Development of Artificial Intelligence-Based Approaches for Computer-Aided Management of Colorectal Polyps

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Background and Objectives: Colorectal cancer (CRC) is the second leading cause of cancer death in the United States, with nearly 150,000 new cases and 50,000 deaths annually. Colonoscopy with polypectomy remains the gold standard for CRC screening and surveillance since removal of neoplastic polyps during colonoscopy modifies disease outcomes and informs subsequent management. Standard practice continues to favor removal of all visualized polyps for histopathological assessment, despite estimates that nearly half of the polyps are non-neoplastic. Studies have shown that the capability to reliably predict polyp pathology endoscopically in real time could result in substantial improvement in the cost-effectiveness of colonoscopy for CRC. The number of colonoscopies performed is increasing, and in the VA more than doubled in a five-year span. This demand does not include subsequent procedures required in ~30% of screened patients. Thus, colonoscopy can benefit greatly from efficiency improvements at every level. In light of this, the past decade has seen an explosion in advances in endoscopic technologies toward diagnosing and treating colorectal neoplasia more precisely. Recent advances in artificial intelligence (AI), specifically in the field of deep learning, and their application to endoscopic imaging, have shown promise for automating endoscopic polyp pathology predictions, overcoming operator-based polyp pathology assessment factors such as interobserver variability, skill, and experience. Such capability would finally open the door to widespread adoption of cost-saving resect-and-discard and leave-behind paradigms for diminutive polyps, as proposed by the American Society for Gastrointestinal Endoscopy Preservation and Incorporation of Valuable Endoscopic Innovations guidelines. More importantly, the incorporation of AI- based quantitative image interpretation into clinical practice, including in the VA, has the potential to increase early cancer detection thus reducing patient morbidity and mortality. To this end, the main goal of the proposed study is to leverage AI, specifically deep learning models, to develop an accurate and robust computer aided diagnosis (CADx) platform to enable the purely endoscopic, optical assessment of mucosal pathologies, specifically colorectal polyps. In parallel, the use of AI models to assess colonic mucosal and luminal features known to inform colonoscopy quality will be investigated. Methods: The study will be guided by three aims. In Aim 1 robust classification models for predicting polyp pathology will be developed. Labeled images and clinical data, from existing datasets and clinical records, will be used to design and validate deep learning models. The design will consist of two steps: outlining regions in an image containing a polyp, and subsequent analysis of the polyp region to provide a pathology prediction. Borrowing from aspects of augmented reality, the pathology prediction along with the estimated polyp boundary, will be presented to endoscopists in an intuitive and clinically friendly manner as a pseudo- color overlay, enhancing the transparency and interpretability of the models output predictions. This immediate visual feedback can thus inform clinical decisions during colonoscopy. Aim 2 will focus on using clinical risk factors associated with colorectal neoplasia in combination with endoscopic imaging data to enhance predictions of polyp pathology. The goal is to investigate incorporation of recognized CRC clinical risk factors and biomarkers, obtained from patients’ electronic health records, in our polyp pathology prediction deep learning models. Finally, in Aim 3 the deep learning-based detection, segmentation, and classification frameworks developed in Aim 1 will be adapted for scoring bowel preparation, recognizing cecal landmarks and rectal retroflexion, identifying colonic diverticula, and delineating endoscopic tattoo markings. These features are associated with performing of high-quality colonoscopy, for which automated identification could improve and facilitate documentation of endoscopic findings and report generation.