Digital Histopathology by Infrared Spectroscopic Imaging

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Pathology

Background:

• Histopathology currently relies on manual examination of stained tissue morphology.
• Infrared (IR) spectroscopic imaging provides molecular and chemical information about tissues.
• Integrating advanced imaging with AI can revolutionize diagnostic pathology.

Purpose of the Study:

• To review IR spectroscopic imaging instrumentation and trends for histopathology.
• To provide an overview of data processing, machine learning, and deep learning applications.
• To propose standardized guidelines for IR spectroscopic imaging in histopathology.

Main Methods:

• Summary of IR imaging instrumentation and performance analyses.
• Overview of data processing techniques and machine learning, including deep learning.
• Discussion of pathology workflows and proposed categorization based on complexity and analytical needs.

Main Results:

• Identification of key trends in IR imaging for histopathology.
• Exploration of AI and deep learning for analyzing spectroscopic data.
• Proposal of a framework for experimental and analytical specifications.

Conclusions:

• IR spectroscopic imaging coupled with AI holds significant potential to transform histopathology.
• Standardized guidelines are needed to advance the field of spectroscopic imaging in pathology.
• This review provides a foundation for future research and clinical adoption.