Publications by authors named "S Parasa"
Article Synopsis
- AI has the potential to improve gastrointestinal endoscopy, but standardized methods are needed for its effective adoption in clinical practice.
- The QUAIDE Explanation and Checklist was created by a panel of 32 experts to provide guidelines for designing and reporting AI studies in this field.
- Consensus was achieved on 18 recommendations across key areas including data collection, outcome reporting, experimental setup, and result presentation, aiming to enhance research consistency and facilitate the use of AI in clinical settings.
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care.
View Article and Find Full Text PDFAugmenting colonoscopy with AI: insights and future directions.
Lancet Gastroenterol Hepatol
October 2024
Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations.
View Article and Find Full Text PDFBackground And Aims: The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently.
Methods: A modified Delphi process was used to develop these consensus statements.
Results: Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions.