論文

2020年3月

Surgical Phase Recognition Method with a Sequential Consistency for CAOS-AI Navigation System

LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
  • Shoichi Nishio
  • ,
  • Belayat Hossain
  • ,
  • Naomi Yagi
  • ,
  • Manabu Nii
  • ,
  • Takafumi Hiranaka
  • ,
  • Syoji Kobashi

開始ページ
8
終了ページ
10
記述言語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/LifeTech48969.2020.1570619203
出版者・発行元
IEEE

© 2020 IEEE. The procedure of orthopedic surgery is quite complicated, and many kinds of equipment have been used. Operating room nurses who deliver surgical instruments to surgeon are supposed to be forced to incur a heavy burden. There are some studies to recognize surgical phase with convolutional neural network (CNN) in minimally invasive laparoscopic surgery only. Previously, we proposed a computer-aided orthopedic surgery (CAOS)-AI navigation system based on CNN. However, the work propose a method to improve accuracy of phase recognition by considering temporal dependency of orthopedic surgery video acquired from surgeon-wearable video camera. The method estimates current surgical phase by combining both temporal dependency and convolutional-long-short term memory network (CNN-LSTM). Experimental results shows a phase recognition accuracy of 59.9% by the proposed method applied in unicomapartmenatal knee arthroplasty (UKA).

リンク情報
DOI
https://doi.org/10.1109/LifeTech48969.2020.1570619203
DBLP
https://dblp.uni-trier.de/rec/conf/lifetech/NishioHYNHK20
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085163512&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85085163512&origin=inward
URL
https://dblp.uni-trier.de/conf/lifetech/2020
URL
https://dblp.uni-trier.de/db/conf/lifetech/lifetech2020.html#NishioHYNHK20
ID情報
  • DOI : 10.1109/LifeTech48969.2020.1570619203
  • ISBN : 9781728170633
  • DBLP ID : conf/lifetech/NishioHYNHK20
  • SCOPUS ID : 85085163512

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