{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:24:48Z","timestamp":1772555088446,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004366","name":"Tabriz University of Medical Sciences","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004366","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Age-related Macular Degeneration (AMD) is a leading cause of irreversible vision loss, particularly in the elderly. Optical Coherence Tomography (OCT), a noninvasive imaging modality, is widely used for retinal disease detection. However, the limited availability of labeled OCT datasets poses a significant challenge, making semi-supervised learning a promising approach. This study introduces a novel Iterative Teacher-Student (ITS) framework, which refines pseudo-labeling strategies to improve AMD detection accuracy, particularly in low-data scenarios. Methods: Initially, an optimal supervised model based on EfficientNet was developed to classify AMD using a dataset from Noor Eye Hospital, consisting of 16,822 OCT images. The dataset size was then progressively reduced to 70%, 50%, 20%, and 5% to evaluate model performance under data scarcity. Unlike conventional semi-supervised learning approaches, our ITS framework iteratively refines pseudo-labels, ensuring more reliable knowledge transfer from teacher to student models. Results: The optimized supervised model achieved 87.14% accuracy in AMD classification. As dataset size decreased to 20% and 5%, accuracy declined to 77.05% and 54.78%, respectively. Implementing the ITS framework improved accuracy to 88.56% at 20% and 64.15% at 5%, outperforming traditional semi-supervised methods. Conclusions: This study highlights the potential of semi-supervised learning, particularly our iterative teacher-student approach, to enhance AMD detection when labeled OCT data are scarce. The proposed framework introduces a novel iterative refinement strategy, which can serve as a foundation for future research in retinal disease diagnosis with limited labeled datasets.<\/jats:p>","DOI":"10.3390\/diagnostics16050656","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T08:50:27Z","timestamp":1772009427000},"page":"656","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Investigating the Impact of Semi-Supervised Learning Methods to Improve the Quality of Diagnosis of Retinal Diseases from OCT Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Armin","family":"Alizadeh","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz 51656, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8018-3585","authenticated-orcid":false,"given":"Ahmad","family":"Alenezi","sequence":"additional","affiliation":[{"name":"Radiologic Sciences Department, Kuwait University, Kuwait City 31470, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nastaran","family":"Khakestari","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz 51656, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yashar","family":"Amizadeh","sequence":"additional","affiliation":[{"name":"Eye Surgery Department, Mehr Hospital, Tabriz 51656, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8117-4886","authenticated-orcid":false,"given":"Ata","family":"Jodeiri","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz 51656, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"e011504","DOI":"10.1136\/bmjopen-2016-011504","article-title":"How does age-related macular degeneration affect real-world visual ability and quality of life? A systematic review","volume":"6","author":"Taylor","year":"2016","journal-title":"BMJ Open"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s40662-016-0063-5","article-title":"Epidemiology of age-related macular degeneration (AMD): Associations with cardiovascular disease phenotypes and lipid factors","volume":"3","author":"Pennington","year":"2016","journal-title":"Eye Vis."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Sotoudeh-Paima, S., Jodeiri, A., Hajizadeh, F., and Soltanian-Zadeh, H. (2022). Multi-scale convolutional neural network for automated AMD classification using retinal OCT images. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105368"},{"key":"ref_7","first-page":"1","article-title":"Artificial intelligence in retina","volume":"67","author":"Sadeghipour","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1097\/IAE.0000000000000134","article-title":"Anti-VEGF treatment in neovascular age-related macular degeneration: A treat-and-extend protocol over 2 years","volume":"34","author":"Abedi","year":"2014","journal-title":"Retina"},{"key":"ref_9","first-page":"552","article-title":"Age-related macular degeneration: A review of current therapies and new treatments","volume":"83","author":"Kniggendorf","year":"2020","journal-title":"Arq. Bras. Oftalmol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s00417-017-3850-3","article-title":"Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning","volume":"256","author":"Treder","year":"2018","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.neucom.2019.08.079","article-title":"Deep Learning Based Early Stage Diabetic Retinopathy Detection Using Optical Coherence Tomography","volume":"369","author":"Li","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1038\/s41433-021-01648-1","article-title":"Optical coherence tomography angiography in Alzheimer\u2019s disease: