Artificial Intelligence for Real-Time Prediction of the Histology of Colorectal Polyps by General Endoscopists
2024; American College of Physicians; Volume: 177; Issue: 7 Linguagem: Inglês
10.7326/m24-0086
ISSN1539-3704
AutoresDouglas K. Rex, Indira Bhavsar-Burke, Daniel Buckles, James R. Burton, Amanda K. Cartee, Kevin M. Comar, Adam Edwards, Blair Fennimore, Monika Fischer, Mark E. Gerich, Ashley C. Gilmore, Shadi Hamdeh, J. C. Hoffman, Michael B. Ibach, Mollie Jackson, Toyia N. James-Stevenson, Tonya Kaltenbach, Jeffrey Kaplan, Saurabh Kapur, Daniel Kohm, Michael Kriss, Shanker Kundumadam, Kondal R. Kyanam Kabir Baig, Paul Menard‐Katcher, Cary Kraft, James Langworthy, Bharat Misra, Eric Molloy, Juan Carlos Muñoz, John P. Norvell, Thomas Nowak, Itegbemie Obaitan, Swati Patel, Mitesh Patel, Shajan Peter, BJ Reid, Nicholas Rogers, J Ross, James C. Ryan, Sashidhar Sagi, Akira Saito, Salih Samo, Fayez Sarkis, Frank I. Scott, Robert M. Siwiec, Shelby Sullivan, Amanda Wieland, Jianying Zhang, Alessandro Repici, Cesare Hassan, Michael F. Byrne, Amit Rastogi,
Tópico(s)Radiomics and Machine Learning in Medical Imaging
ResumoReal-time prediction of histologic features of small colorectal polyps may prevent resection and/or pathologic evaluation and therefore decrease colonoscopy costs. Previous studies showed that computer-aided diagnosis (CADx) was highly accurate, though it did not outperform expert endoscopists.
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