Review Article

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Table 1

Summary of studies conducted with nonimaging data.

Publication referenceTaskData set (nonimaging data)Performance
With nonimaging dataWithout nonimaging data

Lim et al. [9]Predict presence of OA5749 subjects with 24 features including demographics, personal characteristics, lifestyle variables, and health status (3795 training (30% validation), 1955 testing)AUC: 76.8%
Accuracy: 71.97%
SN: 66.67%
SP: 73.35%
Positive predictive value: 39.53%

Christodoulou et al. [4]Predict progression of OAOAI: 4796 subjects with 141 features including joint symptoms, disability, functionality, lifestyle, and general health statusOverall accuracy: 79.39%
Guan et al. [11]Predicting progression of radiographic medial joint space loss7 features of demographic data and radiographic risk factorsAUC: 0.863; SN: 80.5%; SP: 80.5%AUC: 0.799; SN: 78.0%; SP: 75.5%
Kim et al. [17]Predict knee OA severity based on KL-grade6 features including demographics, alignment, and metabolic dataAUC: 0.97 (KL0), 0.85 (KL1), 0.75 (KL2), 0.86 (KL3), and 0.95 (KL4)AUC: 0.91 (KL0), 0.80 (KL1), 0.69 (KL2), 0.86 (KL3), and 0.96 (KL4)
Martinez et al. [18]Detect OA and predict future onset OA3 features including demographic dataDetecting OA: SN: 81.03%; SP: 79.01%Detecting OA: SN: 79.0%; SP: 77.1%
Predicting onset: SN: 76.77%; SP: 62.5%Predicting onset: SN: 76.8%; SP: 57.5%

Nunes et al. [19]Stage severity of cartilage lesion3 features including demographic dataAccuracy: 86.7%Accuracy: 82.79%
Pedoia et al. [20]Detect and stage severity of meniscus and patellofemoral cartilage lesions2 features including demographic dataAccuracy: 80.74% (normal), 78.02% (mild-moderate), 75% (severe)Accuracy: 87.55% (normal), 71.43% (mild-moderate), 66.7% (severe)
Tolpadi et al. [21]Predict total knee replacement27 features including demographic data, health status, disability, pain scoresAUC± SD: 0.890 ± 0.021 (X-ray), 0.834 ± 0.036 (MRI)AUC ± SD: 0.848 ± 0.039 (X-ray), 0.886 ± 0.020 (MRI)
Guan et al. [22]Predict knee pain7 features including demographic and radiographic risk factorsAUC: 0.804; SN: 75.2%; SP: 76.2%AUC: 0.753; SN: 65.77%; SP: 73.51%

Note. Task: osteoarthritis (OA); performance: magnetic resonance imaging (MRI), specificity (SP), sensitivity (SN), and area under receiver operating characteristics curve (AUC).