Artificial Intelligence for Posterior Capsule Opacification

Gurnoor Gill¹David Josue Taylor Gonzalez²Mak Benjamin Djulgegovic³Harshal Sanghvi⁴Ayam Suleiman⁵Shailesh Gupta^2,5,6*

• ¹Florida Atlantic University Charles E Schmidt College of Medicine, Boca Raton, United States
• ²Broward Health North, Deerfield Beach, United States
• ³Wills Eye Hospital, Philadelphia, United States
• ⁴Florida Atlantic University College of Business, Boca Raton, United States
• ⁵Advanced Research LLC, Deerfield Beach, United States
• ⁶Speciality Retina Center LLC, Deerfield Beach, United States

Posterior capsule opacification (PCO) remains the most common long-term complication of cataract surgery, affecting up to one-fifth of patients within five years and often requiring neodymium: yttrium–aluminum–garnet (Nd: YAG) laser capsulotomy. Clinical decisions about if and when to intervene depend primarily on subjective assessments and carry nontrivial 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≈0.83 for continuous severity scores, C-index ≈0.87 for capsulotomy risk nomograms), reducing observer bias and standardizing care. To address the "black-box" 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.