Abstract:

Objective

The aim of this study is to explore the application of deep learning in the analysis of posterior capsule opacification（PCO）following intraocular lens（IOL）implantation.

Methods

Slit-lamp retroillumination images of 100 eyes from 62 patients who underwent cataract extraction combined with IOL implantation at the Shaanxi Eye Hospital between September 2020 and July 2023 were collected.The cohort included 24 males（34 eyes）and 38 females（66 eyes），with an average age of（58.7±9.82）years（ranging from 41 to 78 years）.The PCO analysis framework consisted of four main modules：IOL region segmentation，IOL center localization，opacification region segmentation，and extraction of opacification features.Both the IOL region segmentation and opacification region segmentation modules employed the U-Net model，trained on a dataset of 100 slit-lamp retroillumination images of posterior capsules with implanted IOL.The IOL center was localized using the geometric moment algorithm，while opacification feature extraction used a ResNet-based model to predict three visual quality metrics of patients.The performance of the IOL region segmentation model was evaluated against manually labeled test set annotations.Metrics such as Intersection-over-Union（IoU），Dice coefficient，and recall rate were calculated using Python software.The opacification region segmentation results were similarly evaluated with metrics including accuracy，precision，recall，and f1-score.For image regression tasks predicting OQAS（Optical Quality Analysis System）metrics，the mean absolute error（MAE）was computed using Python software.

Results

In the IOL region segmentation task，the test set achieved an IoU of 0.9117，Dice of 0.9527，recall of 0.9524，and f1-score of 0.9527.In the opacification region segmentation task，the test set achieved an average accuracy of 0.9690，precision of 0.9329，recall of 0.9264，and f1-score of 0.9191.In the visual indicator prediction task，the mean error of strehl ratio，modulation transfer function，object scatter index were2.4319，0.0154，3.4032.

Conclusions

The deep learning model for PCOanalysis incorporates four key modules：IOL region segmentation，IOL center localization，opacification region segmentation，and opacification feature extraction.This model enables automated preprocessing，precise segmentation of opacified regions，and the extraction of features related to visual quality metrics from opacified images.

Key words: Capsule opacification, Deep learning, Slit lamp microscopy, Medical image processing