Table 2 Different examples of artificial intelligence (AI) use across several orthopaedic sub-specialties

1. Fractures detection and prediction [41]

- Evaluate the accuracy of deep neural networks to diagnose neck femur fractures in comparison to perceptual training of medically naïve individuals [1].

- Predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned individuals [30]

- Evaluate the ability of convolutional neural network to detect distal radius fracture on an antero-posterior view of the wrist [19].

- Incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction [6].

2. Osteoarthritis and arthroplasty

- Compare different gait patterns in patients with uni-compartment knee arthroplasty versus total knee arthroplasty [22].

- Early prediction of symptomatic knee osteoarthritis using MRI images [5, 43].

- Develop machine-learning based implant recognition system for hip arthroplasty designs [24].

- Measures of knee cartilage thickness can predict future loss of knee cartilage [23].

- Investigate the quantification of osteoarthritis and prediction of tibial cartilage loss by analysis of the tibia trabecular bone from magnetic resonance images of knees [32].

- Knee cartilage segmentation using a tri-planar convolutional network [44].

- ML tool demonstrates clinical utility with early prediction of patients who are most at risk of developing poor postoperative functional outcomes and PROMs after primary total knee arthroplasty [10].

- Predict length of stay, discharge disposition, and inpatient charges for primary anatomic, reverse, and hemishoulder arthroplasty [26].

3. Spine surgery

- Classification of scoliosis curves [2].

- Detection of lumbar spine compression fractures [3].

- Using a handgrip device and target tracking test to detect impairments of hand motor function in patients with cervical spondylotic myelopathy [31].

- Detection of spinal metastasis using a multi-resolution approach [56].

4. Foot and Ankle surgery

- Using automated segmentation to study distance and coverage mapping in Chopart joints in patients with progressive collapsing foot deformity (PCFD) [7, 8].

- Advanced semi-automated segmentation to evaluate hallux rigidus [15].

- Objective Computational technique to classify ankle osteoarthritis on weight bearing computation tomography (WBCT) [51].

- Semi-automated assessment of different hallux valgus parameters on (WBCT) of the hallux valgus [16]

5. Miscellaneous

- Switching neural networks used to classify multiple osteochondromas [37].

- Develop a machine learning algorithm to predict the prolonged opioid use after total hip arthroplasty (THA) [25].

- Online image messaging platform for remote monitoring of surgical incision sites [58].

- Ensemble learning techniques to study skeletal maturity [12].

- Classify pathological gait patterns using 3D ground reaction force (GRFs) data [4].