Machine learning in orthopaedic surgery

Table 3 Summary of machine learning for orthopaedic surgery imaging applications

Ref.	Subspecialty	Conclusion
Al-Helo et al[66]	Spine	Neural network (93.2% accurate) and k-means approach (98% accurate) used on CT scans for segmentation and prediction of lumbar wedge fractures
Forsberg et al[62]	Spine	Annotated MRIs with information labels for each spine vertebrae used to accurately detect (99.8%) and label (97%) cervical and lumbar vertebrae
Hetherington et al[64]	Spine	CNN successfully identified lumbar vertebral levels on ultrasound images of the sacrum
Jamaludin et al[65]	Spine	CNN model achieved 95.6% accuracy comparable to experienced radiologists in disc detection and labeling of T2 weighted sagittal lumbar MRIs
Pesteie et al[63]	Spine	Used ML system to detect laminae and facet joints in ultrasound images to assist in epidural steroid injection and facet joint injection administration
Ashinsky et al[71]	Joints/arthritis	ML algorithm predicted clinically symptomatic OA on T2 weighted maps of central medial femoral condyle with 75% accuracy
Liu et al[72]	Joints/arthritis	CNN performed rapid and accurate cartilage and bone segmentation within the knee joint
Shah et al[73]	Joints/arthritis	CNN used to automate the segmentation and measurement of cartilage thickness based on MRIs of healthy knees
Xue et al[70]	Joints/arthritis	CNN model trained to diagnose hip OA comparable to an attending physician with 10 years of experience in diagnosing hip OA
Kruse et al[75]	Trauma	ML improved hip fracture detection beyond logistic regression using dual x-ray absorptiometry
Olczak et al[74]	Trauma	DL networks identified fracture, laterality, body part, and exam view on orthopaedic trauma radiographs of the hand, wrist, and ankle
Oh et al[78]	Oncology	ML showed superior predictive accuracy in predicting pathological femoral fractures in metastatic lung cancer

ML: Machine learning; CNN: Convolutional neural network; OA: Osteoarthritis.