Artificial intelligence and machine learning algorithms generate highly accurate and precise computer vision assessments of digitized pathology slides.

Classical approaches to disease staging in neuropathology have limited quantitative reproducibility. As such, there is a critical need for unbiased methods to quantitatively analyse pathological burden on the histological level. Artificial intelligence and machine learning algorithms generate highly accurate and precise computer vision assessments of digitized pathology slides, yielding novel histology metrics at scale. These quantitative metrics can be leveraged in genomic and transcriptomic studies to elucidate mechanisms driving neurodegeneration, as well as predict antemortem symptomology.