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Artificial intelligence in risk prediction and diagnosis of vertebral fractures
by
Peerbhai, Amaan
, Kramer, Andreas
, Namireddy, Srikar R.
, Gill, Saran S.
, Kamath, Abith G.
, Salih, Ahmed
, Ramsay, Daniele S. C.
, Kalasauskas, Darius
, Thavarajasingam, Santhosh G.
, Neuhoff, Jonathan
, Jankovic, Dragan
, Russo, Salvatore
, Ponniah, Hariharan Subbiah
in
692/308/409
/ 692/4023/1671/63
/ Accuracy
/ Artificial Intelligence
/ Bias
/ Chronic illnesses
/ Compression
/ Deep learning
/ Diagnosis
/ Fractures
/ Fractures, Compression - diagnosis
/ Heterogeneity
/ Humanities and Social Sciences
/ Humans
/ Machine learning
/ Meta-analysis
/ multidisciplinary
/ Neurosurgery
/ Non-pathological vertebral fractures
/ Orthopedics
/ Osteoporosis
/ Osteoporotic Fractures - diagnosis
/ Osteoporotic Fractures - epidemiology
/ Osteoporotic vertebral fractures
/ Otolaryngology
/ Prognosis
/ Risk Assessment - methods
/ Science
/ Science (multidisciplinary)
/ Software
/ Spinal Fractures - diagnosis
/ Spinal Fractures - epidemiology
/ Statistical analysis
/ Systematic review
/ Vertebrae
/ Vertebral compression fractures
2024
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Artificial intelligence in risk prediction and diagnosis of vertebral fractures
by
Peerbhai, Amaan
, Kramer, Andreas
, Namireddy, Srikar R.
, Gill, Saran S.
, Kamath, Abith G.
, Salih, Ahmed
, Ramsay, Daniele S. C.
, Kalasauskas, Darius
, Thavarajasingam, Santhosh G.
, Neuhoff, Jonathan
, Jankovic, Dragan
, Russo, Salvatore
, Ponniah, Hariharan Subbiah
in
692/308/409
/ 692/4023/1671/63
/ Accuracy
/ Artificial Intelligence
/ Bias
/ Chronic illnesses
/ Compression
/ Deep learning
/ Diagnosis
/ Fractures
/ Fractures, Compression - diagnosis
/ Heterogeneity
/ Humanities and Social Sciences
/ Humans
/ Machine learning
/ Meta-analysis
/ multidisciplinary
/ Neurosurgery
/ Non-pathological vertebral fractures
/ Orthopedics
/ Osteoporosis
/ Osteoporotic Fractures - diagnosis
/ Osteoporotic Fractures - epidemiology
/ Osteoporotic vertebral fractures
/ Otolaryngology
/ Prognosis
/ Risk Assessment - methods
/ Science
/ Science (multidisciplinary)
/ Software
/ Spinal Fractures - diagnosis
/ Spinal Fractures - epidemiology
/ Statistical analysis
/ Systematic review
/ Vertebrae
/ Vertebral compression fractures
2024
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Artificial intelligence in risk prediction and diagnosis of vertebral fractures
by
Peerbhai, Amaan
, Kramer, Andreas
, Namireddy, Srikar R.
, Gill, Saran S.
, Kamath, Abith G.
, Salih, Ahmed
, Ramsay, Daniele S. C.
, Kalasauskas, Darius
, Thavarajasingam, Santhosh G.
, Neuhoff, Jonathan
, Jankovic, Dragan
, Russo, Salvatore
, Ponniah, Hariharan Subbiah
in
692/308/409
/ 692/4023/1671/63
/ Accuracy
/ Artificial Intelligence
/ Bias
/ Chronic illnesses
/ Compression
/ Deep learning
/ Diagnosis
/ Fractures
/ Fractures, Compression - diagnosis
/ Heterogeneity
/ Humanities and Social Sciences
/ Humans
/ Machine learning
/ Meta-analysis
/ multidisciplinary
/ Neurosurgery
/ Non-pathological vertebral fractures
/ Orthopedics
/ Osteoporosis
/ Osteoporotic Fractures - diagnosis
/ Osteoporotic Fractures - epidemiology
/ Osteoporotic vertebral fractures
/ Otolaryngology
/ Prognosis
/ Risk Assessment - methods
/ Science
/ Science (multidisciplinary)
/ Software
/ Spinal Fractures - diagnosis
/ Spinal Fractures - epidemiology
/ Statistical analysis
/ Systematic review
/ Vertebrae
/ Vertebral compression fractures
2024
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Artificial intelligence in risk prediction and diagnosis of vertebral fractures
Journal Article
Artificial intelligence in risk prediction and diagnosis of vertebral fractures
2024
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Overview
With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Accuracy
/ Bias
/ Fractures, Compression - diagnosis
/ Humanities and Social Sciences
/ Humans
/ Non-pathological vertebral fractures
/ Osteoporotic Fractures - diagnosis
/ Osteoporotic Fractures - epidemiology
/ Osteoporotic vertebral fractures
/ Science
/ Software
/ Spinal Fractures - diagnosis
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