Deokseok Kim1, Woojoong Hwang2, Joonseong Bae3, Hyeyeon Park3, Kwang Gi Kim4,5. 1. MTEG Co. Ltd., Seoul, Korea. 2. Service Development Department, MTEG Co. Ltd., Seoul, Korea. 3. Service Planning Department, MTEG Co. Ltd., Seoul, Korea. 4. Medical Devices R&D Center, Gachon University Gil Hospital, Incheon, Korea. 5. Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon, Korea.
Abstract
OBJECTIVES: As endoscopic, laparoscopic, and robotic surgical procedures become more common, surgical videos are increasingly being treated as records and serving as important data sources for education, research, and developing new solutions with recent advances in artificial intelligence (AI). However, most hospitals do not have a system that can store and manage such videos systematically. This study aimed to develop a system to help doctors manage surgical videos and turn them into content and data. METHODS: We developed a video archiving and communication system (VACS) to systematically process surgical videos. The VACS consists of a video capture device called SurgBox and a video archiving system called SurgStory. SurgBox automatically transfers surgical videos recorded in the operating room to SurgStory. SurgStory then analyzes the surgical videos and indexes important sections or video frames to provide AI reports. It allows doctors to annotate classified indexing frames, "data-ize" surgical information, create educational content, and communicate with team members. RESULTS: The VACS collects surgical and procedural videos, and helps users manage archived videos. The accuracy of a convolutional neural network learning model trained to detect the top five surgical instruments reached 96%. CONCLUSIONS: With the advent of the VACS, the informational value of medical videos has increased. It is possible to improve the efficiency of doctors' continuing education by making video-based online learning more active and supporting research using data from medical videos. The VACS is expected to promote the development of new AI-based products and services in surgical and procedural fields.
OBJECTIVES: As endoscopic, laparoscopic, and robotic surgical procedures become more common, surgical videos are increasingly being treated as records and serving as important data sources for education, research, and developing new solutions with recent advances in artificial intelligence (AI). However, most hospitals do not have a system that can store and manage such videos systematically. This study aimed to develop a system to help doctors manage surgical videos and turn them into content and data. METHODS: We developed a video archiving and communication system (VACS) to systematically process surgical videos. The VACS consists of a video capture device called SurgBox and a video archiving system called SurgStory. SurgBox automatically transfers surgical videos recorded in the operating room to SurgStory. SurgStory then analyzes the surgical videos and indexes important sections or video frames to provide AI reports. It allows doctors to annotate classified indexing frames, "data-ize" surgical information, create educational content, and communicate with team members. RESULTS: The VACS collects surgical and procedural videos, and helps users manage archived videos. The accuracy of a convolutional neural network learning model trained to detect the top five surgical instruments reached 96%. CONCLUSIONS: With the advent of the VACS, the informational value of medical videos has increased. It is possible to improve the efficiency of doctors' continuing education by making video-based online learning more active and supporting research using data from medical videos. The VACS is expected to promote the development of new AI-based products and services in surgical and procedural fields.
Entities:
Keywords:
Artificial Intelligence; Education; General Surgery; Information Management
Authors: Yu Jin Seol; So Hyun Park; Young Jae Kim; Young-Taek Park; Hee Young Lee; Kwang Gi Kim Journal: Sensors (Basel) Date: 2022-06-15 Impact factor: 3.847