Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.
Objectives: Pediatric elbow fractures are a common injury among children. Recent advancements in artificial intelligence (AI), particularly deep learning (DL), have shown promise in diagnosing these fractures. This study systematically evaluated the performance of DL models in detecting pediatric el...
| Published in: | European Journal of Trauma & Emergency Surgery Vol. 51; no. 1; pp. 1 - 12 |
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| Main Authors: | , , , , , , , |
| Format: | research systematic review tables/charts Journal Article |
| Published: |
Springer Nature
2/20/2025
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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=183175825&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183175825 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: 2/20/2025 vid: 51 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183175825 183175825 183175825 10.1007/s00068-025-02779-w 183175825 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis. aug: au: Binh, Le Nguyen Nhu, Nguyen Thanh Nhi, Pham Thi Uyen Son, Do Le Hoang Bach, Nguyen Huy, Hoang Quoc Le, Nguyen Quoc Khanh Kang, Jiunn-Horng affil: https://ror.org/05031qk94 College of Medicine, Taipei Medical University, 11031, Taipei, Taiwan sug: subj: Elbow Fractures Diagnosis Deep Learning Pediatric Care Sensitivity and Specificity Human Systematic Review Meta Analysis PubMed Medline Embase Descriptive Statistics Confidence Intervals Diagnostic Imaging Neural Networks (Computer) Child Child: 6-12 years ab: Objectives: Pediatric elbow fractures are a common injury among children. Recent advancements in artificial intelligence (AI), particularly deep learning (DL), have shown promise in diagnosing these fractures. This study systematically evaluated the performance of DL models in detecting pediatric elbow fractures. Materials and methods: A comprehensive search was conducted in PubMed (Medline), EMBASE, and IEEE Xplore for studies published up to October 20, 2023. Studies employing DL models for detecting elbow fractures in patients aged 0 to 16 years were included. Key performance metrics, including sensitivity, specificity, and area under the curve (AUC), were extracted. The study was registered in PROSPERO (ID: CRD42023470558). Results: The search identified 22 studies, of which six met the inclusion criteria for the meta-analysis. The pooled sensitivity of DL models for pediatric elbow fracture detection was 0.93 (95% CI: 0.91–0.96). Specificity values ranged from 0.84 to 0.92 across studies, with a pooled estimate of 0.89 (95% CI: 0.85–0.92). The AUC ranged from 0.91 to 0.99, with a pooled estimate of 0.95 (95% CI: 0.93–0.97). Further analysis highlighted the impact of preprocessing techniques and the choice of model backbone architecture on performance. Conclusion: DL models demonstrate exceptional accuracy in detecting pediatric elbow fractures. For optimal performance, we recommend leveraging backbone architectures like ResNet, combined with manual preprocessing supervised by radiology and orthopedic experts. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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