Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Study on the Bleeding Detection Using Artificial Intelligence in Surgery Video

수술 동영상에서의 인공지능을 사용한 출혈 검출 연구

  • Si Yeon Jeong (Department of Industrial Management Engineering, College of Engineering, Gachon University) ;
  • Young Jae Kim (Department of Biomedical Engineering, College of IT Convergence, Gachon University ) ;
  • Kwang Gi Kim (Department of Biomedical Engineering, College of IT Convergence, Gachon University )
  • 정시연 (가천대학교 공과대학 산업공학과) ;
  • 김영재 (가천대학교 IT융합대학 의공학과) ;
  • 김광기 (가천대학교 IT융합대학 의공학과)
  • Received : 2023.04.29
  • Accepted : 2023.06.26
  • Published : 2023.06.30
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Abstract

Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient outcomes.

Keywords

Acknowledgement

본 연구는 2023년도 산업통상자원부 및 산업기술평가관리원(KEIT) 연구비 지원(K_G012001187801, 인공지능 기반 영상분석 기술을 탑재한 영상진단 의료기기 개발)과 경기도의 경기도 지역협력연구센터 사업의 일환으로 수행하였음[GRRC-가천2020(B02), AI 기반 의료영상 분석].

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