Diagnostic performance of magnetic resonance imaging–based machine learning in Alzheimer's disease detection: a meta-analysis.
Purpose: Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from norma...
| Publicado en: | Neuroradiology Vol. 65; no. 3; pp. 513 - 528 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2023
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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=161748423&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161748423 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Mar2023 vid: 65 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161748423 160656740 161748423 161748423 10.1007/s00234-022-03098-2 161748423 ppf: 513 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnostic performance of magnetic resonance imaging–based machine learning in Alzheimer's disease detection: a meta-analysis. aug: au: Hu, Jiayi Wang, Yashan Guo, Dingjie Qu, Zihan Sui, Chuanying He, Guangliang Wang, Song Chen, Xiaofei Wang, Chunpeng Liu, Xin affil: Department of Epidemiology and Statistics, School of Public Health, Jilin University, 130021, Changchun, Jilin, China sug: subj: Alzheimer's Disease Diagnosis Brain Radiography Magnetic Resonance Imaging Methods Machine Learning Sensitivity and Specificity Human Meta Analysis Medline Embase Cochrane Library Checklists Descriptive Statistics Image Interpretation, Computer Assisted ab: Purpose: Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI). Methods: The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias. Results: We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81–0.89), 0.88 (95%CI 0.84–0.91), 7.15 (95%CI 5.40–9.47), 0.17 (95%CI 0.12–0.22), 43.34 (95%CI 26.89–69.84), and 0.93 (95%CI 0.91–0.95). Conclusion: ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice. pubtype: Academic Journal doctype: meta analysis research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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