Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference.
Objective: To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears.Materials and Methods: One hundred consecutive patients were retrospectively included, who underwent knee MRI and knee arthroscopy in our institution. All MR...
| Published in: | Skeletal Radiology Vol. 49; no. 8; pp. 1207 - 1218 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Aug2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143819590&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143819590 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03642348 O14 jtl: Skeletal Radiology issn: 03642348 maglogo: N pubinfo: dt: Aug2020 vid: 49 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143819590 143819590 143972300 NLM32170334 143819590 10.1007/s00256-020-03410-2 NLM32170334 143819590 ppf: 1207 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. aug: au: Fritz, Benjamin Marbach, Giuseppe Civardi, Francesco Fucentese, Sandro F. Pfirrmann, Christian W.A. affil: Department of Radiology, Balgrist University Hospital, Forchstrasse 340, CH-8008, Zurich, Switzerland sug: subj: Male Middle Age Arthroscopy Female Adult Aged Adolescence Weights and Measures Clinical Competence Human Magnetic Resonance Imaging Retrospective Design Sensitivity and Specificity Comparative Studies Multicenter Studies Evaluation Research Validation Studies Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years Adolescent: 13-18 years Male Female ab: Objective: To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears.Materials and Methods: One hundred consecutive patients were retrospectively included, who underwent knee MRI and knee arthroscopy in our institution. All MRI were evaluated for medial and lateral meniscus tears by two musculoskeletal radiologists independently and by DCNN. Included patients were not part of the training set of the DCNN. Surgical reports served as the standard of reference. Statistics included sensitivity, specificity, accuracy, ROC curve analysis, and kappa statistics.Results: Fifty-seven percent (57/100) of patients had a tear of the medial and 24% (24/100) of the lateral meniscus, including 12% (12/100) with a tear of both menisci. For medial meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 93%, 91%, and 92%, for reader 2: 96%, 86%, and 92%, and for the DCNN: 84%, 88%, and 86%. For lateral meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 71%, 95%, and 89%, for reader 2: 67%, 99%, and 91%, and for the DCNN: 58%, 92%, and 84%. Sensitivity for medial meniscus tears was significantly different between reader 2 and the DCNN (p = 0.039), and no significant differences existed for all other comparisons (all p ≥ 0.092). The AUC-ROC of the DCNN was 0.882, 0.781, and 0.961 for detection of medial, lateral, and overall meniscus tear. Inter-reader agreement was very good for the medial (kappa = 0.876) and good for the lateral meniscus (kappa = 0.741).Conclusion: DCNN-based meniscus tear detection can be performed in a fully automated manner with a similar specificity but a lower sensitivity in comparison with musculoskeletal radiologists. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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