Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.
We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted usin...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 766 - 778 |
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| Autores principales: | , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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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=177625995&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177625995 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177625995 177625995 177625995 10.1007/s10278-023-00945-3 177625995 ppf: 766 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis. aug: au: Salehi, Mohammad Amin Mohammadi, Soheil Harandi, Hamid Zakavi, Seyed Sina Jahanshahi, Ali Shahrabi Farahani, Mohammad Wu, Jim S. affil: https://ror.org/01c4pz451 School of Medicine, Tehran University of Medical Sciences, Pour Sina St, Keshavarz Blvd, 1417613151, Tehran, Iran sug: subj: Artificial Intelligence Utilization Bone Neoplasms Diagnosis Algorithms Human Systematic Review Meta Analysis PubMed CINAHL Database Medline Prediction Models Confidence Intervals Machine Learning Descriptive Statistics Sensitivity and Specificity ab: We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted using a combination of keywords related to bone tumors and AI. After extracting contingency tables from all included studies, we performed a meta-analysis using random-effects model to determine the pooled sensitivity and specificity, accompanied by their respective 95% confidence intervals (CI). Quality assessment was evaluated using a modified version of Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST). The pooled sensitivities for AI algorithms and clinicians on internal validation test sets for detecting bone neoplasms were 84% (95% CI: 79.88) and 76% (95% CI: 64.85), and pooled specificities were 86% (95% CI: 81.90) and 64% (95% CI: 55.72), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 84% (95% CI: 75.90) and 91% (95% CI: 83.96), respectively. The same numbers for clinicians were 85% (95% CI: 73.92) and 94% (95% CI: 89.97), respectively. The sensitivity and specificity for clinicians with AI assistance were 95% (95% CI: 86.98) and 57% (95% CI: 48.66). Caution is needed when interpreting findings due to potential limitations. Further research is needed to bridge this gap in scientific understanding and promote effective implementation for medical practice advancement. 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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