Review Article
Emergence of Deep Learning in Knee Osteoarthritis Diagnosis
Table 5
Summary of 2D CNN classification approaches on onset of symptomatic osteoarthritis.
| | Publication reference | Target tasks | Modality (imaging sequence) | Data set | Network architecture | Performance |
| | Pierson et al. [56] | Predict knee pain | X-ray (plain radiography) | OAI: 4,172 subjects (2877 training, 1295 validation) | CNN | AUC: 0.69 | | Guan et al. [22] | Predict knee pain | X-ray (plain radiography) | OAI: 2000 subjects (1500 testing, 200 validation, 300 testing) | YOLO + DenseNet | AUC: 0.753; SN: 65.77; SP: 73.51 | | Chang et al. [14] | Predict knee pain | MRI (SAG-IW-TSE) | OAI: 1505 subjects (training : testing; 90% : 10%) | Siamese network | AUC: 0.808 |
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Note. Modality (imaging sequence): magnetic resonance imaging (MRI); data set: Osteoarthritis Initiative (OAI); network architecture: convolutional neural network (CNN); performance: specificity (SP), sensitivity (SN), and area under receiver operating characteristics curve (AUC).
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