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Purpose <p>Hirschsprung’s disease (HSCR) is an intestinal disorder characterized by the absence of nerve cells in parts of the intestinal tract. The definitive diagnosis is confirmed by a full-thickness rectal biopsy to verify the absence of ganglion cells. However, incomplete removal often causes post-operative complications. To establish an optical biopsy technique for targeting mucosa with aganglionosis of HSCR and to confirm its capability by another optical imaging modality and histopathology.</p> Methods <p>Raman spectroscopy (RS) is an emerging technique in tissue diagnosis without staining that makes it possible to support conventional diagnostics and therapeutics for achieving more precise outcomes in HSCR. We demonstrate the proof-of-concept for label-free detection of the aganglionic segment in HSCR based on an RS technique in combination with fine-tuned machine learning algorithms.</p> Results <p>RS distinguished the characteristics of aganglionic segments in the mucosal surface of the lesion. The altered morphology was confirmed by multiphoton microscopy. In addition, discrimination models were built and evaluated by convolutional neural networks and the decision tree combined with gradient boosting framework.</p> Conclusion <p>The proposed method and model show a high accuracy above 90% and a pseudo-blind examination involving three HSCR patients implies the feasibility for clinical application. (195 words)</p>

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Label-free diagnostic procedure for hirschsprung’s disease to detect intestinal mucosal characteristics of aganglionosis by Raman spectroscopy with optimized decision algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月29日

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  • Yusuke Oshima,
  • Yuki Matsumoto,
  • Katsuhiro Ogawa,
  • Kai Tamura,
  • Rena Yagi,
  • Noritaka Fujisawa,
  • Takashi Katagiri,
  • Shun Onishi,
  • Hidefumi Shiroshita,
  • Tsuyoshi Etho,
  • Tsutomu Daa,
  • Satoshi Ieiri,
  • Masafumi Inomata

摘要

Purpose

Hirschsprung’s disease (HSCR) is an intestinal disorder characterized by the absence of nerve cells in parts of the intestinal tract. The definitive diagnosis is confirmed by a full-thickness rectal biopsy to verify the absence of ganglion cells. However, incomplete removal often causes post-operative complications. To establish an optical biopsy technique for targeting mucosa with aganglionosis of HSCR and to confirm its capability by another optical imaging modality and histopathology.

Methods

Raman spectroscopy (RS) is an emerging technique in tissue diagnosis without staining that makes it possible to support conventional diagnostics and therapeutics for achieving more precise outcomes in HSCR. We demonstrate the proof-of-concept for label-free detection of the aganglionic segment in HSCR based on an RS technique in combination with fine-tuned machine learning algorithms.

Results

RS distinguished the characteristics of aganglionic segments in the mucosal surface of the lesion. The altered morphology was confirmed by multiphoton microscopy. In addition, discrimination models were built and evaluated by convolutional neural networks and the decision tree combined with gradient boosting framework.

Conclusion

The proposed method and model show a high accuracy above 90% and a pseudo-blind examination involving three HSCR patients implies the feasibility for clinical application. (195 words)