Digital Histopathological Discrimination of Label-Free Tumoral Tissues by Artificial Intelligence Phase-Imaging...

Area of Science:

• Digital pathology
• Biomedical imaging
• Artificial intelligence in medicine

Background:

• Histopathology is the standard for disease diagnosis but involves time-consuming fixation and staining.
• Digital histology on fresh, label-free samples can reduce processing time and artifacts, improving diagnostic speed and patient prognosis.

Purpose of the Study:

• To propose, implement, and validate a novel digital diagnosis procedure for fresh, label-free histological samples.
• To leverage advanced phase-imaging microscopy parameters and artificial intelligence for automated disease diagnosis.

Main Methods:

• Collected and imaged fresh human histological samples (liver, kidney, ganglion, testicle, brain) using phase-imaging microscopy.
• Calculated advanced phase parameters and evaluated their statistical significance at 10×, 20×, and 40× magnifications.
• Applied and evaluated various artificial intelligence classification algorithms, including Artificial Neural Networks and Decision Trees.

Main Results:

• Artificial Neural Network and Decision Tree algorithms demonstrated high performance, achieving over 90% sensitivity and specificity for most tissues at certain magnifications.
• Statistical significance of phase parameters was evaluated across different tissue types and magnifications.
• The developed procedure showed potential for accurate, automated diagnosis of fresh histological samples.

Conclusions:

• The novel digital diagnosis procedure shows significant potential for label-free, automated diagnosis of fresh histological samples.
• This AI-driven phase-imaging microscopy approach can complement existing clinical histopathology workflows.
• Faster diagnosis through this method could positively impact patient outcomes.