Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges

Area of Science:

• Biomedical Engineering
• Computational Pathology
• Musculoskeletal Research

Background:

• Histopathology is crucial for analyzing musculoskeletal tissues but is time-intensive.
• Current scoring systems create analysis bottlenecks, limiting throughput.
• Digital imaging and AI offer potential for automating histological analysis.

Purpose of the Study:

• To provide an overview of machine learning (ML) applications in musculoskeletal histopathology.
• To outline a pipeline for automated image analysis from tissue collection to model selection.
• To highlight opportunities for advanced computational pathology in musculoskeletal diseases.

Main Methods:

• Review of ML and deep learning applications in histological image analysis.
• Discussion of tissue processing, staining, and imaging optimization for generalizability.
• Analysis of computer vision advancements applicable to histopathology.

Main Results:

• Identified ML and deep learning as key technologies for automating histological analysis.
• Detailed critical optimization steps for tissue preparation and imaging.
• Highlighted the potential of state-of-the-art computational pathology over traditional methods.

Conclusions:

• Significant opportunity exists to expand ML and deep learning in musculoskeletal histopathology.
• Optimized tissue processing and imaging are vital for robust automated models.
• Computational pathology offers advanced solutions for musculoskeletal disease analysis.