Table 1.
Highlight of analyzed studies
| Author | Study design | Prediction input | Number of patients | Machine learning model | Progression stander | Risk factors | Prediction result |
|---|---|---|---|---|---|---|---|
| Wang et al. [9] | Retrospective study | Demographic factors and X-rays in the spine and hand | 810 | Attention-based capsule neural network and DCNN | Cobb angle increase in the major curve of ≥ 6° between the first visit and skeletal maturity in curves that exceeded 25° | 1. Patient demographics; 2. Vertebral morphology; 3. Skeletal maturity; 4. Bone quality | Model accuracy was 83.2% and AUC was 0.84 |
| Wang et al. [12] | Retrospective study | Standing posteroanterior X-rays | 490 | Deep capsule network with self-attention routing | Cobb angle increase in the major curve of ≥ 6° between the first visit and skeletal maturity in curves that exceeded 25° | 1. Radiomics from ROI (apical vertebrae or disc of the major curve, together with at least two adjacent vertebras above and below as well as the lateral rib articulations) | Model accuracy was 77.1% and AUC was 0.74 |
| Yahara et al. [10] | Retrospective study | The frontal view of the total spine radiographs | 58 | DCNN | Cobb angle increased more than 10° within 2 years | 1. Three ROI of frontal view X-rays | Model accuracy was 69% |
| Kadoury et al. [21] | Retrospective study | Biplanar X-rays | 133 | Discriminative probabilistic manifold | Difference of over 6° between the first and last visits | Geometric features from 3D models, anatomical landmarks, Intervertebral parameters, and skeletal properties | The prediction differences of 2.1° in main curve angulation |
| Alfraihat et al. [6] | Retrospective study | Standing and side-bending spinal radiographs, including the pelvis | 193 | Random forest | Cobb angle difference of 6° or more between the first and the last visit | 1. Initial major Cobb angle; 2. Patient flexibility; 3. Initial lumbar lordosis angle; 4. Initial thoracic kyphosis angle; 5. Age at the last visit; 6. The number of spinal levels involved, 7. The Risser “ + ” stage at the initial consultation | MAE of Cobb angle between prediction and truth was 4.64 |
| Deng et al. [31] | Retrospective study | Demographic factors and X-ray | 341 | Support vector machine | Future Cobb angle | 1. Clinical indicators; 2. Brace usage; 3. Patient demographics; 4. Baseline clinical measurements | RMSE was 5.181 |
| Chu et al. [13] | Retrospective study | Demographic factors and posteroanterior X-ray images | 463 | Capsule network | Cobb angle increase > 5° in 3-month follow-up | 1. Sex; 2. Age; 3. Weight; 4. Sitting height; 5. Standing height; 6. Arm span; 7. Risser sign; 8. Distal radius; 9. Ulna classification; 10. Posteroanterior radiographs; 11. Bracing compliance | Accuracy of 73.9% |
| Guo et al. [15] | Retrospective study | Demographic factors and X-ray | 1655 | RNN with LSTM cells | Future Cobb angle | 1. Current Cobb angle; 2. Future Cobb angle; 3. Current age; 4. Time Span; 5. Current Brace; Future Brace; 7. Change brace | RMSE was 1.229 |
| García-Cano et al. [7] | Retrospective study | Stereoradiographic 3D reconstructions from conventional X-rays | 150 | Random Forest | Difference of over 6° between the first and last visits | 1. 9 ICs from the 3D variability of the shapes in the posteroanterior, sagittal and apical planes | Difference between prediction and real was 1.83, 5.18, and 4.79° of Cobb angles in the proximal thoracic, main thoracic, and thoracolumbar lumbar sections, respectively |
| Patel et al. [25] | Retrospective study | Surface topography | 38 | Proportional odds logistic modeling | Cobb angle increase > 6° | 1. Surface topography; 2. Age; 3. Gender; 4. scoliotic angle | Accuracy was 71% |
| Hong et al. [18] | Retrospective study | Surface topography | 45 | Decision trees | Cobb angle increase > 5° | 1. Surface topography | Sensitivity and specificity were 73% and 44% |
| Ghaneei et al. [19] | Retrospective study | Surface topography | 128 | Customized k-Nearest Neighbor | Cobb angle increase > 5° | 1. Surface topography | Accuracy was 93% |
| Zhang et al. [20] | Prospective study | Smartphone photographs of patients' backs | 1780 | CNNs with Attention Mechanisms | Cobb angle increase > 5° in 6-month follow-up | 1. Smartphone photographs of patients' backs | AUC was 0.757 |
| Lv et al. [17] | Retrospective study | Visual inspection, Adam FBT and measurement of the angle of trunk rotation | 3313 | Artificial Neural Network Model | Occurrence of AIS | 1. The ratio of sitting height to standing height; 2. Angle of lumbar rotation; 3. Scapular tilt; 4. Shoulder-height difference; 5. Lumbar concave; 6. Pelvic tilt; | AUC was 0.899 |
| Yan et al. [27] | Retrospective study | Visual inspection, Adam’s FBT, and measurement of the angle of trunk rotation | 1779 | Logistic Regression models | Occurrence of AIS | 1. Angle of thoracic rotation; 2. Angle of thoracolumbar rotation; 3. Angle of lumbar rotation; 4. Scapular tilt 5. Shoulder-height difference; 6. Lumbar concave; 7. Pelvic tilt | Accuracy was 83.3% |