AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review.
Purpose: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accuracy and reliability remain challenging. Artificial...
| Publicado en: | European Journal of Trauma & Emergency Surgery Vol. 50; no. 6; pp. 2819 - 2832 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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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=181829342&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181829342 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18639933 3C05 jtl: European Journal of Trauma & Emergency Surgery issn: 18639933 maglogo: N pubinfo: dt: Dec2024 vid: 50 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181829342 178329759 181829342 181829342 10.1007/s00068-024-02557-0 181829342 ppf: 2819 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review. aug: au: Oude Nijhuis, Koen D. Dankelman, Lente H. M. Wiersma, Jort P. Barvelink, Britt IJpma, Frank F.A. Verhofstad, Michael H. J. Doornberg, Job N. Colaris, Joost W. Wijffels, Mathieu M.E. affil: https://ror.org/03cv38k47 Department of Orthopedic Surgery, Groningen, Groningen University Medical Centre, Groningen, The Netherlands sug: subj: Radius Fractures, Distal Diagnosis Radius Fractures, Distal Classification Radius Fractures, Distal Physiopathology Fracture Fixation Evaluation Artificial Intelligence Utilization Human Medline Embase Cochrane Library Quality Assessment ROC Curve Descriptive Statistics Sensitivity and Specificity Precision ab: Purpose: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs. Methods: A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS). Results: Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73–100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs. Conclusion: AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms. pubtype: Academic Journal doctype: diagnostic images research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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