Several artificial intelligence (AI) technologies are emerging that will change the management of gastrointestinal (GI) diseases sooner rather than later. One of the leading researchers working toward that AI-driven future is Ryan W. Stidham, MD, MS, AGAF, associate professor of gastroenterology and computational medicine and bioinformatics at the University of Michigan, Ann Arbor.
Stidham’s work focuses on leveraging AI to develop automated systems that better quantify disease activity and aid gastroenterologists in their decision-making. He also serves as a meber of AGA's AI Task Force. what the technology may do to improve physician efficiency, and why gastroenterologists shouldn’t be worried about being replaced by machines any time soon.
How did you first become involved in studying AI applications for GI conditions?
My medical training coincided with the emergence of electronic health records (EHRs) making enormous amounts of data, ranging from laboratory results to diagnostic codes and billing records, readily accessible.
Leisa Thompson
Dr. Ryan W. Stidham
I quickly contracted data analytics fever, but a major problem became apparent: EHRs and medical claims data alone only weakly describe a patient. Researchers in the field were excited to use machine learning for personalizing treatment decisions for GI conditions, including inflammatory bowel disease (IBD). But no matter how large the dataset, the EHRs lacked the most rudimentary descriptions: What was the patient’s IBD phenotype? Where exactly was the disease located?
I could see machine learning had the potential to learn and reproduce expert decision-making. Unfortunately, we were fueling this machine-learning rocket ship with crude data unlikely to take us very far. Gastroenterologists rely on data in progress notes, emails, interpretations of colonoscopies, and radiologists’ and pathologists’ reviews of imaging to make treatment decisions, but that information is not well organized in any dataset.
I wanted to use AI to retrieve that key information in text, images, and video that we use every day for IBD care, automatically interpreting the data like a seasoned gastroenterologist. Generating higher-quality data describing patients could take our AI models from interesting research to useful and reliable tools in clinical care.
How did your early research go about trying to solve that problem?
My GI career began amid the IBD field shifting from relying on symptoms alone to objective biomarkers for IBD assessment, particularly focusing on standardized scoring of endoscopic mucosal inflammation. However, these scores were challenged with interobserver variability, prompting the need for centralized reading. More importantly, these scores are qualitative and do not capture all the visual findings an experienced physician appreciates when assessing severity, phenotype, and therapeutic effect. As a result, even experts could disagree on the degree of endoscopic severity, and patients with obvious differences in the appearance of mucosa could have the same endoscopic score.
I asked myself: Are we really using these measures to make treatment decisions and determine the effectiveness of investigational therapies? I thought we could do better and aimed to improve endoscopic IBD assessments using then-emerging digital image analysis techniques.
Convolutional neural network (CNN) modeling was just becoming feasible as computing performance increased. CNNs are well suited for complex medical image interpretation, using an associated “label,” such as the presence or grade of disease, to decipher the complex set of image feature patterns characterizing an expert’s determination of disease severity.
