Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning

被引：0

作者：

Matsumoto, Yuki ^{[1
]}

Ogawa, Katsuhiro ^{[2
]}

Tamura, Kai ^{[1
]}

Yagi, Rena ^{[1
]}

Onishi, Shun ^{[3
]}

Ieiri, Satoshi ^{[3
]}

Etoh, Tsuyoshi ^{[2
]}

Inomata, Masafumi ^{[2
]}

Katagiri, Takashi ^{[1
]}

Oshima, Yusuke ^{[1
,2
]}

机构：

[1] Univ Toyama, Computat Biophoton Lab, 3190 Gofuku, Toyama, Toyama 9308555, Japan

[2] Oita Univ, Dept Gastroenterol & Pediat Surg, Fac Med, 1-1 Hasama, Yufu City, Oita 8795593, Japan

[3] Kagoshima Univ, Dept Pediat Surg, Res Field Med & Hlth Sci, Med & Dent Area,Res & Educ Assembly, 8-35-1 Sakuragaoka, Kagoshima, Kagoshima 8908544, Japan

来源：

QUANTUM TECHNOLOGY: DRIVING COMMERCIALISATION OF AN ENABLING SCIENCE III | 2022年 / 12335卷

关键词：

Raman spectroscopy; Hirschsprung's disease; enteric nervous system; machine learning;

D O I：

暂无

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this study, we aimed to develop a new optical biopsy technique for aganglionosis of Hirschsprung disease (HSCR) and we then evaluated a custom designed Raman optical biopsy system combined with deep learning based on convolutional neural networks (CNNs). Surgical specimens of formalin-fixed tissue of HSCR patients were subjected to this study. In the result, we achieved more than 90% classification accuracy between the normal and the lesion segments in mucosa. This study shows that CNN is useful for discriminating Raman spectra of the human gastrointestinal wall.

引用

页数：2

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