Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data
| Autor: | Cheng Chen, Gregory Chang, Punam K. Saha, Harrison Besser, Uran Ferizi, Chamith S. Rajapakse, Joseph G. Jacobs, Stephen Honig, Pirro G. Hysi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FRAX
Computer science Population Osteoporosis Machine learning computer.software_genre Logistic regression Article Body Mass Index 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Image Processing Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging Prospective Studies education Aged Statistical hypothesis testing education.field_of_study Receiver operating characteristic business.industry Reproducibility of Results Bone fracture Middle Aged medicine.disease Linear discriminant analysis Magnetic Resonance Imaging Cross-Sectional Studies ROC Curve Case-Control Studies Linear Models Female Artificial intelligence business computer Algorithm Algorithms Osteoporotic Fractures |
| Zdroj: | J Magn Reson Imaging |
| ISSN: | 1522-2586 1053-1807 |
| DOI: | 10.1002/jmri.26280 |
| Popis: | 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/m2 , and 60 women without fractures, of mean age = 62.3 and BMI = 21.4 kg/m2 . 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 J. Magn. Reson. Imaging 2019;49:1029-1038. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text