Reimagining partial thickness keratoplasty: An eye mountable robot for autonomous big bubble needle insertion

TL;DR

This paper introduces AUTO-DALK, an autonomous robotic system utilizing AI and OCT imaging for precise, consistent big bubble needle insertion in corneal surgery, improving safety and outcomes over manual and teleoperated methods.

Contribution

The study presents a novel AI-powered robotic system for autonomous big bubble generation in corneal surgery, integrating deep learning and OCT for precise needle control.

Findings

AUTO-DALK outperforms manual and teleoperated methods in accuracy and speed.

Significant improvement in big bubble formation success rate.

Demonstrated effectiveness in both cadaver and live animal models.

Abstract

Autonomous surgical robots have demonstrated significant potential to standardize surgical outcomes, driving innovations that enhance safety and consistency regardless of individual surgeon experience. Deep anterior lamellar keratoplasty (DALK), a partial thickness corneal transplant surgery aimed at replacing the anterior part of cornea above Descemet membrane (DM), would greatly benefit from an autonomous surgical approach as it highly relies on surgeon skill with high perforation rates. In this study, we proposed a novel autonomous surgical robotic system (AUTO-DALK) based on a customized neural network capable of precise needle control and consistent big bubble demarcation on cadaver and live rabbit models. We demonstrate the feasibility of an AI-based image-guided vertical drilling approach for big bubble generation, in contrast to the conventional horizontal needle approach. Our system integrates an optical coherence tomography (OCT) fiber optic distal sensor into the eye-mountable micro robotic system, which automatically segments OCT M-mode depth signals to identify corneal layers using a custom deep learning algorithm. It enables the robot to autonomously guide the needle to targeted tissue layers via a depth-controlled feedback loop. We compared autonomous needle insertion performance and resulting pneumo-dissection using AUTO-DALK against 1) freehand insertion, 2) OCT sensor guided manual insertion, and 3) teleoperated robotic insertion, reporting significant improvements in insertion depth, pneumo-dissection depth, task completion time, and big bubble formation. Ex vivo and in vivo results indicate that the AI-driven, AUTO-DALK system, is a promising solution to standardize pneumo-dissection outcomes for partial thickness keratoplasty.