ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2019
Prediction of lumbar vertebral strength of elderly men based on quantitative computed tomography images using machine learning.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
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- Transformative biomechanics and mechanobiology breakthroughs shaping the future of health and medicine.Innovation (Cambridge (Mass.)) · 2026Review
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review.Frontiers in medicine · 2026Review
- Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.PloS one · 2025Article
- Assessment of submicron bone tissue composition in plastic-embedded samples using optical photothermal infrared (O-PTIR) spectral imaging and machine learning.Journal of structural biology: X · 2024Article
- New Horizons: Artificial Intelligence Tools for Managing Osteoporosis.The Journal of clinical endocrinology and metabolism · 2023Article
- A Review of CT-Based Fracture Risk Assessment with Finite Element Modeling and Machine Learning.Current osteoporosis reports · 2022Review
- Artificial intelligence in spine care: current applications and future utility.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- Finite Element Assessment of Bone Fragility from Clinical Images.Current osteoporosis reports · 2021Review
- Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study.European radiology experimental · 2021Article
- CNN-based qualitative detection of bone mineral density via diagnostic CT slices for osteoporosis screening.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2021Article
- Artificial intelligence in orthopaedics: A scoping review.PloS one · 2021Article
- A Review on the Use of Artificial Intelligence in Spinal Diseases.Asian spine journal · 2020Article
- Applications of Machine Learning Using Electronic Medical Records in Spine Surgery.Neurospine · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
The parameters extracted from quantitative computed tomography (QCT) images were used to predict vertebral strength through machine learning models, and the highly accurate prediction indicated that it may be a promising approach to assess fracture risk in clinics.
introductionVertebral fracture is common in elderly populations. The main factor contributing to vertebral fracture is the reduced vertebral strength. This study aimed to predict vertebral strength based on clinical QCT images by using machine learning.
methodsEighty subjects with QCT data of lumbar spine were randomly selected from the MrOS cohorts. L1 vertebral strengths were computed by QCT-based finite element analysis. A total of 58 features of each L1 vertebral body were extracted from QCT images, including grayscale distribution, grayscale values of 39 partitioned regions, BMD
resultsThe 58 parameters were simplified to five features (grayscale value of the 60% percentile, grayscale values of three specific partitioned regions, and BMD
conclusionsThis study provided an effective approach to predict vertebral strength and showed that it may have great potential in clinical applications for noninvasive assessment of vertebral fracture risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.