{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T10:51:23Z","timestamp":1793962283218,"version":"4.8.0"},"reference-count":92,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>Posterior capsule opacification (PCO) remains the most common long-term complication of cataract surgery, affecting up to one-fifth of patients within 5 years and often requiring neodymium: yttrium\u2013aluminum\u2013garnet (Nd:YAG) laser capsulotomy. Clinical decisions about if and when to intervene depend primarily on subjective assessments and carry non-trivial risks, including transient intraocular pressure spikes, cystoid macular edema, and rare retinal detachment. Recent advances in artificial intelligence (AI), spanning classical machine learning and deep convolutional neural networks, offer an objective, data-driven framework to (1) detect and grade PCO severity from imaging (retro-illumination photographs, OCT, Scheimpflug tomography), (2) stratify individual risk of clinically significant opacification and personalize follow-up, and (3) support timing and dosing of Nd:YAG capsulotomy. AI models have achieved expert-level performance (e.g., AUC up to 0.97 for binary detection of vision-threatening PCO, correlation r \u2248 0.83 for continuous severity scores, C-index \u2248 0.87 for capsulotomy risk nomograms), reducing observer bias and standardizing care. To address the \u201cblack-box\u201d nature of complex models, mechanistic interpretability techniques, such as heatmaps and quantifiable feature extraction, are emerging to clarify decision logic and bolster clinician trust. Key challenges include assembling large, diverse, multi-center datasets (potentially via federated learning), prospective validation in real-world settings, regulatory approval, seamless integration into electronic health records and imaging workflows, and ensuring data privacy. Future directions emphasize true multimodal fusion of slit-lamp, OCT, and Scheimpflug tomography data, intraoperative feedback systems to minimize residual lens epithelial cells, patient-driven home monitoring via smartphone apps, and user-tunable AI thresholds to align with individual clinician and patient priorities. By combining transparent AI insights with surgical expertise, these tools can transform PCO management. They may optimize visual rehabilitation, minimize unnecessary procedures, and enhance safety in cataract postoperative care.<\/jats:p>","DOI":"10.3389\/fmed.2025.1695525","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T07:12:00Z","timestamp":1765523520000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial intelligence for posterior capsule opacification"],"prefix":"10.3389","volume":"12","author":[{"given":"Gurnoor","family":"Gill","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Taylor Gonzalez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harshal","family":"Sanghvi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mak","family":"Djulbegovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayam","family":"Suleiman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shailesh","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1002\/ima.22812","article-title":"A deep learning approach for classification of COVID and pneumonia using DenseNet-201.","volume":"32","author":"Sanghvi","year":"2022","journal-title":"Int J Imaging Syst Technol."},{"key":"B2","first-page":"1052","article-title":"Data science for e-healthcare, entertainment and finance.","volume-title":"In: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA).","author":"Sanghvi","year":"2021"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e69612","DOI":"10.7759\/cureus.69612","article-title":"Comparison of gemini advanced and ChatGPT 4.0\u2019s performances on the ophthalmology resident ophthalmic knowledge assessment program (OKAP) examination review question banks.","volume":"16","author":"Gill","year":"2024","journal-title":"Cureus."},{"key":"B4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/B978-0-12-824557-6.00011-X","article-title":"Autonomous robotic system for ultraviolet disinfection.","volume-title":"Cyber-Physical Systems: AI and COVID-19.","author":"Patel","year":"2022"},{"key":"B5","first-page":"1","article-title":"A comprehensive review on cloud security using machine learning techniques.","volume-title":"Artificial Intelligence in Cyber Security: Theories and Applications.","author":"Gangwani","year":"2023"},{"key":"B6","first-page":"619","article-title":"Role of information technology in education system.","volume-title":"In: Proceedings of the 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA).","author":"Sanghvi","year":"2021"},{"key":"B7","volume-title":"An Empirical Analysis of Diffusion, Autoencoders, and Adversarial Deep Learning Models for Predicting Dementia Using High-Fidelity MRI.","author":"Gajjar","year":"2023"},{"key":"B8","article-title":"A proposed framework for stutter detection: implementation on embedded systems.","volume-title":"In: Proceedings of the 2022 IEEE World Conference on Applied Intelligence and Computing (AIC).","author":"Alhalabi","year":"2025"},{"key":"B9","doi-asserted-by":"publisher","first-page":"e15734056286918","DOI":"10.2174\/0115734056286918240419100058","article-title":"An artificial intelligence driven approach for classification of ophthalmic images using convolutional neural network: an experimental study.","volume":"20","author":"Singh","year":"2024","journal-title":"Curr Med Imaging."