{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T12:33:43Z","timestamp":1791808423537,"version":"4.3.4"},"reference-count":20,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model\u2019s accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis.<\/jats:p>","DOI":"10.3390\/bioengineering11040300","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T10:03:59Z","timestamp":1711101839000},"page":"300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8018-3585","authenticated-orcid":false,"given":"Ahmad","family":"Alenezi","sequence":"first","affiliation":[{"name":"Radiologic Sciences Department, Kuwait University, Jabriya 31470, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6765-5634","authenticated-orcid":false,"given":"Hamad","family":"Alhamad","sequence":"additional","affiliation":[{"name":"Occupational Therapy Department, Kuwait University, Jabriya 31470, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajit","family":"Brindhaban","sequence":"additional","affiliation":[{"name":"Radiologic Sciences Department, Kuwait University, Jabriya 31470, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yashar","family":"Amizadeh","sequence":"additional","affiliation":[{"name":"Eye Surgery Department, Mehr Hospital, Tabriz 51656, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8117-4886","authenticated-orcid":false,"given":"Ata","family":"Jodeiri","sequence":"additional","affiliation":[{"name":"Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz 51656, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8258-0437","authenticated-orcid":false,"given":"Sebelan","family":"Danishvar","sequence":"additional","affiliation":[{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e188","DOI":"10.7861\/fhj.2021-0095","article-title":"Artificial intelligence in healthcare: Transforming the practice of medicine","volume":"8","author":"Bajwa","year":"2021","journal-title":"Future Healthc. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"232","DOI":"10.7150\/thno.28447","article-title":"Artificial intelligence-based decision-making for age-related macular degeneration","volume":"9","author":"Hwang","year":"2019","journal-title":"Theranostics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1016\/S0140-6736(12)60282-7","article-title":"Age-related macular degeneration","volume":"379","author":"Lim","year":"2012","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","article-title":"Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: A systematic review and meta-analysis","volume":"2","author":"Wong","year":"2014","journal-title":"Lancet Glob. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.gendis.2021.02.009","article-title":"Age-related macular degeneration: Epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy","volume":"9","author":"Deng","year":"2022","journal-title":"Genes Dis."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, T., Zhou, Q., and Zou, Y. (2022). Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020532"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1038\/s41433-021-01540-y","article-title":"Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: A systematic review and meta-analysis","volume":"36","author":"Cheung","year":"2021","journal-title":"Eye"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vyass, A., Raman, S., Surya, J., Sen, S., and Raman, R. (2023). The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010130"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.survophthal.2019.02.003","article-title":"Automated detection of age-related macular degeneration in color fundus photography: A systematic review","volume":"64","author":"Pead","year":"2019","journal-title":"Surv. Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Muntean, G.A., Marginean, A., Groza, A., Damian, I., Roman, S.A., Hapca, M.C., Muntean, M.V., and Nicoar\u0103, S.D. (2023). The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression\u2014A Systematic Review. Diagnostics, 13.","DOI":"10.3390\/diagnostics13142464"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15434","DOI":"10.1038\/s41598-020-72359-y","article-title":"Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers","volume":"10","author":"Banerjee","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"726599","DOI":"10.3389\/fgene.2021.726599","article-title":"Genetic Mechanism Revealed of Age-Related Macular Degeneration Based on Fusion of Statistics and Machine Learning Method","volume":"12","author":"Du","year":"2021","journal-title":"Front. Genet."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1038\/s41598-020-58510-9","article-title":"A transcriptome-wide association study based on 27 tissues identifies 106 genes potentially relevant for disease pathology in age-related macular degeneration","volume":"10","author":"Strunz","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Han, J., Choi, S., Park, J.I., Hwang, J.S., Han, J.M., Ko, J., Yoon, J., and Hwang, D.D.-J. (2023). Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network. J. Clin. Med., 12.","DOI":"10.3390\/jcm12031005"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, L., Wang, L., Cheng, S., and Li, Y. (2021). MHANet: A hybrid attention mechanism for retinal diseases classification. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0261285"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, S., and Quan, Z. (2020, January 12\u201315). Attention-Aware Convolutional Neural Network for Age-Related Macular Degeneration Classification. Proceedings of the 12th International Conference on Communication Software and Networks, Chongqing, China.","DOI":"10.1109\/ICCSN49894.2020.9139104"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102673","DOI":"10.1016\/j.media.2022.102673","article-title":"Deep semi-supervised multiple instance learning with self-correction for DME classification from OCT images","volume":"83","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1007\/s11517-022-02627-8","article-title":"Self-supervised patient-specific features learning for OCT image classification","volume":"60","author":"Fang","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Santos, A.M., Paiva, A.C., Santos, A.P.M., Mpinda, S.A.T., Gomes, D.L., Silva, A.C., Braz, G., de Almeida, J.D.S., and Gattass, M. (2018). Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine. Biomed. Eng. Online, 17.","DOI":"10.1186\/s12938-018-0592-3"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/4\/300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:18:06Z","timestamp":1760105886000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/4\/300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,22]]},"references-count":20,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["bioengineering11040300"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11040300","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,22]]}}}