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

Area of Science:

• Pathology
• Biomedical Imaging
• Artificial Intelligence

Background:

• Traditional pathology relies on expert visual interpretation, facing challenges with subjective variability.
• Digital pathology and AI are advancing disease detection, but diagnostic consistency remains a goal.
• Precision cancer therapies require highly accurate pathological diagnoses.

Purpose of the Study:

• To review the applications of multiphoton microscopy (MPM) in pathological diagnosis.
• To summarize multiphoton diagnostic features and the role of AI in enhancing diagnosis.
• To explore the integration challenges and future perspectives of AI-empowered MPM.

Main Methods:

• Utilizing nonlinear optical effects for label-free, high-resolution imaging of biological tissues with MPM.
• Applying AI for image preprocessing, differential diagnosis, and outcome prognostication in MPM data.
• Systematically reviewing MPM applications across various human diseases.

Main Results:

• MPM enables label-free imaging of intrinsic tissue components for pathological analysis.
• AI significantly improves the accuracy and efficiency of MPM-based pathological diagnosis.
• Common multiphoton diagnostic features have been identified across diseases.

Conclusions:

• AI-empowered MPM presents a promising auxiliary diagnostic method for pathology.
• Synergy between AI and label-free MPM can create novel diagnostic frameworks.
• Accelerating the clinical adoption of intelligent multiphoton pathology systems is a key future direction.