Kalman filter/deep-learning hybrid automatic boundary tracking of optical coherence tomography data for deep anterior lamellar keratoplasty (DALK)

Deep anterior lamellar keratoplasty (DALK) is a highly challenging partial thickness cornea transplant surgery that replaces the anterior cornea above Descemet's membrane (DM) with a donor cornea. In our previous work, we proposed the design of an optical coherence tomography (OCT) sensor integrated needle to acquire real-time M-mode images to provide depth feedback during OCT-guided needle insertion during Big Bubble DALK procedures. Machine learning and deep learning techniques were applied to M-mode images to automatically identify the DM in OCT M-scan data. However, such segmentation methods often produce inconsistent or jagged segmentation of the DM which reduces the model accuracy. Here we present a Kalman filter based OCT M-scan boundary tracking algorithm in addition to AI-based precise needle guidance to improve automatic DM segmentation for OCT-guided DALK procedures. By using the Kalman filter, the proposed method generates a smoother layer segmentation result from OCT M-mode images for more accurate tracking of the DM layer and epithelium. Initial ex vivo testing demonstrates that the proposed approach significantly increases the segmentation accuracy compared to conventional methods without the Kalman filter. Our proposed model can provide more consistent and precise depth sensing results, which has great potential to improve surgical safety and ultimately contributes to better patient outcomes.

Autonomous Robotic System with Optical Coherence Tomography Guidance for Vascular Anastomosis

Vascular anastomosis, the surgical connection of blood vessels, is essential in procedures such as organ transplants and reconstructive surgeries. The precision required limits accessibility due to the extensive training needed, with manual suturing leading to variable outcomes and revision rates up to 7.9%. Existing robotic systems, while promising, are either fully teleoperated or lack the capabilities necessary for autonomous vascular anastomosis. We present the Micro Smart Tissue Autonomous Robot (micro-STAR), an autonomous robotic system designed to perform vascular anastomosis on small-diameter vessels. The micro-STAR system integrates a novel suturing tool equipped with Optical Coherence Tomography (OCT) fiber-optic sensor and a microcamera, enabling real-time tissue detection and classification. Our system autonomously places sutures and manipulates tissue with minimal human intervention. In an ex vivo study, micro-STAR achieved outcomes competitive with experienced surgeons in terms of leak pressure, lumen reduction, and suture placement variation, completing 90% of sutures without human intervention. This represents the first instance of a robotic system autonomously performing vascular anastomosis on real tissue, offering significant potential for improving surgical precision and expanding access to high-quality care.