Idris O. Sunmola

Northwestern University, Johns Hopkins University

Papers

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About

Idris O. Sunmola is a researcher at the forefront of surgical robotics and autonomous systems, with a focus on computer vision, machine learning, and tissue sensing. His work addresses critical challenges in minimally invasive surgery, particularly in automating complex tasks like suturing and anastomosis. Sunmola’s 2022 paper on capturing fine-grained details for video-based suturing skills assessment (13 citations) lays the groundwork for objective, automated evaluation of surgical proficiency. More recently, his 2024 study on automatic, real-time tissue sensing for autonomous intestinal anastomosis (4 citations) introduces a hybrid MLP-DC-CNN classifier integrated with optical coherence tomography, a key step toward reliable robotic tissue identification. This work directly supports the Smart Tissue Autonomous Robot (STAR) system, aiming to improve efficiency and consistency in laparoscopic procedures. By bridging deep learning with real-time surgical sensing, Sunmola is helping to push the boundaries of what autonomous surgical robots can achieve, promising safer and more reproducible outcomes in gastrointestinal, urologic, and gynecologic surgeries.