@inproceedings{e7358e7634264737b681ed77ad330693,
title = "Intraoperative tissue identification for gastrointestinal disorder by Raman spectroscopy and machine learning",
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.",
keywords = "gastrointestinal surgery, Hirschsprung's disease, laparoscopy, machine learning, Raman spectroscopy",
author = "Yusuke Oshima and Katsuhiro Ogawa and Haruto Fumuro and Takashi Katagiri and Masafumi Inomata",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Biomedical Vibrational Spectroscopy 2024: Advances in Research and Industry ; Conference date: 27-01-2024 Through 28-01-2024",
year = "2024",
doi = "10.1117/12.3005335",
language = "英語",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Zhiwei Huang",
booktitle = "Biomedical Vibrational Spectroscopy 2024",
}