Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Shu Wang; Junlin Pan; Xiao Zhang; Yueying Li; Wenxi Liu; Ruolan Lin; Xingfu Wang; Deyong Kang; Zhijun Li; Feng Huang; Liangyi Chen; Jianxin Chen

doi:10.1038/s41377-024-01597-w

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Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Reviews

- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy
- Light: Science & Applications Vol. 13, Issue 12, Pages: 2887-2911(2024)
- Affiliations：
  
  1.School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
  2.Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
  3.College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
  4.Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
  5.Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
  6.Department of Pathology, Fujian Medical University Union Hospital, Fuzhou 350001, China
  7.New Cornerstone Laboratory, State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing 100091, China
- Author bio：
  
  Feng Huang (huangf@fzu.edu.cn)
  Liangyi Chen (lychen@pku.edu.cn)
  Jianxin Chen (chenjianxin@fjnu.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01597-w
  CLC：
- Received：26 February 2024，
  
  Revised：04 August 2024，
  
  Accepted：2024-08-21，
  
  Published Online：14 September 2024，
  
  Published：31 December 2024
- Accepted：
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Wang, S. et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy. Light: Science & Applications, 13, 2887-2911 (2024).
DOI：

Wang, S. et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy. Light: Science & Applications, 13, 2887-2911 (2024). DOI： 10.1038/s41377-024-01597-w.

摘要

Abstract

Diagnostic pathology

historically dependent on visual scrutiny by experts

is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements

the variability in pathologists’ subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies

there is an increasing demand for accurate pathological diagnoses. Consequently

traditional diagnostic pathology is evolving towards “next-generation diagnostic pathology”

prioritizing on the development of a multi-dimensional

intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues

multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis

holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review

we systematically outline the applications of MPM in pathological diagnosis across various human diseases

and summarize common multiphoton diagnostic features. Moreover

we examine the significant role of AI in enhancing multiphoton pathological diagnosis

including aspects such as image preprocessing

refined differential diagnosis

and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI

encompassing equipment

datasets

analytical models

and integration into the existing clinical pathways. Finally

the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks

aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

关键词

Keywords

references

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