},{"key":"B10","first-page":"91","article-title":"Revolutionizing patient care: the synergy of IoT and machine learning in smart healthcare.","volume-title":"Advances in Computers.","author":"Sanghvi","year":"2025"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e76209","DOI":"10.7759\/cureus.76209","article-title":"Enhancing ophthalmic care: the transformative potential of digital twins in healthcare.","volume":"16","author":"Banoub","year":"2024","journal-title":"Cureus."},{"key":"B12","doi-asserted-by":"publisher","first-page":"e77109","DOI":"10.7759\/cureus.77109","article-title":"Evaluation of Convolutional neural networks (CNNs) in Identifying retinal conditions through classification of Optical coherence tomography (OCT) Images.","volume":"17","author":"Teegavarapu","year":"2025","journal-title":"Cureus."},{"key":"B13","doi-asserted-by":"publisher","first-page":"e75476","DOI":"10.7759\/cureus.75476","article-title":"Differentiating cystic lesions in the sellar region of the brain using artificial intelligence and machine learning for early diagnosis: a prospective review of the novel diagnostic modalities.","volume":"16","author":"Patel","year":"2024","journal-title":"Cureus."},{"key":"B14","doi-asserted-by":"publisher","first-page":"e84036","DOI":"10.7759\/cureus.84036","article-title":"Artificial intelligence-driven telehealth framework for detecting nystagmus.","volume":"17","author":"Sanghvi","year":"2025","journal-title":"Cureus."},{"key":"B15","doi-asserted-by":"publisher","first-page":"337","DOI":"10.3390\/bioengineering11040337","article-title":"The role of AI in hospitals and clinics: transforming healthcare in the 21st century.","volume":"11","author":"Maleki Varnosfaderani","year":"2024","journal-title":"Bioengineering."},{"key":"B16","doi-asserted-by":"publisher","first-page":"e59954","DOI":"10.7759\/cureus.59954","article-title":"Unveiling the influence of AI predictive analytics on patient outcomes: a comprehensive narrative review.","volume":"16","author":"Dixon","year":"2024","journal-title":"Cureus."},{"key":"B17","doi-asserted-by":"publisher","first-page":"e70312","DOI":"10.1002\/hsr2.70312","article-title":"The impact of artificial intelligence on healthcare: a comprehensive review of advancements in diagnostics, treatment, and operational efficiency.","volume":"8","author":"Faiyazuddin","year":"2025","journal-title":"Health Sci Rep."},{"key":"B18","doi-asserted-by":"publisher","first-page":"440","DOI":"10.3390\/biomimetics9070440","article-title":"Innovation through Artificial intelligence in triage systems for resource optimization in future pandemics.","volume":"9","author":"Garrido","year":"2024","journal-title":"Biomimetics."},{"key":"B19","doi-asserted-by":"publisher","first-page":"e59906","DOI":"10.7759\/cureus.59906","article-title":"Use of artificial intelligence in triage in hospital emergency departments: a scoping review.","volume":"16","author":"Tyler","year":"2024","journal-title":"Cureus."},{"key":"B20","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1111\/cts.12884","article-title":"Precision medicine, AI, and the future of personalized health care.","volume":"14","author":"Johnson","year":"2021","journal-title":"Clin Transl Sci."},{"key":"B21","doi-asserted-by":"publisher","first-page":"12482","DOI":"10.1038\/s41598-025-97565-4","article-title":"Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings.","volume":"15","author":"Haripriya","year":"2025","journal-title":"Sci Rep."},{"key":"B22","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1016\/j.csbj.2025.02.022","article-title":"Designing and implementing SMILE: an AI-driven platform for enhancing clinical decision-making in mental health and neurodivergence management.","volume":"27","author":"Pesqueira","year":"2025","journal-title":"Comput Struct Biotechnol J."},{"key":"B23","doi-asserted-by":"publisher","first-page":"106848","DOI":"10.1016\/j.compbiomed.2023.106848","article-title":"Privacy-preserving artificial intelligence in healthcare: techniques and applications.","volume":"158","author":"Khalid","year":"2023","journal-title":"Comput Biol Med."},{"key":"B24","doi-asserted-by":"publisher","first-page":"e0000810","DOI":"10.1371\/journal.pdig.0000810","article-title":"Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.","volume":"4","author":"Weiner","year":"2025","journal-title":"PLoS Digit Health."