Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists.

Context.—: Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice.

Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope.

Several machine learning algorithms have demonstrated high predictive capability in the identification of cancer within digitized pathology slides. The Augmented Reality Microscope (ARM) has allowed these algorithms to be seamlessly integrated within the pathology workflow by overlaying their inferences onto its microscopic field of view in real time. We present an independent assessment of the LYmph Node Assistant (LYNA) models, state-of-the-art algorithms for the identification of breast cancer metastases in lymph node biopsies, optimized for usage on the ARM.

Accelerated Progression of Disseminated Coccidioidomycosis Following SARS-CoV-2 Infection: A Case Report.

We describe a patient with subclinical coccidioidomycosis who experienced rapid disease dissemination shortly after SARS-CoV-2 infection, suggesting host immune response dysregulation to coccidioidomycosis by SARS-CoV-2. We hypothesize that disrupted cell-mediated signaling may result after SARS-CoV-2 infection leading to functional exhaustion and CD8+ T-cell senescence with impairment in host cellular response to Coccidioides infection.

Introduction to Artificial Intelligence and Machine Learning for Pathology.

Context.—: Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data.