دورية أكاديمية
أعرض في EDS

Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI.

التفاصيل البيبلوغرافية
العنوان: Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI.
المؤلفون: Lin DJ; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Schwier M; Siemens Medical Solutions USA, Princeton, NJ., Geiger B; Siemens Medical Solutions USA, Princeton, NJ., Raithel E; Siemens Healthcare GmbH, Erlangen, Germany., von Busch H; Siemens Healthcare GmbH, Erlangen, Germany., Fritz J; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Kline M; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Brooks M; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Dunham K; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Shukla M; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Alaia EF; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Samim M; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Joshi V; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Walter WR; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY., Ellermann JM; Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN., Ilaslan H; Imaging Institute, Cleveland Clinic, Cleveland, OH., Rubin D, Winalski CS; Imaging Institute, Cleveland Clinic, Cleveland, OH., Recht MP; From the Department of Radiology, NYU Grossman School of Medicine, New York, NY.
المصدر: Investigative radiology [Invest Radiol] 2023 Jun 01; Vol. 58 (6), pp. 405-412. Date of Electronic Publication: 2023 Jan 18.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0045377 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-0210 (Electronic) Linking ISSN: 00209996 NLM ISO Abbreviation: Invest Radiol Subsets: MEDLINE
أسماء مطبوعة: Publication: 1998- : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: Philadelphia.
مواضيع طبية MeSH: Rotator Cuff Injuries*/diagnostic imaging , Rotator Cuff Injuries*/pathology , Deep Learning*, Humans ; Shoulder ; Rotator Cuff/pathology ; Magnetic Resonance Imaging/methods
مستخلص: Background: Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency.
Purpose: The aim of this study was to develop a prototype DL model for detection and classification of rotator cuff tears on shoulder magnetic resonance imaging into no tear, partial-thickness tear, or full-thickness tear.
Materials and Methods: This Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included a total of 11,925 noncontrast shoulder magnetic resonance imaging scans from 2 institutions, with 11,405 for development and 520 dedicated for final testing. A DL ensemble algorithm was developed that used 4 series as input from each examination: fluid-sensitive sequences in 3 planes and a sagittal oblique T1-weighted sequence. Radiology reports served as ground truth for training with categories of no tear, partial tear, or full-thickness tear. A multireader study was conducted for the test set ground truth, which was determined by the majority vote of 3 readers per case. The ensemble comprised 4 parallel 3D ResNet50 convolutional neural network architectures trained via transfer learning and then adapted to the targeted domain. The final tear-type prediction was determined as the class with the highest probability, after averaging the class probabilities of the 4 individual models.
Results: The AUC overall for supraspinatus, infraspinatus, and subscapularis tendon tears was 0.93, 0.89, and 0.90, respectively. The model performed best for full-thickness supraspinatus, infraspinatus, and subscapularis tears with AUCs of 0.98, 0.99, and 0.95, respectively. Multisequence input demonstrated higher AUCs than single-sequence input for infraspinatus and subscapularis tendon tears, whereas coronal oblique fluid-sensitive and multisequence input showed similar AUCs for supraspinatus tendon tears. Model accuracy for tear types and overall accuracy were similar to that of the clinical readers.
Conclusions: Deep learning diagnosis of rotator cuff tears is feasible with excellent diagnostic performance, particularly for full-thickness tears, with model accuracy similar to subspecialty-trained musculoskeletal radiologists.
Competing Interests: Conflicts of interest and sources of funding: Industry collaboration with Siemens (authors' expertise as above), provision of funding for multireader study ground truth research reads.
(Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)
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تواريخ الأحداث: Date Created: 20230202 Date Completed: 20230508 Latest Revision: 20230926
رمز التحديث: 20240628
DOI: 10.1097/RLI.0000000000000951
PMID: 36728041
قاعدة البيانات: MEDLINE
الوصف
تدمد:1536-0210
DOI:10.1097/RLI.0000000000000951