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HomeRadiologyVol. 306, No. 1 PreviousNext Reviews and CommentaryEditorialDeep Learning to Detect Pancreatic Cancer at CT: Artificial Intelligence Living Up to Its HypeAlex M. Aisen , Pedro S. RodriguesAlex M. Aisen , Pedro S. RodriguesAuthor AffiliationsFrom Philips Healthcare, Cambridge, MA (A.M.A.); and Philips Healthcare, Best, the Netherlands (P.S.R.).Address correspondence to A.M.A. (email: [email protected]).Alex M. Aisen Pedro S. RodriguesPublished Online:Sep 13 2022https://doi.org/10.1148/radiol.222126MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Burns JE, Yao J, Summers RM. Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift. J Bone Miner Res 2020;35(1):28–35. Crossref, Medline, Google Scholar2. Erickson BJ. Basic Artificial Intelligence Techniques: Machine Learning and Deep Learning. Radiol Clin North Am 2021;59(6):933–940. Crossref, Medline, Google Scholar3. Chen P, Wu T, Wang P, et al. Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study. Radiology 2023;306(1):172–182. Link, Google Scholar4. Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59(6):987–1002. Crossref, Medline, Google Scholar5. Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing Machine Learning in Radiology Practice and Research. AJR Am J Roentgenol 2017;208(4):754–760. Crossref, Medline, Google Scholar6. Willemink MJ, Koszek WA, Hardell C, et al. Preparing Medical Imaging Data for Machine Learning. Radiology 2020;295(1):4–15. Link, Google Scholar7. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019;17(1):195. Crossref, Medline, Google Scholar8. Park SH, Han K. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology 2018;286(3):800–809. Link, Google Scholar9. Rao VM, Levin DC, Parker L, Cavanaugh B, Frangos AJ, Sunshine JH. How widely is computer-aided detection used in screening and diagnostic mammography? J Am Coll Radiol 2010;7(10):802–805. Crossref, Medline, Google Scholar10. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8):500–510. Crossref, Medline, Google ScholarArticle HistoryReceived: Aug 22 2022Revision requested: Aug 24 2022Revision received: Aug 25 2022Accepted: Aug 29 2022Published online: Sept 13 2022Published in print: Jan 2023 FiguresReferencesRelatedDetailsAccompanying This ArticlePancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based StudySep 13 2022RadiologyRecommended Articles Deep Learning: A Primer for RadiologistsRadioGraphics2017Volume: 37Issue: 7pp. 2113-2131Current Applications and Future Impact of Machine Learning in RadiologyRadiology2018Volume: 288Issue: 2pp. 318-328Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology In TrainingRadiology2022Volume: 304Issue: 3pp. 509-515Artificial Intelligence Outperforms Radiologists for Pancreatic Cancer Lymph Node Metastasis Prediction at CTRadiology2022Volume: 306Issue: 1pp. 170-171Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based StudyRadiology2022Volume: 306Issue: 1pp. 172-182See More RSNA Education Exhibits Artificial Intelligence for Early Detection of Pancreatic Cancer: Preliminary Observations and ChallengesDigital Posters2019A Multidisciplinary Approach for Program Development with Artificial Intelligence in Pancreatic Cancer: How We Fit InDigital Posters2019Artificial Intelligence in Radiology: A Primer for Residents and StudentsDigital Posters2019 RSNA Case Collection Primary pancreatic lymphoma RSNA Case Collection2020IgG4-Related Disease RSNA Case Collection2021Krukenberg TumorsRSNA Case Collection2021 Vol. 306, No. 1 Metrics Altmetric Score PDF download