Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases
Jul 1, 2024·,
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0 min read
Juan A. Retamero
Ege Gulturk
Alican Bozkurt
Siqi Liu
Mirela Gorgan
Lorena Moral
Mark Horton
Matthew Hanna
Philippe Mathieu
Brandon Rothrock
Thomas J. Fuchs
Abstract
Lymph node evaluation for the presence of breast cancer metastases is a critical component of breast cancer staging and treatment planning. Artificial intelligence tools for computational pathology have shown promise in aiding pathologists with this task. In this study, we evaluated whether an AI-based decision support system could help pathologists improve both their diagnostic accuracy and efficiency in detecting breast cancer lymph node metastases. Our results show that pathologists assisted by the AI system demonstrated improvements in sensitivity and overall accuracy, while also completing the task more efficiently.
Type
Publication
In The American Journal of Surgical Pathology 48(7), 846–854 (2024)
Authors
AI Scientist
I am an AI Scientist at Paige AI. I did my Ph.D. with Jennifer Dy, Dana Brooks, and Jan-Willem van de Meent at Northeastern University. My main research interests are machine learning with emphasis on probabilistic programming, deep neural networks, and their applications in biomedical image processing. I am one of the developers of Probabilistic Torch, a library for deep generative models that extends PyTorch. I am also one of the maintainers of the PyTorch distributions module.