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241 results on '"Charles E. Kahn"'

1. Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis

Author

Isabel Song, Elizabeth W. Thompson, Anurag Verma, Matthew T. MacLean, Jeffrey Duda, Ameena Elahi, Richard Tran, Pavan Raghupathy, Sophia Swago, Mohamad Hazim, Abhijit Bhattaru, Carolin Schneider, Marijana Vujkovic, Drew A. Torigian, Charles E. Kahn, James C. Gee, Arijitt Borthakur, Colleen M. Kripke, Christopher C. Carson, Rotonya Carr, Qasim Jehangir, Yi-An Ko, Harold Litt, Mark Rosen, David A. Mankoff, Mitchell D. Schnall, Haochang Shou, Julio Chirinos, Scott M. Damrauer, Marina Serper, Jinbo Chen, Daniel J. Rader, Penn Medicine BioBank, Walter R. T. Witschey, and Hersh Sagreiya

Subjects
Medicine, Science
Abstract

Abstract The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ − 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients—lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p

Published
2024
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2. Artificial intelligence and machine learning in cancer imaging

Author

Dow-Mu Koh, Nickolas Papanikolaou, Ulrich Bick, Rowland Illing, Charles E. Kahn, Jayshree Kalpathi-Cramer, Celso Matos, Luis Martí-Bonmatí, Anne Miles, Seong Ki Mun, Sandy Napel, Andrea Rockall, Evis Sala, Nicola Strickland, and Fred Prior

Subjects
Medicine
Abstract

Koh, Papanikolaou et al. discuss the application of artificial intelligence in cancer imaging. The authors highlight opportunities for exploiting machine learning algorithms in this field, and outline barriers in their implementation and how these might be addressed.

Published
2022
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12. Understanding metric-related pitfalls in image analysis validation.

Author

Annika Reinke, Minu Tizabi, Michael Baumgartner 0001, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Ación, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew B. Blaschko, Florian Büttner 0001, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen 0001, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase 0001, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek 0001, Anna Kreshuk, Tahsin M. Kurç, Bennett A. Landman, Geert Litjens 0001, Amin Madani, Klaus H. Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern H. Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir M. Rajpoot, Mauricio Reyes 0001, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, and Lena Maier-Hein

Published
2023
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19. Metrics reloaded: Pitfalls and recommendations for image analysis validation.

Author

Lena Maier-Hein, Annika Reinke, Evangelia Christodoulou, Ben Glocker, Patrick Godau, Fabian Isensee, Jens Kleesiek, Michal Kozubek 0001, Mauricio Reyes 0001, Michael A. Riegler, Manuel Wiesenfarth, Michael Baumgartner 0001, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Minu Dietlinde Tizabi, Laura Ación, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Annette Kopp-Schneider, Anna Kreshuk, Tahsin M. Kurç, Bennett A. Landman, Geert Litjens 0001, Amin Madani, Klaus H. Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern H. Menze, David Moher, Karel G. M. Moons, Henning Müller, Felix Nickel, Brennan Nichyporuk, Jens Petersen, Nasir M. Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clarisa Sánchez Gutiérrez, Shravya Shetty, Maarten van Smeden, Carole H. Sudre, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, and Paul F. Jäger

Published
2022
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22. Common Limitations of Image Processing Metrics: A Picture Story.

Author

Annika Reinke, Matthias Eisenmann, Minu Dietlinde Tizabi, Carole H. Sudre, Tim Rädsch, Michela Antonelli, Tal Arbel, Spyridon Bakas, M. Jorge Cardoso, Veronika Cheplygina, Keyvan Farahani, Ben Glocker, Doreen Heckmann-Nötzel, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Jens Kleesiek, Tahsin M. Kurç, Michal Kozubek 0001, Bennett A. Landman, Geert Litjens 0001, Klaus H. Maier-Hein, Bjoern H. Menze, Henning Müller, Jens Petersen, Mauricio Reyes 0001, Nicola Rieke, Bram Stieltjes, Ronald M. Summers, Sotirios A. Tsaftaris, Bram van Ginneken, Annette Kopp-Schneider, Paul Jäger 0002, and Lena Maier-Hein

Published
2021

40. Artificial Intelligence and Radiology Education

Author

Ali S. Tejani, Hesham Elhalawani, Linda Moy, Marc Kohli, and Charles E. Kahn

Subjects
Quality Education, Good Health and Well Being, Radiological and Ultrasound Technology, Medical Education, Artificial Intelligence, Imaging Informatics, Impact of AI on Education, Use of AI in Education, Biomedical Imaging, Radiology, Nuclear Medicine and imaging, Precision Education, Special Report, Natural Language Processing
Abstract

Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education.

Published
2023

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