{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:41:52Z","timestamp":1789756912022,"version":"4.0.1"},"reference-count":42,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T00:00:00Z","timestamp":1752883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cRomanian Hub for Artificial Intelligence-HRIA\u201d, Smart Growth, Digitization and Financial Instruments Program","award":["334906"],"award-info":[{"award-number":["334906"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Optical Coherence Tomography has become a common imaging technique that enables a non-invasive and detailed visualization of the retina and allows for the identification of various diseases. Through the advancement of technology, the volume and complexity of OCT data have rendered manual analysis infeasible, creating the need for automated means of detection. Methods: This study investigates the ability of state-of-the-art object detection models, including the latest YOLO versions (from v8 to v12), YOLO-World, YOLOE, and RT-DETR, to accurately detect pathological biomarkers in two retinal OCT datasets. The AROI dataset focuses on fluid detection in Age-related Macular Degeneration, while the OCT5k dataset contains a wide range of retinal pathologies. Results: The experiments performed show that YOLOv12 offers the best balance between detection accuracy and computational efficiency, while YOLOE manages to consistently outperform all other models across both datasets and most classes, particularly in detecting pathologies that cover a smaller area. Conclusions: This work provides a comprehensive benchmark of the capabilities of state-of-the-art object detection for medical applications, specifically for identifying retinal pathologies from OCT scans, offering insights and a starting point for the development of future automated solutions for analysis in a clinical setting.<\/jats:p>","DOI":"10.3390\/diagnostics15141823","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T07:56:14Z","timestamp":1753084574000},"page":"1823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Can YOLO Detect Retinal Pathologies? A Step Towards Automated OCT Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Adriana-Ioana","family":"Ardelean","sequence":"first","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0098-4228","authenticated-orcid":false,"given":"Eugen-Richard","family":"Ardelean","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8426-588X","authenticated-orcid":false,"given":"Anca","family":"Marginean","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,19]]},"reference":[{"key":"ref_1","unstructured":"Bille, J.F. (2025, May 26). Optical Coherence Tomography (OCT): Principle and Technical Realization, High Resolution Imaging in Microscopy and Ophthalmology: New Frontiers in Biomedical Optics, Available online: http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK554044\/."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zysk, A.M., Nguyen, F.T., Oldenburg, A.L., Marks, D.L., and Boppart, S.A. (2007). Optical coherence tomography: A review of clinical development from bench to bedside. J. Biomed. Opt., 12.","DOI":"10.1117\/1.2793736"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1038\/86589","article-title":"Ultrahigh-resolution ophthalmic optical coherence tomography","volume":"7","author":"Drexler","year":"2001","journal-title":"Nat. Med."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhou, Y., Zhao, J., Yang, J., Yu, W., Chen, Y., and Li, X. (2022, January 27). Lesion Localization in OCT by Semi-Supervised Object Detection. Proceedings of the 2022 International Conference on Multimedia Retrieval, New York, NY, USA.","DOI":"10.1145\/3512527.3531418"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Brehar, R., Groza, A., Damian, I., Muntean, G., and Nicoar\u0103, S.D. (2023, January 24\u201326). Age-Related Macular Degeneration Biomarker Segmentation from OCT images. Proceedings of the 2023 24th International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania.","DOI":"10.1109\/CSCS59211.2023.00076"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Herlea, D.M., Iancu, B., and Ardelean, E.-R. (2025). A Study of Deep Learning Models for Audio Classification of Infant Crying in a Baby Monitoring System. Informatics, 12.","DOI":"10.3390\/informatics12020050"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kalita, H., Dandapat, S., and Kumar Bora, P. (2025, January 17\u201320). Contextual Self-Attention Based UNet Architecture for Fluid Segmentation in Retinal OCT B-scans. Proceedings of the Fifteenth Indian Conference on Computer Vision Graphics and Image Processing, New York, NY, USA.","DOI":"10.1145\/3702250.3702291"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Melin\u0161\u010dak, M. (2023, January 22\u201326). Attention-based U-net: Joint Segmentation of Layers and Fluids from Retinal OCT Images. Proceedings of the 2023 46th MIPRO ICT and Electronics Convention (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO57284.2023.10159914"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, X., Bamba, A.L., Song, X., Brott, B.C., Litovsky, S.H., and Gan, Y. (2023). Toward reliable calcification detection: Calibration of uncertainty in object detection from coronary optical coherence tomography images. J. Biomed. Opt., 28.","DOI":"10.1117\/1.JBO.28.3.036008"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rizzieri, N., Dall\u2019Asta, L., and Ozoli\u0146\u0161, M. (2024). Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9. Vision, 8.","DOI":"10.3390\/vision8030048"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, C., Che, S., Gong, H., Ding, Y., Luo, Y., Xi, J., Qi, L., and Zhang, G. (2024). PI-YOLO: Dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1347123"},{"key":"ref_13","unstructured":"Wahid, F., Ma, Y., Khan, D., Aamir, M., and Bukhari, S.U.K. (2025, June 03). Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods. Available online: https:\/\/arxiv.org\/abs\/2408.03393v2."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1038\/s41433-024-03585-1","article-title":"Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy","volume":"39","author":"Neri","year":"2025","journal-title":"Eye"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Melin\u0161\u010dak, M., Radmilovi\u010d, M., Vatavuk, Z., and Lon\u010dari\u0107, S. (October, January 27). AROI: Annotated Retinal OCT Images Database. Proceedings of the 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO52101.2021.9596934"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/s41597-024-04259-z","article-title":"OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers","volume":"12","author":"Arikan","year":"2025","journal-title":"Sci. Data"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Karn, P.K., and Abdulla, W.H. (2024). Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images. Bioengineering, 11.","DOI":"10.3390\/bioengineering11030240"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s10462-025-11253-3","article-title":"YOLOv12 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series","volume":"58","author":"Sapkota","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_19","unstructured":"Khanam, R., and Hussain, M. (2025). A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_22","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_23","unstructured":"Jocher, G., and Qiu, J. (2025, June 29). Ultralytics YOLO. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_24","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17\u201324). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y.M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_27","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_28","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-Centric Real-Time Object Detectors. arXiv."},{"key":"ref_29","unstructured":"Sapkota, R., Cheppally, R.H., Sharda, A., and Karkee, M. (2025). RF-DETR Object Detection vs YOLOv12: A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). DETRs Beat YOLOs on Real-time Object Detection. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_31","unstructured":"Lv, W., Zhao, Y., Chang, Q., Huang, K., Wang, G., and Liu, Y. (2025, June 04). RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer. Available online: https:\/\/arxiv.org\/abs\/2407.17140v1."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cheng, T., Song, L., Ge, Y., Liu, W., Wang, X., and Shan, Y. (2024). YOLO-World: Real-Time Open-Vocabulary Object Detection. arXiv.","DOI":"10.1109\/CVPR52733.2024.01599"},{"key":"ref_33","unstructured":"Wang, A., Liu, L., Chen, H., Lin, Z., Han, J., and Ding, G. (2025). YOLOE: Real-Time Seeing Anything. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Portase, R.L., Tolas, R., and Potolea, R. (2024). From Sensors to Insights: An Original Method for Consumer Behavior Identification in Appliance Usage. Electronics, 13.","DOI":"10.3390\/electronics13071364"},{"key":"ref_35","unstructured":"Blagec, K., Dorffner, G., Moradi, M., and Samwald, M. (2021). A critical analysis of metrics used for measuring progress in artificial intelligence. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1007\/s10115-024-02275-4","article-title":"A path-based distance computation for non-convexity with applications in clustering","volume":"67","author":"Ardelean","year":"2024","journal-title":"Knowl. Inf. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, F., Liu, S., Xiang, Y., Lin, Z., Li, C., Zhou, L., Gong, Y., Li, L., Li, Z., and Guo, C. (2021). Deep Learning for Detecting Subretinal Fluid and Discerning Macular Status by Fundus Images in Central Serous Chorioretinopathy. Front. Bioeng. Biotechnol., 9.","DOI":"10.3389\/fbioe.2021.651340"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Habe, T.T., Haataja, K., and Toivanen, P. (2025). Precision enhancement in wireless capsule endoscopy: A novel transformer-based approach for real-time video object detection. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1529814"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, W., Zhang, Y., Xu, T., An, T., Liang, Y., and Zhang, B. (2025). Object Detection for Medical Image Analysis: Insights from the RT-DETR Model. arXiv.","DOI":"10.1145\/3730436.3730506"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.4236\/ojapps.2025.154078","article-title":"Early Detection of Sexually Transmitted Infections Using YOLO 12: A Deep Learning Approach","volume":"15","author":"Chileshe","year":"2025","journal-title":"Open J. Appl. Sci."},{"key":"ref_41","unstructured":"Chen, Y.-H. (2025). Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"118189","DOI":"10.1016\/j.marpolbul.2025.118189","article-title":"Real-time dense small object detection model for floating litter detection and removal on water surfaces","volume":"218","author":"Ye","year":"2025","journal-title":"Mar. Pollut. Bull."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/14\/1823\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:12:44Z","timestamp":1760033564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/14\/1823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,19]]},"references-count":42,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["diagnostics15141823"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15141823","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,19]]}}}