Table 4.
Summary of 2DCNN classification approaches on progression of osteoarthritis diagnosis.
| Publication reference | Target tasks | Modality (imaging sequence) | Data set | Network architecture | Performance |
|---|---|---|---|---|---|
| Guan et al. [53] | Predict OA progression | X-ray (plain radiography) | OAI: 600 subjects (450 training, 50 validation, 100 testing) | Vgg16 and DenseNet | Vgg16: AUC: 0.717; SN: 80.0%; SP: 56.1% |
| DenseNet: AUC: 0.744; SN: 94.1%; SP: 48.0% | |||||
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| |||||
| Tiulpin et al. [54] | Predict OA progression | X-ray (plain radiography) | OAI: 5139 images (training) | CNN | AUC: 0.71 |
| MOST: 2,491 images (testing) | |||||
|
| |||||
| Guan et al. [11] | Predicting progression of radiographic medial joint space loss | X-ray (plain radiography) | OAI: (1400 training, 150 validation, 400 testing) images | YOLO + DenseNet | AUC: 0.799; SN: 78.0%; SP: 75.5% |
| Razmjoo et al. [7] | Predict OA incidence | MRI | OAI: 1805 subjects | Topological data analysis (TDA) + graph convolutional network (GCN model) | Accuracy (F1): 0.91; SN: 0.84; SP: 0.99 |
| Li et al. [55] | Predict OA progression by assessing severity | X-ray (plain radiography) | MOST: 3021 subjects (training : validation : testing; 80 : 10 : 10%) | Siamese neural network | AUC: 0.90 |
Note. Modality (imaging sequence): magnetic resonance imaging (MRI); data set: Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST); network architecture: convolutional neural network (CNN); performance: specificity (SP), sensitivity (SN), and area under receiver operating characteristics curve (AUC).