作者
Thomas Schaffter, Diana SM Buist, Christoph I Lee, Yaroslav Nikulin, Dezső Ribli, Yuanfang Guan, William Lotter, Zequn Jie, Hao Du, Sijia Wang, Jiashi Feng, Mengling Feng, Hyo-Eun Kim, Francisco Albiol, Alberto Albiol, Stephen Morrell, Zbigniew Wojna, Mehmet Eren Ahsen, Umar Asif, Antonio Jimeno Yepes, Shivanthan Yohanandan, Simona Rabinovici-Cohen, Darvin Yi, Bruce Hoff, Thomas Yu, Elias Chaibub Neto, Daniel L Rubin, Peter Lindholm, Laurie R Margolies, Russell Bailey McBride, Joseph H Rothstein, Weiva Sieh, Rami Ben-Ari, Stefan Harrer, Andrew Trister, Stephen Friend, Thea Norman, Berkman Sahiner, Fredrik Strand, Justin Guinney, Gustavo Stolovitzky, Lester Mackey, Joyce Cahoon, Li Shen, Jae Ho Sohn, Hari Trivedi, Yiqiu Shen, Ljubomir Buturovic, Jose Costa Pereira, Jaime S Cardoso, Eduardo Castro, Karl Trygve Kalleberg, Obioma Pelka, Imane Nedjar, Krzysztof J Geras, Felix Nensa, Ethan Goan, Sven Koitka, Luis Caballero, David D Cox, Pavitra Krishnaswamy, Gaurav Pandey, Christoph M Friedrich, Dimitri Perrin, Clinton Fookes, Bibo Shi, Gerard Cardoso Negrie, Michael Kawczynski, Kyunghyun Cho, Can Son Khoo, Joseph Y Lo, A Gregory Sorensen, Hwejin Jung, DM DREAM Consortium
发表日期
2020/3/2
期刊
JAMA network open
卷号
3
期号
3
页码范围
e200265-e200265
出版商
American Medical Association
简介
Importance
Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives.
Objective
To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms.
Design, Setting, and Participants
In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016.
Main Outcomes and Measurements
Algorithms used images alone (challenge 1) or combined images, previous examinations (if available …
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T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli… - JAMA network open, 2020