JavaCyte, a novel open-source tool for automated quantification of key hallmarks of cardiac structural remodeling

Area of Science:

• Cardiovascular research
• Histopathology
• Medical imaging analysis

Background:

• Cardiac pathologies involve structural changes often analyzed manually.
• Manual analysis is time-consuming and prone to observer bias.
• Spatial relationships in cardiac tissue structure are underexploited.

Purpose of the Study:

• To develop and validate an automated tool for cardiac histology analysis.
• To improve the efficiency and reduce bias in quantifying cardiac structural features.
• To assess the accuracy and reliability of automated analysis compared to manual methods.

Main Methods:

• Developed JavaCyte, an ImageJ-based tool for automated histological analysis.
• Included quantification of cardiomyocyte size, fibrosis, fibroblast density, and capillary characteristics.
• Validated the tool against manual quantification in goat models of atrial fibrillation and human atrial biopsies.

Main Results:

• JavaCyte accurately reproduced manual findings in goat models.
• Strong correlations observed between manual and automated measurements in human biopsies (e.g., myocyte count r=0.94, fibrosis r=0.98).
• Automated analysis showed minimal inter-observer variability (ICC=0.89) and faster throughput.

Conclusions:

• JavaCyte provides a validated, open-source solution for high-throughput cardiac histology.
• The automated tool is as accurate as manual measurements but with reduced bias and increased speed.
• This facilitates more efficient and reliable research into cardiac structural remodeling.