A systematic review and meta-analysis","volume":"36","author":"Katsimpris","year":"2022","journal-title":"Eye"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1038\/s41433-023-02483-2","article-title":"Optical coherence tomography angiography measurements in Parkinson\u2019s disease: A systematic review and meta-analysis","volume":"37","author":"Salehi","year":"2023","journal-title":"Eye"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Donica, V.C., Alexa, A.I., Pavel, I.A., Danielescu, C., Ciap\u0103, M.A., Donica, A.L., and Bogd\u0103nici, C.M. (2023). The Evolvement of OCT and OCT-A in Identifying Multiple Sclerosis Biomarkers. Biomedicines, 11.","DOI":"10.3390\/biomedicines11113031"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"5278196","DOI":"10.1155\/2018\/5278196","article-title":"Applications of Artificial Intelligence in Ophthalmology: General Overview","volume":"2018","author":"Lu","year":"2018","journal-title":"J. Ophthalmol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.oret.2016.12.009","article-title":"Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration","volume":"1","author":"Lee","year":"2017","journal-title":"Ophthalmology. Retin."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Alenezi, A., Alhamad, H., Brindhaban, A., Amizadeh, Y., Jodeiri, A., and Danishvar, S. (2024). Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach. Bioengineering, 11.","DOI":"10.3390\/bioengineering11040300"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hassan, B., Qin, S., Ahmed, R., Hassan, T., Taguri, A.H., Hashmi, S., and Werghi, N. (2021). Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy. Comput. Biol. Med., 136.","DOI":"10.1016\/j.compbiomed.2021.104727"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00417-018-04224-8","article-title":"Fully automated detection of retinal disorders by image-based deep learning","volume":"257","author":"Li","year":"2019","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"ref_25","first-page":"1","article-title":"On OCT image classification via deep learning","volume":"11","author":"Wang","year":"2019","journal-title":"IEEE Photonics J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"419","DOI":"10.2147\/OPTH.S235751","article-title":"Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma","volume":"14","author":"Zapata","year":"2020","journal-title":"Clin. Ophthalmol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e229960","DOI":"10.1001\/jamanetworkopen.2022.9960","article-title":"Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases","volume":"5","author":"Dong","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_28","first-page":"204","article-title":"A lightweight deep learning model for retinal optical coherence tomography image classification","volume":"33","author":"Anzar","year":"2022","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_29","first-page":"389","article-title":"Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning","volume":"26","author":"Ren","year":"2018","journal-title":"Technol. Health Care Off. J. Eur. Soc. Eng. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1097\/APO.0000000000000498","article-title":"Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset","volume":"11","author":"Zheng","year":"2022","journal-title":"Asia-Pac. J. Ophthalmol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, D., Ao, Y., and Liu, S. (2020). Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images. Symmetry, 12.","DOI":"10.3390\/sym12071067"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rahdar, A., Ahmadi, M.J., Naseripour, M., Akhtari, A., Sedaghat, A., Hosseinabadi, V.Z., Yarmohamadi, P., Hajihasani, S., and Mirshahi, R. (2023). Semi-supervised segmentation of retinoblastoma tumors in fundus images. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-39909-6"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TMI.2019.2903434","article-title":"Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment","volume":"38","author":"Colomer","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"e13746","DOI":"10.1002\/acm2.13746","article-title":"Semi-supervised classification of fundus images combined with CNN and GCN","volume":"23","author":"Duan","year":"2022","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tan, Z., Zhang, Q., Lan, G., Xu, J., Ou, C., An, L., Qin, J., and Huang, Y. (2024). OCT Retinopathy Classification via a Semi-Supervised Pseudo-Label Sub-Domain Adaptation and Fine-Tuning Method. Mathematics, 12.","DOI":"10.3390\/math12020347"},{"key":"ref_37","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Doll\u00e1r, P. (2020, January 13\u201319). Designing network design spaces. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref_39","unstructured":"Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. Workshop on Challenges in Representation Learning, ICML."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/656\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T05:10:30Z","timestamp":1772169030000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/656"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,25]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["diagnostics16050656"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16050656","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,25]]}}}