Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data

Abstract

Background: A current challenge in osteoporosis is identifying patients at risk of bone fracture. Purpose: To identify the machine learning classifiers that predict best osteoporotic bone fractures and, from the data, to highlight the imaging features and the anatomical regions that contribute most to prediction performance. Study Type: Prospective (cross-sectional) case-control study. Population: Thirty-two women with prior fragility bone fractures, of mean age = 61.6 and body mass index (BMI) = 22.7 kg/m 2 , and 60 women without fractures, of mean age = 62.3 and BMI = 21.4 kg/m 2 . Field Strength/ Sequence: 3D FLASH at 3T. Assessment: Quantitative MRI outcomes by software algorithms. Mechanical and topological microstructural parameters of the trabecular bone were calculated for five femoral regions, and added to the vector of features together with bone mineral density measurement, fracture risk assessment tool (FRAX) score, and personal characteristics such as age, weight, and height. We fitted 15 classifiers using 200 randomized cross-validation datasets. Statistical Tests: Data: Kolmogorov-Smirnov test for normality. Model Performance: sensitivity, specificity, precision, accuracy, F1-test, receiver operating characteristic curve (ROC). Two-sided t-test, with P < 0.05 for statistical significance. Results: The top three performing classifiers are RUS-boosted trees (in particular, performing best with head data, F1 = 0.64 ± 0.03), the logistic regression and the linear discriminant (both best with trochanteric datasets, F1 = 0.65 ± 0.03 and F1 = 0.67 ± 0.03, respectively). A permutation of these classifiers comprised the best three performers for four out of five anatomical datasets. After averaging across all the anatomical datasets, the score for the best performer, the boosted trees, was F1 = 0.63 ± 0.03 for All-features dataset, F1 = 0.52 ± 0.05 for the no-MRI dataset, and F1 = 0.48 ± 0.06 for the no-FRAX dataset. Data Conclusion: Of many classifiers, the RUS-boosted trees, the logistic regression, and the linear discriminant are best for predicting osteoporotic fracture. Both MRI and FRAX independently add value in identifying osteoporotic fractures. The femoral head, greater trochanter, and inter-trochanter anatomical regions within the proximal femur yielded better F1-scores for the best three classifiers. Level of Evidence: 2 Technical Efficacy: Stage 2

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

“…ML techniques are also becoming increasingly useful in clinical diagnosis. ML methods can be extended to check for osteoporosis and fracture using models trained on both MRI and CT images, 31,32 which are useful in spine and general orthopedic surgery. Even simple models trained quickly and on relatively scant data, such as regression-based on imaging density of vertebral bodies in a CT scan and demographic information can achieve 90% accuracy in identifying osteoporosis with CT scans.…”

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

“…Calibration curves may be constructed from the phantoms in dedicated QCT scans, mapping CT attenuation in HU to bone mineral density in mg/cc on that scanner. (63)(64)(65) For example, MRI in combination with FRAX score, BMD, and patient physical characteristics has been used to predict osteoporotic bone fractures. DXA and automated quantitative CT of the lumbar spine have been compared for assessment of bone mineral density on CT with an area under the ROC curve of 0.888.…”

“…This calibration curve may then be used to convert the mean trabecular bone CT attenuation on this now-calibrated scanner to give a BMD estimation without the presence of the phantom. (65) Opportunistic screening (62) Osteoporosis and fragility fracture risk have also been assessed on dental panorex radiographs, hip radiographs, and on MRI.…”

“…(62) Osteoporosis and fragility fracture risk have also been assessed on dental panorex radiographs, hip radiographs, and on MRI. (63)(64)(65) For example, MRI in combination with FRAX score, BMD, and patient physical characteristics has been used to predict osteoporotic bone fractures. (65)…”

“…(63)(64)(65) For example, MRI in combination with FRAX score, BMD, and patient physical characteristics has been used to predict osteoporotic bone fractures. (65)…”