},{"key":"B25","doi-asserted-by":"publisher","first-page":"e26297","DOI":"10.1016\/j.heliyon.2024.e26297","article-title":"Ethical and regulatory challenges of AI technologies in healthcare: a narrative review.","volume":"10","author":"Mennella","year":"2024","journal-title":"Heliyon."},{"key":"B26","doi-asserted-by":"publisher","first-page":"241873","DOI":"10.1098\/rsos.241873","article-title":"Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use.","volume":"12","author":"Pham","year":"2025","journal-title":"R Soc Open Sci."},{"key":"B27","doi-asserted-by":"publisher","first-page":"66","DOI":"10.31662\/jmaj.2024-0139","article-title":"Artificial intelligence applications in ophthalmology.","volume":"8","author":"Oshika","year":"2025","journal-title":"JMA J."},{"key":"B28","doi-asserted-by":"publisher","first-page":"101095","DOI":"10.1016\/j.xcrm.2023.101095","article-title":"Artificial intelligence in ophthalmology: the path to the real-world clinic.","volume":"4","author":"Li","year":"2023","journal-title":"Cell Rep Med."},{"key":"B29","doi-asserted-by":"publisher","first-page":"326","DOI":"10.3390\/diagnostics13020326","article-title":"Deep learning in optical coherence tomography angiography: current progress, challenges, and future directions.","volume":"13","author":"Yang","year":"2023","journal-title":"Diagnostics."},{"key":"B30","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s40123-025-01109-y","article-title":"A systematic review of advances in AI-assisted analysis of fundus fluorescein angiography (FFA) images: from detection to report generation.","volume":"14","author":"Yu","year":"2025","journal-title":"Ophthalmol Ther."},{"key":"B31","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.survophthal.2021.03.003","article-title":"Artificial intelligence: the unstoppable revolution in ophthalmology.","volume":"67","author":"Benet","year":"2022","journal-title":"Surv Ophthalmol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"690","DOI":"10.3390\/jpm14070690","article-title":"Novel approaches for early detection of retinal diseases using artificial intelligence.","volume":"14","author":"Sorrentino","year":"2024","journal-title":"J Pers Med."},{"key":"B33","doi-asserted-by":"publisher","first-page":"326","DOI":"10.22336\/rjo.2023.53","article-title":"Evaluation of structural retinal layer alterations in retinitis pigmentosa.","volume":"67","author":"Yavuzer","year":"2023","journal-title":"Rom J Ophthalmol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.ajo.2025.02.022","article-title":"Diagnostic accuracy of IDX-DR for detecting diabetic retinopathy: a systematic review and meta-analysis.","volume":"273","author":"Khan","year":"2025","journal-title":"Am J Ophthalmol."},{"key":"B35","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3390\/medicina61030433","article-title":"Enhancing ophthalmic diagnosis and treatment with artificial intelligence.","volume":"61","author":"Olawade","year":"2025","journal-title":"Medicina."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1576465","DOI":"10.3389\/fcell.2025.1576465","article-title":"Artificial intelligence technology in ophthalmology public health: current applications and future directions.","volume":"13","author":"Chen","year":"2025","journal-title":"Front Cell Dev Biol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1186\/s12886-024-03381-1","article-title":"Artificial intelligence in age-related macular degeneration: state of the art and recent updates.","volume":"24","author":"Crincoli","year":"2024","journal-title":"BMC Ophthalmol."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.3390\/ph17111440","article-title":"Application of artificial intelligence models to predict the onset or recurrence of neovascular age-related macular degeneration.","volume":"17","author":"Sorrentino","year":"2024","journal-title":"Pharmaceuticals."},{"key":"B39","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.2147\/OPTH.S529187","article-title":"Evaluating the predictive accuracy of an AI-based tool for postoperative vault estimation in phakic intraocular lens implantation.","volume":"19","author":"Zaldivar","year":"2025","journal-title":"Clin Ophthalmol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"3499","DOI":"10.2147\/OPTH.S438127","article-title":"Artificial intelligence in ophthalmic surgery: current applications and expectations.","volume":"17","author":"Nuliqiman","year":"2023","journal-title":"Clin Ophthalmol."},{"key":"B41","doi-asserted-by":"publisher","first-page":"100084","DOI":"10.1016\/j.apjo.2024.100084","article-title":"Natural language processing in medicine and ophthalmology: a review for the 21st-century clinician.","volume":"13","author":"Rojas-Carabali","year":"2024","journal-title":"Asia Pac J Ophthalmol."