Diagnostic performance of magnetic resonance imaging–based machine learning in Alzheimer’s disease detection: a meta-analysis

• Published in: Neuroradiology Vol. 65; no. 3; pp. 513 - 527
• Main Authors: Hu, Jiayi, Wang, Yashan, Guo, Dingjie, Qu, Zihan, Sui, Chuanying, He, Guangliang, Wang, Song, Chen, Xiaofei, Wang, Chunpeng, Liu, Xin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023; Springer Nature B.V
• Subjects: Aged; Algorithms; Alzheimer Disease - diagnosis; Alzheimer's disease; Artificial Intelligence; Bias; Diagnostic Neuroradiology; Disease detection; Feature extraction; Humans; Imaging; Learning algorithms; Likelihood ratio; Machine Learning; Magnetic Resonance Imaging; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Meta-analysis; Neurodegenerative diseases; Neuroimaging; Neurology; Neuroradiology; Neurosciences; Neurosurgery; Object recognition; Older people; Patients; Performance evaluation; Quality assessment; Quality control; Radiology; Reproducibility of Results; Resonance; Sensitivity and Specificity; Diagnosis; Alzheimer’s disease; Magnetic resonance imaging; Machine learning; Meta-analysis
• ISSN: 0028-3940; 1432-1920
• DOI: 10.1007/s00234-022-03098-2

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.