The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis

TL;DR

This study reviews how machine learning models using medical imaging can accurately detect osteoporosis, especially with x-ray and CT scans.

Contribution

The study systematically evaluates the diagnostic performance of machine learning models for osteoporosis across different imaging modalities.

Findings

X-ray and CT-based models show high sensitivity and specificity for osteoporosis detection.

MRI-based models are underrepresented and require further validation.

External validation remains limited across studies.

Abstract

Osteoporosis (OP) is projected to be a major issue significantly impacting the well-being of middle-aged and old populations. Machine learning (ML) and deep learning (DL) models developed based on medical imaging have enhanced clinicians’ diagnostic accuracy and work efficiency. However, the diagnostic performance of different types of medical imaging for OP has not been systematically assessed.

By summarizing related literature, this study aims to elucidate the role of DL models based on different medical imaging modalities in OP detection.

PubMed, Embase, the Cochrane Library, and Web of Science were systematically searched for studies using ML for the diagnosis of OP based on medical imaging. The final search was conducted on May 16, 2024. The risk of bias in the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate mixed-effects model was applied to perform meta-analyses of sensitivity (SEN) and specificity (SPC), stratified by imaging modality (x-ray, computed tomography [CT], magnetic resonance imaging [MRI]). In addition, subgroup analyses were carried out based on the type of ML algorithm, the method of validation dataset generation, and the anatomical site of assessment.

A total of 60 studies comprising 66,195 participants were encompassed in this systematic review and meta-analysis. Among these, 22 studies used x-ray imaging, 37 applied CT imaging, and 3 used MRI for ML-based OP diagnosis. For x-ray–based models, the pooled SEN and SPC for studies focusing on the appendicular skeleton were 0.97 (95% CI 0.83‐0.99) and 0.90 (95% CI 0.75‐0.96), respectively. For studies using the mandible as the target site, SEN and SPC were 0.94 (95% CI 0.89‐0.97) and 0.80 (95% CI 0.56‐0.93), respectively. For those focusing on the lumbar spine, the pooled SEN and SPC were 0.87 (95% CI 0.77‐0.93) and 0.82 (95% CI 0.75‐0.87), respectively. For CT-based models, studies targeting the hip joint reported a pooled SEN and SPC of 0.87 (95% CI 0.83‐0.90) and 0.92 (95% CI 0.81‐0.96), respectively. For the thoracic spine, SEN and SPC were 0.91 (95% CI 0.86‐0.94) and 0.94 (95% CI 0.92‐0.95), respectively, while for the lumbar spine, they were 0.91 (95% CI 0.87‐0.94) and 0.92 (95% CI 0.86‐0.95), respectively.

ML based on medical imaging demonstrates high diagnosis accuracy for OP, particularly DL models using x-ray and CT modalities. However, this study included only a limited number of original studies using MRI-based ML, and there remains a lack of adequate external validation across studies, which poses interpretative limitations. Future research should aim to develop artificial intelligence tools with broader applicability and enhanced diagnostic precision.

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