},{"key":"B42","doi-asserted-by":"publisher","first-page":"906554","DOI":"10.3389\/fmed.2022.906554","article-title":"Applications of natural language processing in ophthalmology: present and future.","volume":"9","author":"Chen","year":"2022","journal-title":"Front Med."},{"key":"B43","doi-asserted-by":"publisher","first-page":"1642","DOI":"10.3390\/healthcare13141642","article-title":"Exploring the role of Artificial intelligence in smart healthcare: a capability and function-oriented review.","volume":"13","author":"Abbas","year":"2025","journal-title":"Healthcare."},{"key":"B44","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","article-title":"Multimodal biomedical AI.","volume":"28","author":"Acosta","year":"2022","journal-title":"Nat Med."},{"key":"B45","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3390\/diagnostics13010100","article-title":"Comprehensive review on the use of artificial intelligence in ophthalmology and future research directions.","volume":"13","author":"Anton","year":"2022","journal-title":"Diagnostics."},{"key":"B46","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1097\/01.APO.0000656988.16221.04","article-title":"Artificial intelligence for cataract detection and management.","volume":"9","author":"Goh","year":"2020","journal-title":"Asia Pac J Ophthalmol."},{"key":"B47","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40662-021-00273-z","article-title":"Application of artificial intelligence in cataract management: current and future directions.","volume":"9","author":"Gutierrez","year":"2022","journal-title":"Eye Vis."},{"key":"B48","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1136\/bjophthalmol-2019-314729","article-title":"Universal artificial intelligence platform for collaborative management of cataracts.","volume":"103","author":"Wu","year":"2019","journal-title":"Br J Ophthalmol."},{"key":"B49","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1097\/APO.0000000000000394","article-title":"Artificial intelligence in cornea, refractive surgery, and cataract: basic principles, clinical applications, and future directions.","volume":"10","author":"Rampat","year":"2021","journal-title":"Asia Pac J Ophthalmol."},{"key":"B50","doi-asserted-by":"publisher","first-page":"101163","DOI":"10.1016\/j.lanepe.2024.101163","article-title":"Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data.","volume":"49","author":"van der Meijden","year":"2024","journal-title":"Lancet Reg Health Eur."},{"key":"B51","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1097\/SLA.0000000000005853","article-title":"Artificial intelligence-enabled decision support in surgery: state-of-the-art and future directions.","volume":"278","author":"Loftus","year":"2023","journal-title":"Ann Surg."},{"key":"B52","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1016\/j.surg.2021.10.002","article-title":"The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: a systematic review.","volume":"171","author":"Stam","year":"2022","journal-title":"Surgery."},{"key":"B53","doi-asserted-by":"publisher","first-page":"104956","DOI":"10.1016\/j.amsu.2022.104956","article-title":"Performance of machine learning algorithms for surgical site infection case detection and prediction: a systematic review and meta-analysis.","volume":"84","author":"Wu","year":"2022","journal-title":"Ann Med Surg."},{"key":"B54","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1001\/archopht.1989.01070020048027","article-title":"Axial myopia increases the risk of retinal complications after neodymium-YAG laser posterior capsulotomy.","volume":"107","author":"Koch","year":"1989","journal-title":"Arch Ophthalmol."},{"key":"B55","doi-asserted-by":"publisher","first-page":"e14268","DOI":"10.1002\/acm2.14268","article-title":"Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification.","volume":"25","author":"Liu","year":"2024","journal-title":"J Appl Clin Med Phys."},{"key":"B56","doi-asserted-by":"publisher","first-page":"e000992","DOI":"10.1136\/bmjophth-2022-000992","article-title":"Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification.","volume":"7","author":"Huemer","year":"2022","journal-title":"BMJ Open Ophthalmol."},{"key":"B57","volume-title":"Automatic Quantification of the Severity of Posterior Capsule Opacification Using Deep Learning.","author":"Peer","year":"2024"},{"key":"B58","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.jcrs.2011.09.036","article-title":"Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.","volume":"38","author":"Mohammadi","year":"2012","journal-title":"J Cataract Refract Surg."},{"key":"B59","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1186\/s12886-025-03983-3","article-title":"Influencing factors and prediction model construction of posterior capsular opacification after intraocular lens implantation treated with Nd: yag laser.","volume":"25","author":"Chang","year":"2025","journal-title":"BMC Ophthalmol."