[Retracted] Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Table 4

Summary of automated knee OA diagnosis and prognosis.

Task	Subtask	Area of OA management	Achievements	Future work suggestions	Machine learning techniques
Localization of knee joint	Detection of knee joint	Diagnosis	(i) Detected tibiofemoral joints on X-ray images [40]	(i) Recognition of OA features	(i) Histogram of oriented gradients [40–43]
			(ii) Detected patellofemoral joints on X-ray images [44]	(ii) Quantification of qualitative OA features	(ii) Local binary pattern [41, 42, 44]
					(iii) Random forest regression voting [44]
					(iv) Fully convolutional neural network [45, 46]
			(iii) Detected cartilage X-ray images [41, 42]		(v) YOLOv2 network [46]
			Segmentation of knee joint components			Diagnosis	(i) Segmented knee cartilage from 2D ultrasound images [27, 28]	(i) Area measurement	(i) Locally statistical level set method [28]
							(ii) Segmented knee cartilage from 2D MRI images [47]	(ii) Volumetric measurement	(ii) Automatic seed point detection [48]
	(iii) Segmented cartilage and meniscus from MRI images [29]	(iii) Joint shape measurement		(iii) Random walker [27]
	(iv) Segmented subchondral bone from multiple 2D MRI images [48]	(iv) Quantification of measurable OA features		(iv) Watershed
	(v) Segmented distal femur and proximal tibia from X-ray images [49]	(v) Reconstruction of 3D knee joint model for simulation and joint loading study		(v) Graph cut [27]
	(vi) Calculated joint space width on X-ray images [49]	(vi) Finite element analysis		(vi) Support vector machine classifier [43]
	(vii) Segmented femoral condyle cartilage from ultrasound images [50]	(vii) Utilization of statistical and computational models		(vii) Decision tree classifier [41, 42]
	(viii) Segmented bones (femur and tibia) and cartilages (femoral and tibial cartilages) on MRI images [51]			(viii) Active contour algorithm [42]
	(ix) Segmented knee bones, cartilage, and muscle tissues on MRI images [52, 53]			(ix) U-Net [29, 47, 54–57]
	(x) Segmented femoral cartilage and tibial cartilage from 3D MRI images [54, 58]			(x) Res-U-Net [49]
				(xi) Siam-U-Net [50]
				(xii) CUMed-Vision [49]
				(xiii) DeepLabv3 [49]
				(xiv) FC-DenseNet [47]
				(xv) LinkNet [47]
				(xvi) TernausNet [47]
				(xvii) AlbuNet [47]
				(xviii) Attention U-Net [47]
				(xix) LadderNet [47]
				(xx) Multi-atlas registration [51]
				(xxi) CycleGAN [51]
				(xxii) cGANs [52]
				(xxiii) Connected conditional random field model [53]
				(xxiv) Convolutional encoder-decoder model [53]
	Classification of knee OA severity	N/A	Diagnosis	(i) Discriminated osteoarthritic knee based on MRI features [59]	(i) Risk stratification	(i) 3D-CNN [56]
				(ii) Discriminated patellofemoral OA based on X-ray images [44]	(ii) Classification of OA features	(ii) Deep Siamese CNN [60]
(iii) Classified meniscal lesion using MRI data [56]				(iii) Classification of OA severity based on computational outcomes	(iii) CNN with LBP [40]
(iv) Graded knee OA severity using X-ray images based on KL classification [40, 46, 60]					(iv) CNN with HOG [40]
					(v) ResNet [46, 61]
					(vi) VGG [46, 61]
					(vii) DenseNet [46, 55, 61, 62]
					(viii) InceptionV3 [46]
					(ix) GooLeNet [61]
					(x) ResNeXt [61]
					(xi) MobileNetV2 [61]
					(xii) Linear mixed-effects models [45]
					(xiii) Elastic net [45]
					(xiv) Support vector [59] machine [40]
					(xv) Random forest model [45, 56]
					(xvi) K-nearest neighbour [42]
					(xvii) Ensemble method using SE-ResNet-50 and SE-ResNet-50-32x4d [63]
Prediction of knee OA disease progression	Without intervention	Prognosis	(i) Estimated future knee OA incidence	(i) Risk stratification	(i) Random forest classifier [64, 65]
			(a) 30 months [64]	(ii) Selection of data from suitable time points to indicate short-term and long-term OA changes	(ii) Logistic regression classifier [66–69]
			(b) 48 months [70]	(iii) OA feature change detection	(iii) Support vector machine classifier [66]
			(c) 8 years	(iv) Discovery of pain-associated imaging features	(iv) XGBoost model [49]
			(ii) Predicted medial JSN progression [66]		(v) Multilayer perceptron [67, 71]
			(iii) Predicted radiographic joint space loss progression [67]		(vi) LASSO regression [39]
			(iv) Predicted knee OA onset and knee OA deterioration [71]		(vii) Artificial neural network [70]
			(v) Discriminated between progressors and nonprogressors [72]		(viii) Deep CNN [44, 72, 73]
			(vi) Predicted pain [73, 74]		(ix) DenseNet CNN [68]
			(vii) Predicted risk of progressive pain and structural change [65]		(x) Gradient boosting machine [44, 70]
			(viii) Predicted total knee replacement (TKR) incidence [68, 75]		(xi) Duo classifier [65]
					(xii) DeepSurv [75]
					(xiii) Dynamic functional mixed-effects model [54]