Publication

AI-Driven CAD for Histological Analysis in Mountain Regions: Advancing Local Healthcare and Sustainable Development

2025

Abstract

Accurate quantification of the Ki-67 proliferation index is essential in cancer diagnostics, yet manual evaluation remains time-consuming and prone to inter-observer variability. This study presents a machine vision-based CAD tool designed to compute Ki-67 proliferation index from digitized histopathological images, aiding diagnostic workflows at the U. Parini hospital in Aosta. Developed through an interdisciplinary approach integrating artificial intelligence and computational pathology, the system was validated against expert annotations, demonstrating strong concordance and clinical reliability. Beyond its scientific contributions, this initiative fosters digital transformation in healthcare, improving diagnostic accessibility in mountainous regions while promoting local economic and technological development.

Official source

https://infoscience.epfl.ch/entities/publication/62f59bba-1598-44d0-9e92-247f4acecf76

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2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/62f59bba-1598-44d0-9e92-247f4acecf76

About this result

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