},{"key":"B60","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1186\/s12886-023-03064-3","article-title":"Objective quantification of posterior capsule opacification after cataract surgery with swept-source optical coherence tomography.","volume":"23","author":"Zhou","year":"2023","journal-title":"BMC Ophthalmol."},{"key":"B61","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1093\/jamia\/ocad213","article-title":"Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.","volume":"31","author":"Lin","year":"2024","journal-title":"J Am Med Inform Assoc."},{"key":"B62","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s00417-023-06170-6","article-title":"A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study.","volume":"262","author":"Mastropasqua","year":"2024","journal-title":"Graefes Arch Clin Exp Ophthalmol."},{"key":"B63","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1186\/s12886-024-03510-w","article-title":"Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma.","volume":"24","author":"Lee","year":"2024","journal-title":"BMC Ophthalmol."},{"key":"B64","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1167\/tvst.13.5.17","article-title":"Prediction of visual outcome after rhegmatogenous retinal detachment surgery using Artificial intelligence techniques.","volume":"13","author":"Guo","year":"2024","journal-title":"Transl Vis Sci Technol."},{"key":"B65","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/s00417-022-05884-3","article-title":"Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study.","volume":"261","author":"Fung","year":"2023","journal-title":"Graefes Arch Clin Exp Ophthalmol."},{"key":"B66","doi-asserted-by":"publisher","first-page":"2683","DOI":"10.1364\/BOE.446519","article-title":"Automatic evaluation of graft orientation during Descemet membrane endothelial keratoplasty using intraoperative OCT.","volume":"13","author":"Muijzer","year":"2022","journal-title":"Biomed Opt Express."},{"key":"B67","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1167\/tvst.12.5.14","article-title":"Deep learning using preoperative AS-OCT predicts graft detachment in DMEK.","volume":"12","author":"Patefield","year":"2023","journal-title":"Transl Vis Sci Technol."},{"key":"B68","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1167\/tvst.12.5.3","article-title":"Aqueous flare changes in Ex-PRESS glaucoma shunt eyes after 4.7 Tesla high-field magnetic resonance imaging.","volume":"12","author":"Edo","year":"2023","journal-title":"Transl Vis Sci Technol."},{"key":"B69","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1167\/tvst.10.13.23","article-title":"Phacotrainer: a multicenter study of deep learning for activity recognition in cataract surgical videos.","volume":"10","author":"Yeh","year":"2021","journal-title":"Transl Vis Sci Technol."},{"key":"B70","doi-asserted-by":"publisher","first-page":"2969","DOI":"10.2147\/OPTH.S489054","article-title":"Applications of Artificial intelligence in cataract surgery: a review.","volume":"18","author":"Ahuja","year":"2024","journal-title":"Clin Ophthalmol."},{"key":"B71","doi-asserted-by":"publisher","first-page":"5150","DOI":"10.3390\/jcm14145150","article-title":"The role of artificial intelligence in the diagnosis and management of diabetic retinopathy.","volume":"14","author":"Ansari","year":"2025","journal-title":"J Clin Med."},{"key":"B72","doi-asserted-by":"publisher","first-page":"9","DOI":"10.4103\/DLJO.DLJO_7_23","article-title":"Artificial intelligence: a review of objective grading and quantification of posterior Capsular Opacification.","volume":"33","author":"Kushwaha","year":"2023","journal-title":"Delhi J. Ophthalmol."},{"key":"B73","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/eye.1999.126","article-title":"Cell biology of posterior capsular opacification.","volume":"13","author":"Marcantonio","year":"1999","journal-title":"Eye."},{"key":"B74","doi-asserted-by":"publisher","first-page":"2847","DOI":"10.3390\/jcm10132847","article-title":"Posterior capsule opacification: a review of experimental studies.","volume":"10","author":"Konopi\u0144ska","year":"2021","journal-title":"J Clin Med."},{"key":"B75","doi-asserted-by":"publisher","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":"B76","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1080\/08820538.2024.2432902","article-title":"Leveraging Artificial intelligence for diabetic retinopathy screening and management: history and current advances.","volume":"40","author":"Rajalakshmi","year":"2024","journal-title":"Semin Ophthalmol."},{"key":"B77","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.2337\/dci23-0032","article-title":"Artificial intelligence and diabetic retinopathy: ai framework, prospective studies, head-to-head validation, and cost-effectiveness.","volume":"46","author":"Rajesh","year":"2023","journal-title":"Diabetes Care."