Corradetti 2024 {published data only}
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De Fauw 2017 {published data only}
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Diao 2023 {published data only}
    1. Diao S, Su J, Yang C, Zhu W, Xiang D, Chen X, et al. Classification and segmentation of OCT images for age-related macular degeneration based on dual guidance networks. Biomedical Signal Processing and Control 2023;84:104810. [DOI: 10.1016/j.bspc.2023.104810] - DOI
Dominguez 2023 {published data only}
    1. Dominguez C, Heras J, Mata E, Pascual V, Royo D, Zapata MA. Binary and multi-class automated detection of age-related macular degeneration using convolutional- and transformer-based architectures. Computer Methods and Programs in Biomedicine 2023;229:107302. [DOI: 10.1016/j.cmpb.2022.107302] - DOI - PubMed
Dutta 2023 {published data only}
    1. Dutta P, Sathi KA, Hossain MA, Dewan MA. Conv-vit: a convolution and vision transformer-based hybrid feature extraction method for retinal disease detection. Journal of Imaging 2023;9(7):140. [DOI: 10.3390/jimaging9070140] - DOI - PMC - PubMed
E 2022 {published data only}
    1. E H, Ding J, Yuan L. SAE-wAMD: a self-attention enhanced convolution neural network for fine-grained classification of wet age-related macular degeneration using OCT images. In: 2022 International Conference on Image Processing, Computer Vision and Machine Learning; 2022 October 28-30; Online, China. Institute of Electrical and Electronics Engineers Inc., 2022:619-27. [DOI: 10.1109/ICICML57342.2022.10009714] - DOI
E 2023 {published data only}
    1. E H, He J, Hu T, Yuan L, Zhang R, Zhang S, et al. KFWC: a knowledge-driven deep learning model for fine-grained classification of wet-AMD. Computer Methods in Programs in Biomedicine 2023;229:107312. [DOI: 10.1016/j.cmpb.2022.107312] - DOI - PubMed
Elsharkawy 2024 {published data only}
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References to other published versions of this review

Kang 2023
    1. Kang C, Lin JC, Zhang H, Scott IU, Kalpathy-Cramer J, Liu SH, et al. Artificial intelligence for diagnosing exudative age-related macular degeneration. Cochrane Database of Systematic Reviews 2023, Issue 1. Art. No: CD015522. [DOI: 10.1002/14651858.CD015522] - DOI - PMC - PubMed

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