Intraoperative tissue identification for gastrointestinal disorder by Raman spectroscopy and machine learning

Yusuke Oshima; Katsuhiro Ogawa; Haruto Fumuro; Takashi Katagiri; Masafumi Inomata

doi:10.1117/12.3005335

12 March 2024 Intraoperative tissue identification for gastrointestinal disorder by Raman spectroscopy and machine learning

Yusuke Oshima, Katsuhiro Ogawa, Haruto Fumuro, Takashi Katagiri, Masafumi Inomata

Author Affiliations +

Proceedings Volume 12839, Biomedical Vibrational Spectroscopy 2024: Advances in Research and Industry; 1283903 (2024) https://doi.org/10.1117/12.3005335
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

In this study, we evaluated surgical specimens obtained from patients for detecting resection merging in Hirschsprung’s disease. Conventional multivariate analyses successfully characterized Raman spectral data. Furthermore, the Raman spectroscopic approach combined with machine learning methods successfully predicted whether the target specimen was healthy or diseased by the decision algorithm. Toward practical use, we developed a portable Raman spectroscopic system and a fiber-optic Raman probe for laparoscopic surgery. we performed in vivo Raman measurement of abdominal organs using a live porcine during laparoscopy.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yusuke Oshima, Katsuhiro Ogawa, Haruto Fumuro, Takashi Katagiri, and Masafumi Inomata "Intraoperative tissue identification for gastrointestinal disorder by Raman spectroscopy and machine learning", Proc. SPIE 12839, Biomedical Vibrational Spectroscopy 2024: Advances in Research and Industry, 1283903 (12 March 2024); https://doi.org/10.1117/12.3005335

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