},{"key":"B78","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1007\/s40123-023-00723-y","article-title":"Development and validation of a prediction model for Nd:yag laser capsulotomy: a retrospective cohort study of 9768 eyes.","volume":"12","author":"Li","year":"2023","journal-title":"Ophthalmol Ther."},{"key":"B79","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.ophtha.2022.12.022","article-title":"Incidence of retinal detachment, macular edema, and ocular hypertension after neodymium:yttrium-aluminum-garnet capsulotomy: a population-based nationwide study-the french YAG 2 study.","volume":"130","author":"Dot","year":"2023","journal-title":"Ophthalmology."},{"key":"B80","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.3390\/jcm14051605","article-title":"Shaping the future of healthcare: ethical clinical challenges and pathways to trustworthy AI.","volume":"14","author":"Goktas","year":"2025","journal-title":"J Clin Med."},{"key":"B81","doi-asserted-by":"publisher","first-page":"714","DOI":"10.21037\/atm-20-976","article-title":"Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization.","volume":"8","author":"Wu","year":"2020","journal-title":"Ann Transl Med."},{"key":"B82","doi-asserted-by":"publisher","first-page":"2835","DOI":"10.3390\/diagnostics12112835","article-title":"Federated learning in ocular imaging: current progress and future direction.","volume":"12","author":"Nguyen","year":"2022","journal-title":"Diagnostics."},{"key":"B83","doi-asserted-by":"publisher","first-page":"948","DOI":"10.3390\/biomedinformatics3040058","article-title":"Federated learning for diabetic retinopathy detection using vision transformers.","volume":"3","author":"Chetoui","year":"2023","journal-title":"BioMedInformatics."},{"key":"B84","doi-asserted-by":"publisher","first-page":"e0201142","DOI":"10.1371\/journal.pone.0201142","article-title":"Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network.","volume":"13","author":"Jiang","year":"2018","journal-title":"PLoS One."},{"key":"B85","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.","volume":"1","author":"Abr\u00e0moff","year":"2018","journal-title":"NPJ Digit Med."},{"key":"B86","doi-asserted-by":"publisher","first-page":"21012","DOI":"10.48550\/arXiv.2312.03401","article-title":"Predicting postoperative intraocular lens dislocation in cataract surgery via deep learning.","volume":"12","author":"Ghamsarian","year":"2024","journal-title":"IEEE Access."},{"key":"B87","doi-asserted-by":"publisher","first-page":"e001475","DOI":"10.1136\/bmjophth-2023-001475","article-title":"Feasibility of an artificial intelligence phone call for postoperative care following cataract surgery in a diverse population: two phase prospective study protocol.","volume":"9","author":"Hatamnejad","year":"2024","journal-title":"BMJ Open Ophthalmol."},{"key":"B88","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s12886-020-01710-8","article-title":"Utility of the optical quality analysis system for decision-making in Nd: yag laser posterior capsulotomy in patients with light posterior capsule opacity.","volume":"21","author":"Lu","year":"2021","journal-title":"BMC Ophthalmol."},{"key":"B89","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2211.06114","article-title":"Treatment classification of posterior capsular opacification (PCO) using automated ground truths.","author":"Shrestha","year":"2022","journal-title":"arXiv [preprint]."},{"key":"B90","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1038\/s41433-019-0630-9","article-title":"5 year incidence of YAG capsulotomy and PCO after cataract surgery with single-piece monofocal intraocular lenses: a real-world evidence study of 20,763 eyes.","volume":"34","author":"Ursell","year":"2020","journal-title":"Eye."},{"key":"B91","doi-asserted-by":"publisher","first-page":"4665","DOI":"10.1167\/iovs.02-0634","article-title":"The effect of posterior capsule opacification on visual function.","volume":"44","author":"Meacock","year":"2003","journal-title":"Invest Ophthalmol Vis Sci."},{"key":"B92","doi-asserted-by":"publisher","first-page":"2984","DOI":"10.4103\/IJO.IJO_3372_22","article-title":"Accuracy of an artificial intelligence-based mobile application for detecting cataracts: results from a field study.","volume":"71","author":"Vasan","year":"2023","journal-title":"Indian J Ophthalmol."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1695525\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T07:12:05Z","timestamp":1765523525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1695525\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":92,"alternative-id":["10.3389\/fmed.2025.1695525"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2025.1695525","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,12]]},"article-number":"1695525"}}