Browsing by Author "Pandolfino, John E."

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    American Neurogastroenterology and Motility Society (ANMS) Task Force Recommendations for Resumption of Motility Laboratory Operations During the COVID-19 Pandemic
    (American Neurogastro­enterology and Motility Society (ANMS), 2020-05-17) Baker, Jason R.; Moshiree, Baha; Rao, Satish; Neshatian, Leila; Nguyen, Linda; Chey, William D.; Saad, Richard; Garza, Jose; Waseem, Shamaila; Khan, Abraham R.; Pandolfino, John E.; Gyawali, C. Prakash; Department of Pediatrics, IU School of Medicine
    The ANMS organized a Task Force for developing guidance strategies regarding re-opening of motility laboratories. This document describes how to stratify urgency of motility physiologic procedures, screen prior to the procedures, optimize personal protective equipment (PPE) utilization, clean and prepare the motility laboratory space during the COVID-19 pandemic.
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    Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation
    (Thieme, 2024-07-03) Wittbrodt, Matthew; Klug, Matthew; Etemadi, Mozziyar; Yang, Anthony; Pandolfino, John E.; Keswani, Rajesh N.; Surgery, School of Medicine
    Background and study aims: Low-quality colonoscopy increases cancer risk but measuring quality remains challenging. We developed an automated, interactive assessment of colonoscopy quality (AI-CQ) using machine learning (ML). Methods: Based on quality guidelines, metrics selected for AI development included insertion time (IT), withdrawal time (WT), polyp detection rate (PDR), and polyps per colonoscopy (PPC). Two novel metrics were also developed: HQ-WT (time during withdrawal with clear image) and WT-PT (withdrawal time subtracting polypectomy time). The model was pre-trained using a self-supervised vision transformer on unlabeled colonoscopy images and then finetuned for multi-label classification on another mutually exclusive colonoscopy image dataset. A timeline of video predictions and metric calculations were presented to clinicians in addition to the raw video using a web-based application. The model was externally validated using 50 colonoscopies at a second hospital. Results: The AI-CQ accuracy to identify cecal intubation was 88%. IT ( P = 0.99) and WT ( P = 0.99) were highly correlated between manual and AI-CQ measurements with a median difference of 1.5 seconds and 4.5 seconds, respectively. AI-CQ PDR did not significantly differ from manual PDR (47.6% versus 45.5%, P = 0.66). Retroflexion was correctly identified in 95.2% and number of right colon evaluations in 100% of colonoscopies. HQ-WT was 45.9% of, and significantly correlated with ( P = 0.85) WT time. Conclusions: An interactive AI assessment of colonoscopy skill can automatically assess quality. We propose that this tool can be utilized to rapidly identify and train providers in need of remediation.
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    Response to Richter and Vaezi
    (Wolters Kluwer, 2021-01) Gyawali, C. Prakash; Baker, Jason R.; Moshiree, Baha; Rao, Satish; Neshatian, Leila; Nguyen, Linda; Chey, William D.; Saad, Richard; Garza, Jose M.; Waseem, Shamaila; Khan, Abraham R.; Pandolfino, John E.; Pediatrics, School of Medicine