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

被引:0
|
作者
Wang, Shu [1 ,2 ]
Pan, Junlin [1 ]
Zhang, Xiao [3 ]
Li, Yueying [1 ]
Liu, Wenxi [3 ]
Lin, Ruolan [4 ]
Wang, Xingfu [5 ]
Kang, Deyong [6 ]
Li, Zhijun [2 ]
Huang, Feng [1 ]
Chen, Liangyi [7 ]
Chen, Jianxin [2 ]
机构
[1] Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China
[2] Fujian Normal Univ, Key Lab Optoelect Sci & Technol Med, Fujian Prov Key Lab Photon Technol, Minist Educ, Fuzhou 350007, Peoples R China
[3] Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350108, Peoples R China
[4] Fujian Med Univ, Union Hosp, Dept Radiol, Fuzhou 350001, Peoples R China
[5] Fujian Med Univ, Affiliated Hosp 1, Dept Pathol, Fuzhou 350005, Peoples R China
[6] Fujian Med Univ, Union Hosp, Dept Pathol, Fuzhou 350001, Peoples R China
[7] Peking Univ, Inst Mol Med, Natl Biomed Imaging Ctr, Beijing Key Lab Cardiometab Mol Med,State Key Lab, Beijing 100091, Peoples R China
基金
中国国家自然科学基金;
关键词
CARCINOMA IN-SITU; FREE IDENTIFICATION; QUANTITATIVE-ANALYSIS; OPTICAL BIOPSY; FLUORESCENCE; COLLAGEN; TISSUE; CELL; TOMOGRAPHY; VIVO;
D O I
10.1038/s41377-024-01597-w
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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. AI-empowered multiphoton microscopy enhances diagnostic accuracy and efficiency for various human diseases, evolving towards next-generation diagnostic pathology with an endogenous, multi-dimensional, and intelligent approach.
引用
收藏
页数:25
相关论文
共 50 条
  • [1] AI-empowered next-generation multiscale climate modelling for mitigation and adaptation
    Eyring, Veronika
    Gentine, Pierre
    Camps-Valls, Gustau
    Lawrence, David M.
    Reichstein, Markus
    [J]. NATURE GEOSCIENCE, 2024,
  • [2] AI-Empowered Next Generation Consumer Internet of Things
    Herencsar, Norbert
    [J]. IEEE CONSUMER ELECTRONICS MAGAZINE, 2023, 12 (02) : 11 - 13
  • [3] Towards next-generation label-free biosensors: recent advances in whispering gallery mode sensors
    Kim, Eugene
    Baaske, Martin D.
    Vollmer, Frank
    [J]. LAB ON A CHIP, 2017, 17 (07) : 1190 - 1205
  • [4] Next-Generation Sequencing in Diagnostic Pathology
    Ilyas, Mohammad
    [J]. PATHOBIOLOGY, 2017, 84 (06) : 292 - 305
  • [5] Hybrid label-free multiphoton and optoacoustic microscopy (MPOM)
    Soliman, Dominik
    Tserevelakis, George J.
    Omar, Murad
    Ntziachristos, Vasilis
    [J]. OPTO-ACOUSTIC METHODS AND APPLICATIONS IN BIOPHOTONICS II, 2015, 9539
  • [6] Label-free imaging of cortical structures with multiphoton microscopy
    Wang, Shu
    Chen, Xiuqiang
    Wu, Weilin
    Chen, Zhida
    Lin, Ruolan
    Lin, Peihua
    Wang, Xingfu
    Fu, Yu Vincent
    Chen, Jianxin
    [J]. MULTIPHOTON MICROSCOPY IN THE BIOMEDICAL SCIENCES XVII, 2017, 10069
  • [7] Label-free imaging of Drosophila larva by multiphoton autofluorescence and second harmonic generation microscopy
    Lin, Chiao-Ying
    Hovhannisyan, Vladimir
    Wu, June-Tai
    Lin, Chii-Wann
    Chen, Jyh-Horng
    Lin, Sung-Jan
    Dong, Chen-Yuan
    [J]. JOURNAL OF BIOMEDICAL OPTICS, 2008, 13 (05)
  • [8] Towards Next-Generation Cybersecurity with Graph AI
    Bowman, Benjamin
    Howie Huang, H.
    [J]. Operating Systems Review (ACM), 2021, 55 (01): : 61 - 67
  • [9] Label-free detection of brain invasion in meningiomas by multiphoton microscopy
    Fang, Na
    Wu, Zanyi
    Cai, Shanshan
    Chen, Yupeng
    Lin, Yuanxiang
    Zheng, Xianying
    Tu, Haohua
    Qiu, Lida
    Liu, Xueyong
    Wang, Feng
    Chen, Yue
    Li, Lianhuang
    Wang, Xingfu
    Chen, Jianxin
    [J]. LASER PHYSICS LETTERS, 2019, 16 (01)
  • [10] Optical Visualization of Cerebral Cortex by Label-Free Multiphoton Microscopy
    Wang, Shu
    Wang, Feng
    Qiu, Lida
    Ma, Yukun
    Lin, Ruolan
    Chen, Yue
    Chen, Fuxiang
    Lin, Yuanxiang
    Tu, Haohua
    Wang, Xingfu
    Chen, Jianxin
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, 2019, 25 (01)