A machine learning approach toward automating spatial identification of LAG3+/CD3+ cells in ulcerative colitis.

ABSTRACT: Over the past decade, automation of digital image analysis has become commonplace in both research and clinical settings. Spurred by recent advances in artificial intelligence and machine learning (AI/ML), tissue sub-compartments and cellular phenotypes within those compartments can be identified with higher throughput and accuracy than ever before. Recently, immune checkpoints have emerged as potential targets for auto-immune diseases. As such, spatial identification of these proteins along with immune cell markers (e.g., CD3⁺/LAG3⁺ T-cells) is a crucial step in understanding the potential and/or efficacy of such treatments. Here, we describe a semi-automated imaging and analysis pipeline that identifies CD3⁺/LAG3⁺ cells in colorectal tissue sub

SUBMITTER: Bonnevie ED 

PROVIDER: S-EPMC10709428 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature