Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Background/Objectives: Histopathological diagnosis of invasive carcinoma breast samples includes the scoring of mitotic activity. This is a tedious and time-consuming task with high interpathologist variability. Methods: As an assistance to pathologists, we developed a deep learning based pipeline for mitosis detection and mitotic scoring according to the Elston and Ellis grading system on Whole Slide Images (WSI) for the first time here described. Results: We present its performance on routine data through a clinical study which clearly demonstrates its value. When assisted by Artificial Intelligence (AI), pathologists show better accuracy and reproducibility on the mitotic score. Conclusions: To the best of our knowledge, this is the first study to demonstrate that AI can successfully assist pathologists for mitotic score determination in human breast WSI in routine practice.

Article activity feed