Sungman Cho

Papers

Total Citations

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About

Sungman Cho is a researcher at the intersection of artificial intelligence and surgical technology, with a primary focus on computer vision and deep learning applications in otologic surgery. His most cited work, "Video recognition of simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regions" (2021, 15 citations), represents a pioneering effort to automate the analysis of surgical video data. In this study, Cho and his team developed convolutional neural network models capable of simultaneously detecting surgical instruments and segmenting critical anatomical structures during mastoidectomy procedures. This contribution is significant for advancing computer-assisted surgical training, intraoperative decision support, and objective skill assessment in otolaryngology. By enabling real-time recognition of surgical tools and anatomical landmarks, Cho’s work lays the groundwork for more intelligent surgical systems that can enhance patient safety and surgical education. His research demonstrates a novel integration of clinical surgical knowledge with state-of-the-art machine learning techniques, marking him as an emerging leader in the field of AI-driven surgical analytics.