Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based Multiple Surgical Instrument Tracking in Training and Actual Operations
- 주제(키워드) surgical skills , robotic surgery , deep learning , surgical instrument tracking , quantitative evaluation
- 주제(기타) Medicine, General & Internal
- 설명문(일반) [Lee, Dongheon] Seoul Natl Univ, Grad Sch, Interdisciplinary Program, 101 Daehak Ro, Seoul 03080, South Korea; [Yu, Hyeong Won] Seoul Natl Univ, Dept Surg, Bundang Hosp, 82,Gumi Ro 173 Beon Gil, Seongnam Si 13620, Gyeonggi Do, South Korea; [Kwon, Hyungju] Ewha Womans Univ, Dept Surg, Med Ctr, 1071 Anyangcheon Ro, Seoul 07985, South Korea; [Kong, Hyoun-Joong] Chungnam Natl Univ Hosp, Dept Biomed Engn, 282 Munhwa Ro, Daejeon 301721, South Korea; [Kong, Hyoun-Joong] Coll Med, 282 Munhwa Ro, Daejeon 301721, South Korea; [Kong, Hyoun-Joong; Kim, Hee Chan] Seoul Natl Univ, Med Res Ctr, Inst Med & Biol Engn, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea; [Lee, Kyu Eun] Seoul Natl Univ Hosp, Dept Surg, 101 Daehak Ro, Seoul 03080, South Korea; [Lee, Kyu Eun] Coll Med, 101 Daehak Ro, Seoul 03080, South Korea; [Kim, Hee Chan] Seoul Natl Univ, Dept Biomed Engn, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea
- 등재 SCIE, SCOPUS
- OA유형 gold, Green Published
- 발행기관 MDPI
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000172363
- 본문언어 영어
- Published As https://dx.doi.org/10.3390/jcm9061964
- PubMed https://pubmed.ncbi.nlm.nih.gov/32585953
초록/요약
As the number of robotic surgery procedures has increased, so has the importance of evaluating surgical skills in these techniques. It is difficult, however, to automatically and quantitatively evaluate surgical skills during robotic surgery, as these skills are primarily associated with the movement of surgical instruments. This study proposes a deep learning-based surgical instrument tracking algorithm to evaluate surgeons' skills in performing procedures by robotic surgery. This method overcame two main drawbacks: occlusion and maintenance of the identity of the surgical instruments. In addition, surgical skill prediction models were developed using motion metrics calculated from the motion of the instruments. The tracking method was applied to 54 video segments and evaluated by root mean squared error (RMSE), area under the curve (AUC), and Pearson correlation analysis. The RMSE was 3.52 mm, the AUC of 1 mm, 2 mm, and 5 mm were 0.7, 0.78, and 0.86, respectively, and Pearson's correlation coefficients were 0.9 on thex-axis and 0.87 on they-axis. The surgical skill prediction models showed an accuracy of 83% with Objective Structured Assessment of Technical Skill (OSATS) and Global Evaluative Assessment of Robotic Surgery (GEARS). The proposed method was able to track instruments during robotic surgery, suggesting that the current method of surgical skill assessment by surgeons can be replaced by the proposed automatic and quantitative evaluation method.
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