Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms.
Ayelet Akselrod-Ballin,Michal Chorev,Yoel Shoshan,Adam Spiro,Alon Hazan,Roie Melamed,Ella Barkan,Esma Herzel,Shaked Naor,Ehud Karavani,Gideon Koren,Yaara Goldschmidt,Varda Shalev,Michal Rosen-Zvi,Michal Guindy +14 more
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TLDR
The algorithm, which combined machine-learning and deep-learning approaches, can be applied to assess breast cancer at a level comparable to radiologists and has the potential to substantially reduce missed diagnoses of breast cancer.Abstract:
Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a matter of debate. Purpose To evaluate the accuracy and efficiency of a combined machine and deep learning approach for early breast cancer detection applied to a linked set of digital mammography images and electronic health records. Materials and Methods In this retrospective study, 52 936 images were collected in 13 234 women who underwent at least one mammogram between 2013 and 2017, and who had health records for at least 1 year before undergoing mammography. The algorithm was trained on 9611 mammograms and health records of women to make two breast cancer predictions: to predict biopsy malignancy and to differentiate normal from abnormal screening examinations. The study estimated the association of features with outcomes by using t test and Fisher exact test. The model comparisons were performed with a 95% confidence interval (CI) or by using the DeLong test. Results The resulting algorithm was validated in 1055 women and tested in 2548 women (mean age, 55 years ± 10 [standard deviation]). In the test set, the algorithm identified 34 of 71 (48%) false-negative findings on mammograms. For the malignancy prediction objective, the algorithm obtained an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.93), with specificity of 77.3% (95% CI: 69.2%, 85.4%) at a sensitivity of 87%. When trained on clinical data alone, the model performed significantly better than the Gail model (AUC, 0.78 vs 0.54, respectively; P < .004). Conclusion The algorithm, which combined machine-learning and deep-learning approaches, can be applied to assess breast cancer at a level comparable to radiologists and has the potential to substantially reduce missed diagnoses of breast cancer. © RSNA, 2019 Online supplemental material is available for this article.read more
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Journal ArticleDOI
Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study
Hyo-Eun Kim,Hak Hee Kim,Boo Kyung Han,Kihwan Kim,Kyunghwa Han,Hyeonseob Nam,Eun Hye Lee,Eun Kyung Kim +7 more
TL;DR: The AI algorithm developed with large-scale mammography data showed better diagnostic performance in breast cancer detection compared with radiologists and a significant improvement in radiologists' performance when aided by AI supports application of AI to mammograms as a diagnostic support tool.
Journal ArticleDOI
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Thomas Schaffter,Diana S. M. 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,F. Albiol,Alberto Albiol,Stephen Morrell,Zbigniew Wojna,Mehmet Eren Ahsen,Umar Asif,Antonio Jimeno Yepes,Shivanthan A.C. Yohanandan,Simona Rabinovici-Cohen,Darvin Yi,Bruce Hoff,Thomas Yu,Elias Chaibub Neto,Daniel L. Rubin,Peter Lindholm,Laurie R. Margolies,Russell B. McBride,Joseph H. Rothstein,Weiva Sieh,Rami Ben-Ari,Stefan Harrer,Andrew D. Trister,Stephen H. Friend,Thea Norman,Berkman Sahiner,Fredrik Strand,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,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 +74 more
TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
Journal ArticleDOI
Precision Medicine, AI, and the Future of Personalized Health Care
Kevin B. Johnson,Wei-Qi Wei,Dilhan Weeraratne,Mark E. Frisse,Karl E. Misulis,Kyu Rhee,Juan Zhao,Jane L. Snowdon +7 more
TL;DR: Translational research exploring the convergence of artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.
Journal ArticleDOI
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
Mattie Salim,Mattie Salim,Erik Wåhlin,Karin Dembrower,Edward Azavedo,Theodoros Foukakis,Theodoros Foukakis,Yue Liu,Kevin Smith,Martin Eklund,Fredrik Strand,Fredrik Strand +11 more
TL;DR: The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials.
Journal ArticleDOI
Inconsistent Performance of Deep Learning Models on Mammogram Classification.
TL;DR: The results demonstrate performance inconsistency across the data sets and models, indicating that the high performance of deep learning models on one data set cannot be readily transferred to unseen external data sets, and these models need further assessment and validation before being applied in clinical practice.
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