Table-1.

Ongoing developments in the field of AI in MSK, in the recent years (5-years; 2017–2021).

Serial No.	Year	Modality	Aim (task assisted)	MSK tissue	AI Approach	Dataset used	Performance Output	Obvious limitations	Reference
1.	2021	Radiograph	Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs	Bone	ImageNet convolutional neural network (CNN)	1306	Area under curve = 0.72 Sensitivity = 73% Specificity = 73%	-Small dataset	Chen HY et al.35
2.	2020	Radiograph	Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs	Bone	Ensemble of CNNs	7,15,343	Overall AUC = 0.974; Sensitivity = 95.2%; Specificity = 81.3%; PPV = 47.4% NPV = 99.7%	over-represented infrequently acquired regions	Jones RM et al.36
3.	2020	Radiograph	Bone fracture detection through the two-stage system of Crack-Sensitive Convolutional Neural Network	Bone	Double CNN models in sequence- FastNet, followed by CrackNet	3053	Accuracy = 0.91; precision = 0.89; recall = 0.90; F-measure = 0.90	-Small dataset	Ma Y et al.25
4.	2019	Radiograph	Classify hip fracture, patient traits and hospital process variables	Bone	CNN's	23,602	The fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86)	-Absence of a reliable gold standard. -Limited label accuracy. -Limited accuracy of covariate data. -Pre-processing reduces image resolution.	Badgeley M et al.37
5.	2018	Radiographs	Deep neural network improves fracture detection by clinicians (all extremities for pretraining but wrist radiographs for final training, validation and testing	Bone	CNN	1,32,345	The average clinician's sensitivity was 80.8% (95% CI, 76.7–84.1%) unaided and 91.5% (95% CI, 89.3–92.9%) aided, and specificity was 87.5% (95 CI, 85.3–89.5%) unaided and 93.9% (95% CI, 92.9–94.9%) aided.	-Single Institute study -Ground truth is subject to the experience of the radiologist	Lindsey R et al.38
6.	2018	Radiograph	The ability of a deep learning algorithm to detect and classify proximal humerus fractures using AP shoulder radiographs.	Bone	CNN	1891	Sensitivity = 0.99 Specificity = 0.97; Youden index = 0.97; Area under curve = 1.0	-Neer classification was used, which is only moderately reliable. -Cannot be applied to clinics	Chung SW et al.39
7.	2017	Radiograph	Automated deep learning system to detect hip fractures from frontal pelvic x-rays	Bone	Regression-based CNN	53,000	The area under the ROC curve of 0.994	Small labelled dataset	Gale W et al.23
8.	2017	Radiograph	Automated fracture detection on plain radiographs (wrist radiographs).	Bone	Inception V3 Network- CNN	11,112	The area under the ROC curve 0.954	-Ground truth was a radiologist (human) -Small labelled dataset.	Kim DH et al.40
9.	2017	Radiographs	Automatic Classification of Proximal Femur Fractures	Bone	Attention Models- Spatial transformer	1000	High sensitivity and specificity	-Small dataset (Single institution study)	Kazi et al.41
10.	2021	CT	A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance.	Bone	CNN	8529	-Increased detection recall and classification accuracy (0.922 and 0.863) compared with the radiologists alone (0.812 vs. 0.850). -The radiologists achieved a higher precision rate, recall rate, and F1-score for fracture detection when using the deep learning model, at 0.943, 0.978, and 0.960,	NA	Meng XH et al.42
11.	2020	CT	A multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.	Bone	3D- U-Net	130	Mean DSC for the multiscale algorithm was 0.61 ± 0.15 compared with 0.32 ± 0.16 for the 3D U-Net method and 0.52 ± 0.17. AUCs for automated volume measurement and categorical estimation were 0.86 and 0.77, respectively (P = .004). An optimal cutoff of 278.9 mL yielded Accuracy = 84%, Sensitivity = 82%, Specificity = 93%, PPV = 86%, NPV = 83%.	-Single institution study	Dreizin D et al.43
12.	2020	CT	Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility.	Bone	Faster R–CNN and YOLOv3	1079	The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 s.	-The current model cannot show the anatomical location of the rib fractures (right or light, number of ribs, anatomical name of fractured rib) -Small validation test set	Zhou QQ et al.44
13.	2018	CT	An automatic system that can detect incidental osteoporotic vertebral fractures in chest, abdomen, and pelvis.	Bone	ResNet34 model for feature extraction; Long-short term memory model	1432	These results indicate that our CNN/LSTM approach has high efficacy for diagnosing OVF and its performance is on par with practicing radiologists.	-Single institution study, therefore generalisability is arguable. -Single label for entire model, therefore chances of confounding.	Tomita N et al.45
14.	2020	MRI	MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations	Muscle, tendons	Base model = VGG-16	2492	Mean area under the curve = 0.98	-Single Institution study	Kim M et al.46
15.	2019	MRI	Deep Learning Algorithm in Detecting Osteonecrosis of the Femoral Head on MRI	Bone	ResNet-CNN	1892 hips (1037 diseased and 855 normal)	Sensitivity and specificity for the external test set were 84.8% and 91.3% for the DL algorithm. Sensitivity and specificity for the geographic external test set were 75.2% and 97.2% for the DL algorithm. Higher than less experienced radiologist, and comparable to the experienced radiologist.	-Ideal testing environment. -Slight selection bias. -Difficult to know if the performance of the model will be hindered by other diseases affecting the trabecular pattern.	Chee CG et al.47
16.	2018	MRI	Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet	Ligament	MRNet (CNN)	1370	In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the ROC (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set.	-Performance was sub-par as compared to radiologists.	Bien N et al.48
17.	2018	MRI	Super-resolution musculoskeletal MRI using deep learning	NA (Scan quality)	3D- CNN “DeepResolve”	124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients.	Significantly better structural similarity, peak signal to noise ratio, and root mean square error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all down-sampling factors.	It did not match the image quality of the high-resolution ground-truth images, but it outperformed other resolution enhancement methods.	Chaudhari AS et al.49
18.	2018	USG	investigation into the feasibility of using deep learning methods for developing arbitrary full spatial resolution regression analysis of B-mode ultrasound images of human skeletal muscle.	Muscle	Feature engineering (Wavelet), convolutional neural networks (CNN), residual convolutional neural networks (ResNet) and deconvolutional neural networks	8	Deconvolutional Neural Network > CNN/ResNet > Wavelet	None stated.	Cunningham R et al.50
19.	2017	USG	Ultrasound aided vertebral level localization for lumbar surgery	Bone	Deep CNN, Random Forest	19	DL method outperformed the Random Forest on the test dataset (F-measure of 0.90 vs 0.83)	Semi-automatic (therefore, user dependent)	Baka N et al.51