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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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.Al-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 Al 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.

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

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School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China

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

College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China

Department of Radiology,Fujian Medical University Union Hospital,Fuzhou 350001,China

Department of Pathology,The First Affiliated Hospital of Fujian Medical University,Fuzhou 350005,China

Department of Pathology,Fujian Medical University Union Hospital,Fuzhou 350001,China

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

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2024

10.1038/s41377-024-01597-w

光:科学与应用(英文版)
中国科学院长春光学精密机械与物理研究所

光:科学与应用(英文版)

CSTPCD
ISSN：2095-5545
年,卷(期)：2024.13(12)