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Peer-Reviewed Publication
Comput Biol Med2023;166107453.November 1, 2023Journal Article

Visual modalities-based multimodal fusion for surgical phase recognition.

Bogyu Park1, Hyeongyu Chi2, Bokyung Park3, Jiwon Lee4, Hye Su Jin4, Sunghyun Park5, Woo Jin Hyung6, Min-Kook Choi7
1AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea. Electronic address: bgpark@hutom.io.
2AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea. Electronic address: hyeongyuc96@hutom.io.
3AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea. Electronic address: bokyung@hutom.io.
4AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea. Electronic address: jiwon@hutom.io.
5Yonsei University College of Medicine, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea. Electronic address: CODON@yush.ac.
6AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea; Yonsei University College of Medicine, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea. Electronic address: wjhyung@hutom.io.
7AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea. Electronic address: mkchoi@hutom.io.
PubMed DOI

Abstract

Surgical workflow analysis is essential to help optimize surgery by encouraging efficient communication and the use of resources. However, the performance of phase recognition is limited by the use of information related to the presence of surgical instruments. To address the problem, we propose visual modality-based multimodal fusion for surgical phase recognition to overcome the limited diversit…

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