دورية أكاديمية
أعرض في EDS

Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

التفاصيل البيبلوغرافية
العنوان: Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.
المؤلفون: Ortiz A; AI for Good Research Lab, Microsoft, Seattle, WA, USA., Trivedi A; AI for Good Research Lab, Microsoft, Seattle, WA, USA., Desbiens J; Intelligent Retinal Imaging Systems, Pensacola, FL, USA., Blazes M; Department of Ophthalmology, University of Washington, Seattle, WA, USA., Robinson C; AI for Good Research Lab, Microsoft, Seattle, WA, USA., Gupta S; Intelligent Retinal Imaging Systems, Pensacola, FL, USA., Dodhia R; AI for Good Research Lab, Microsoft, Seattle, WA, USA., Bhatraju PK; Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, USA., Liles WC; Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, USA., Lee A; Department of Ophthalmology, University of Washington, Seattle, WA, USA. leeay@uw.edu., Ferres JML; AI for Good Research Lab, Microsoft, Seattle, WA, USA. jlavista@microsoft.com.
المصدر: Scientific reports [Sci Rep] 2022 Feb 02; Vol. 12 (1), pp. 1716. Date of Electronic Publication: 2022 Feb 02.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Deep Learning* , SARS-CoV-2* , Tomography, X-Ray Computed*/methods , Tomography, X-Ray Computed*/standards, COVID-19/*diagnosis , COVID-19/*virology , Thorax/*diagnostic imaging , Thorax/*pathology, Algorithms ; COVID-19/mortality ; Databases, Genetic ; Humans ; Image Interpretation, Computer-Assisted/methods ; Image Processing, Computer-Assisted/methods ; Prognosis
مستخلص: The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.
(© 2022. The Author(s).)
References: J Digit Imaging. 2013 Dec;26(6):1045-57. (PMID: 23884657)
Radiology. 2020 Aug;296(2):E65-E71. (PMID: 32191588)
Magn Reson Imaging. 2012 Nov;30(9):1234-48. (PMID: 22898692)
Radiol Cardiothorac Imaging. 2020 Feb 13;2(1):e200034. (PMID: 33778547)
Cell. 2020 Jun 11;181(6):1423-1433.e11. (PMID: 32416069)
BMJ. 2020 Apr 7;369:m1328. (PMID: 32265220)
Eur Respir J. 2020 Aug 6;56(2):. (PMID: 32444412)
IEEE Rev Biomed Eng. 2021;14:16-29. (PMID: 32356760)
Front Bioeng Biotechnol. 2020 Jul 31;8:898. (PMID: 32850746)
Lancet Infect Dis. 2020 Apr;20(4):384-385. (PMID: 32105641)
Lancet Infect Dis. 2020 Apr;20(4):425-434. (PMID: 32105637)
Radiology. 2020 Jun;295(3):200463. (PMID: 32077789)
IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651. (PMID: 27244717)
Radiol Artif Intell. 2020 Mar 25;2(2):e200029. (PMID: 33937821)
PLoS One. 2020 Mar 19;15(3):e0230548. (PMID: 32191764)
Appl Comput Harmon Anal. 2013 May 1;34(3):469-487. (PMID: 23543920)
IEEE Rev Biomed Eng. 2021;14:4-15. (PMID: 32305937)
Radiol Artif Intell. 2020 Jul 29;2(4):e200048. (PMID: 33928255)
Ann Transl Med. 2020 Jul;8(14):859. (PMID: 32793703)
معلومات مُعتمدة: K23 DK116967 United States DK NIDDK NIH HHS; K23 EY029246 United States EY NEI NIH HHS
تواريخ الأحداث: Date Created: 20220203 Date Completed: 20220208 Latest Revision: 20240822
رمز التحديث: 20240822
مُعرف محوري في PubMed: PMC8810911
DOI: 10.1038/s41598-022-05532-0
PMID: 35110593
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-022-05532-0