Artificial Intelligence–Assisted Quantitative Histology for Early Detection of Cellular Damage: A Multimodal Morphometric Study
Keywords:
artificial intelligence, histology, cellular injury, digital pathology, morphometry, apoptosis, oxidative stressAbstract
The integration of artificial intelligence (AI) into histopathology represents a rapidly advancing frontier in medical biology. This study evaluates the diagnostic accuracy of AI-assisted quantitative morphometric analysis in detecting early cellular damage in comparison with conventional light microscopy. A total of 180 histological samples subjected to controlled oxidative and inflammatory injury were analyzed using both traditional assessment and deep learning–based digital image segmentation. Quantitative parameters including nuclear-cytoplasmic ratio, chromatin condensation index, mitochondrial density, cytoplasmic vacuolization, and apoptotic body count were measured. AI-based analysis demonstrated significantly higher sensitivity in detecting subcellular alterations (94.6%) compared to conventional microscopy (81.3%, p < 0.01). Morphometric data revealed distinct distribution patterns between reversible and irreversible cellular injury. A double pie chart analysis illustrated the proportional differences in histological features following early and advanced cellular damage. The findings support AI-driven histological quantification as a powerful adjunct tool for improving early detection and reproducibility in diagnostic histology and medical education.
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