Assessment of Postoperative Retinal Complications and the Possibility of Optical Diagnostics Using Support Vector Machine
DOI:
https://doi.org/10.56294/saludcyt2024.1359Keywords:
Cataract, Surgery, Optical Coherence Tomography, Machine Learning, Support Vector MachineAbstract
Introduction: Cataract is a prevalent eye condition that affects millions of individuals worldwide, leading to visual impairment and reduced quality of life. Cataract surgery is the most effective treatment, but post-surgical complications can arise, impacting the success of the intervention. Optical coherence tomography (OCT) has emerged as a valuable imaging technique for evaluating these complications, but the manual interpretation of OCT images is time-consuming and subjective.
Objective: In this study, we aimed to assess the performance of a machine learning (ML) tool specifically developed for detecting post-surgical complications in cataract patients.
Methods: We employed a support vector machine (SVM) algorithm to analyze a comprehensive dataset of OCT images. The dataset comprised 700 OCT images obtained post-surgery, including patients with cystoid macular oedema (CMO), retinal detachment (RD), and healthy individuals. The ML tool utilized pre-processed OCT images with annotations provided by expert ophthalmologists, undergoing retinal layer segmentation using intensity-based features.
Results: The SVM algorithm demonstrated high sensitivity and specificity in detecting and classifying post-surgical complications. It achieved a sensitivity of 92.5% in detecting CMO and 90.9% in identifying RD. The specificity of the algorithm was 90.9% and 96.2% for these complications, respectively. The overall accuracy of the ML tool in correctly identifying and classifying post-surgical complications was 92%.
Conclusions: The integration of ML algorithms in OCT imaging shows promise in enhancing the accuracy and efficiency of assessing post-surgical complications in cataract patients. The ML tool developed in this study provides reliable and objective assessments, reducing the subjectivity and variability associated with the manual interpretation of OCT images
References
1. Song P, Wang H, Theodoratou E, Chan KY, Rudan I. The national and subnational prevalence of cataract and cataract blindness in China: a systematic review and meta-analysis. Journal of Global Health. 2018;8(1):010804. https://doi.org/10.7189/jogh.08.010804
2. Cicinelli MV, Buchan JC, Nicholson M, Varadaraj V, Khanna RC. Cataracts. Lancet. 2023;401(10374):377-389. https://doi.org/10.1016/S0140-6736(22)01839-6
3. Polack S. Restoring sight: how cataract surgery improves the lives of older adults. Community Eye Health. 2008;21(66):24–25.
4. Stein JD. Serious adverse events after cataract surgery. Current Opinion in Ophthalmology. 2012;23(3):219–225. https://doi.org/10.1097/ICU.0b013e3283524068
5. Yong GY, Mohamed-Noor J, Salowi MA, Adnan TH, Zahari M. Risk factors affecting cataract surgery outcome: The Malaysian cataract surgery registry. PloS one. 2022;17(9):e0274939. https://doi.org/10.1371/journal.pone.0274939
6. Vukicevic M, Gin T, Al-Qureshi S. Prevalence of optical coherence tomography-diagnosed postoperative cystoid macular oedema in patients following uncomplicated phacoemulsification cataract surgery. Clinical & Experimental Ophthalmology. 2012;40(3):282-287. https://doi.org/10.1111/j.1442-9071.2011.02638.x
7. Nguyen P, Chopra V. Applications of optical coherence tomography in cataract surgery. Current Opinion in Ophthalmology. 2013 Jan;24(1):47-52. https://doi.org/10.1097/ICU.0b013e32835aee7b
8. Fujimoto JG, Pitris C, Boppart SA, Brezinski ME. Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia (New York, N.Y.). 2000;2(1-2):9–25. https://doi.org/10.1038/sj.neo.7900071
9. Ang M, Baskaran M, Werkmeister RM, Chua J, Schmidl D, dos Santos VA, et al. Anterior segment optical coherence tomography. Progress in Retinal and Eye Research. 2018;66:132-156. https://doi.org/10.1016/j.preteyeres.2018.04.002
10. Zeppieri M, Marsili S, Enaholo ES, Shuaibu AO, Uwagboe N, Salati C, et al. Optical Coherence Tomography (OCT): A Brief Look at the Uses and Technological Evolution of Ophthalmology. Medicina. 2023;59:2114. https://doi.org/10.3390/medicina59122114
11. Karn PK, Abdulla WH. On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images. Bioengineering (Basel, Switzerland). 2023;10(4):407. https://doi.org/10.3390/bioengineering10040407
12. Balyen L, Peto T. Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology. Asia-Pacific Journal of Ophthalmology. 2019;8(3):264-272. https://doi.org/10.22608/APO.2018479
13. Yang D, Ran AR, Nguyen TX, Lin TPH, Chen H, Lai TYY, et al. Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions. Diagnostics. 2023;13(2):326. https://doi.org/10.3390/diagnostics13020326
14. Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Frontiers in Public Health. 2023;11:1273253. https://doi.org/10.3389/fpubh.2023.1273253
15. Kulmaganbetov M, Bevan RJ, Anantrasirichai N, Achim A, Erchova I, White N, et al. Textural Feature Analysis of Optical Coherence Tomography Phantoms. Electronics. 2022;11(4):669. https://doi.org/10.3390/electronics11040669
16. 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. https://doi.org/10.7759/cureus.41615
17. Chu CJ, Johnston RL, Buscombe C, Sallam AB, Mohamed Q, Yang YC, et al. Risk Factors and Incidence of Macular Edema after Cataract Surgery: A Database Study of 81984 Eyes. Ophthalmology. 2016;123(2):316-323. https://doi.org/10.1016/j.ophtha.2015.10.001
18. Olsen T, Jeppesen P. The incidence of retinal detachment after cataract surgery. The Open Ophthalmology Journal. 2012;6:79–82. https://doi.org/10.2174/1874364101206010079
19. Moraru AD, Costin D, Moraru RL, Branisteanu DC. Artificial intelligence and deep learning in ophthalmology - present and future (Review). Experimental and Therapeutic Medicine. 2020;20:3469-3473. https://doi.org/10.3892/etm.2020.9118
20. Srivastava O, Tennant M, Grewal P, Rubin U, Seamone M. Artificial intelligence and machine learning in ophthalmology: A review. Indian Journal of Ophthalmology. 2023;71(1):11–17. https://doi.org/10.4103/ijo.IJO_1569_22
21. Yanagihara RT, Lee CS, Ting DSW, Lee AY. Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review. Translational Vision Science and Technology. 2020;9(2):11. https://doi.org/10.1167/tvst.9.2.11
22. Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science. 2021;2(3):160. https://doi.org/10.1007/s42979-021-00592-x
23. Scheer L, Marcus-Freeman S. Using Optical Coherence Tomography in the Management of Postoperative Wound Leaks After Cataract Surgery. Federal practitioner: for the health care professionals of the VA, DoD, and PHS. 2019;36(8):356–364.
24. Romo-Bucheli D, Seeböck P, Orlando JI, Gerendas BS, Waldstein SM, Schmidt-Erfurth U, et al. Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina. Biomedical Optics Express. 2019;11(1):346–363. https://doi.org/10.1364/BOE.379978.
Published
Issue
Section
License
Copyright (c) 2025 Mansur Inkarbekov , Mukhit Kulmaganbetov , Galiya Bazarbekova, Almagul Baiyrkhanova (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
