Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning

TL;DR

This paper introduces a fully-automatic deep learning framework that predicts intraocular lens dislocation during cataract surgery by analyzing surgical videos, potentially aiding in better intraoperative decision-making and post-surgical outcomes.

Contribution

It presents the first comprehensive CNN-based system for real-time assessment of lens stability and dislocation risk during cataract surgery, leveraging large-scale video data.

Findings

Significant correlation between lens unfolding delay and rotation.

Differences in intraoperative stability among four lens brands.

Framework reliably evaluates lens behavior during surgery.

Abstract

A critical yet unpredictable complication following cataract surgery is intraocular lens dislocation. Postoperative stability is imperative, as even a tiny decentration of multifocal lenses or inadequate alignment of the torus in toric lenses due to postoperative rotation can lead to a significant drop in visual acuity. Investigating possible intraoperative indicators that can predict post-surgical instabilities of intraocular lenses can help prevent this complication. In this paper, we develop and evaluate the first fully-automatic framework for the computation of lens unfolding delay, rotation, and instability during surgery. Adopting a combination of three types of CNNs, namely recurrent, region-based, and pixel-based, the proposed framework is employed to assess the possibility of predicting post-operative lens dislocation during cataract surgery. This is achieved via performing a large-scale study on the statistical differences between the behavior of different brands of intraocular lenses and aligning the results with expert surgeons' hypotheses and observations about the lenses. We exploit a large-scale dataset of cataract surgery videos featuring four intraocular lens brands. Experimental results confirm the reliability of the proposed framework in evaluating the lens' statistics during the surgery. The Pearson correlation and t-test results reveal significant correlations between lens unfolding delay and lens rotation and significant differences between the intra-operative rotations stability of four groups of lenses. These results suggest that the proposed framework can help surgeons select the lenses based on the patient's eye conditions and predict post-surgical lens dislocation.