Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph.
Objective: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear.Methods: The algorithm was developed using 6793 shoulder radiograph series performed between January 2015 and June 201...
| Publicado en: | European Radiology Vol. 30; no. 5; pp. 2843 - 2853 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142738736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142738736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: May2020 vid: 30 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142738736 142738736 NLM32025834 10.1007/s00330-019-06639-1 NLM32025834 142738736 ppf: 2843 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph. aug: au: Kim, Youngjune Choi, Dongjun Lee, Kyong Joon Kang, Yusuhn Ahn, Joong Mo Lee, Eugene Lee, Joon Woo Kang, Heung Sik affil: Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, 13620, Seongnam-si, Gyeonggi-do, South Korea sug: subj: Radiographic Image Interpretation, Computer-Assisted Methods Radiography Methods Female Male Sensitivity and Specificity Reproducibility of Results Middle Age Rotator Cuff Retrospective Design Scales Middle Aged: 45-64 years Female Male ab: Objective: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear.Methods: The algorithm was developed using 6793 shoulder radiograph series performed between January 2015 and June 2018, which were labeled based on ultrasound or MRI conducted within 90 days, and clinical information (age, sex, dominant side, history of trauma, degree of pain). The output was the probability of significant rotator cuff tear (supraspinatus/infraspinatus complex tear with > 50% of tendon thickness). An operating point corresponding to sensitivity of 98% was set to achieve high negative predictive value (NPV) and low negative likelihood ratio (LR-). The performance of the algorithm was tested with 1095 radiograph series performed between July and December 2018. Subgroup analysis using Fisher's exact test was performed to identify factors (clinical information, radiography vendor, advanced imaging modality) associated with negative test results and NPV.Results: Sensitivity, NPV, and LR- were 97.3%, 96.6%, and 0.06, respectively. The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients suspected of rotator cuff tear. The subgroup analysis showed that age < 60 years (p < 0.001), non-dominant side (p < 0.001), absence of trauma history (p = 0.001), and ultrasound examination (p < 0.001) were associated with negative test results. NPVs were higher in patients with age < 60 years (p = 0.024) and examined with ultrasound (p < 0.001).Conclusion: The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder radiographs.Key Points: • The deep learning algorithm can rule out significant rotator cuff tear with a negative likelihood ratio of 0.06 and a negative predictive value of 96.6%. • The deep learning algorithm can guide patients with significant rotator cuff tear to additional shoulder ultrasound or MRI with a sensitivity of 97.3%. • The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients with clinically suspected rotator cuff tear. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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