Danielle Guerrero

Hoa Hoang Ngoc Pham, et al, The American Journal of Pathology, 2020
Pham and colleagues set out to address high false positivity of lymph nodes metastasis analysis using deep learning. As characterizing lymph node metastases in breast and lung cancer is of great clinical importance for treatment selection and prognosis, finding a method with high sensitivity and specificity would represent a major advance. Here, the researchers demonstrate a two-step approach with HALO AI where the first deep learning algorithm excludes the lymph germinal centers that are the source of false positivity and the second algorithm detects tumor cells. The researchers demonstrate this method on lung cancer lymph tissue and find a sensitivity ~78% and specificity ~97% and conclude that a two-step approach can successfully be used to detect lung cancer metastases to the lymph nodes with high specificity. Future research may target development of an algorithm or algorithms with increased sensitivity that maintain high specificity.

David R. Martin, et al, Archives of Pathology & Laboratory Medicine, 2019
Researchers from the University of New Mexico set out to investigate tissue classification using deep learning to evaluate nonneoplastic gastric biopsies. Ground truth diagnosis was established by gastrointestinal pathologists. HALO AI was trained to recognize Helicobacter pylori (H pylori) mediated gastritis, chemical gastropathy, normal mucosa, smooth muscle, and glass. The HALO AI classifier showed high sensitivity and specificity for control biopsies and gastropathy cases and represents the first deep learning driven evaluation of inflammatory gastrointestinal pathology published. The sensitivity and specificity was as follows: normal tissue (73.7% and 79.6%), H pylori (95.7% and 100%), and reactive gastropathy (100% and 62.5%). Martin and colleagues conclude that a convolutional neural network such as HALO AI can function as a screening aid for H pylori gastritis.