Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patient Population
2.2. X-ray Acquisition Parameters
2.3. Methodology
2.3.1. Knee Structure-Aware Image Preprocessing
2.3.2. DL Architecture
2.3.3. Model Specifications
2.4. Statistical Analysis
3. Results
3.1. Performance of the Classification Models
3.2. Comparison with Physician Evaluation
3.3. Qualitative Results of the Classification Models
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Characteristics | Hospital A (S.T.) (n = 280) | Hospital B (S.S.T.) (n = 233) | Hospital C (G.S.T.) (n = 450) | Hospital D (C.T. Hospital) (n = 450) |
---|---|---|---|---|
Sex | ||||
Male | 118 | 67 | 189 | 185 |
Female | 162 | 166 | 261 | 265 |
Age (mean ± SD) | 62 ± 4 | 62 ± 5 | 62 ± 2 | 63 ± 3 |
Number of Image (disease statue) | ||||
Normal | 168 (60%) | 139 (59.7%) | 150 (33.3%) | 150 (33.3%) |
Abnormal | 112 (40%) | 94 (40.3%) | 300 (66.7%) | 300 (66.7%) |
X-ray parameter | ||||
Tube potential (kVp) | 60 | 60–70 | 60 | 60 |
Tube intensity (mA) | 100 | 100 | 100 | 100 |
Exposure time (s) | 0.125 | 0.300 | 0.125 | 0.125 |
Focus to detector distance (cm) | ||||
Supine | 85 | 100 | 100 | 100 |
Erect | 78 | 100 | 100 | 100 |
Metric (±95% CI) | DenseNet121 | Proposed Method | p-Value † |
---|---|---|---|
AUC | 0.821 (0.770–0.872) | 0.892 (0.853–0.931) | <0.001 |
Accuracy | 0.764 (0.707–0.815) | 0.803 (0.749–0.850) | 0.133 |
Sensitivity | 0.753 (0.686–0.835) | 0.820 (0.753–0.880) | 0.052 |
Specificity | 0.776 (0.619–0.785) | 0.785 (0.710–0.851) | 1.000 |
Metric (±95% CI) | Proposed Method | Non-Orthopedic | |||
---|---|---|---|---|---|
Physician 1 | p-Value † | Physician 2 | p-Value † | ||
Accuracy | 0.803 (0.749–0.850) | 0.568 (0.505–0.630) | <0.001 | 0.568 (0.505–0.630) | <0.001 |
Sensitivity | 0.820 (0.753–0.880) | 0.701 (0.626–0.776) | 0.020 | 0.723 (0.641 –0.798) | 0.048 |
Specificity | 0.785 (0.710–0.851) | 0.421 (0.330–0.512) | <0.001 | 0.396 (0.314–0.479) | <0.001 |
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Won, H.; Lee, H.S.; Youn, D.; Park, D.; Eo, T.; Kim, W.; Hwang, D. Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study. Diagnostics 2024, 14, 1900. https://doi.org/10.3390/diagnostics14171900
Won H, Lee HS, Youn D, Park D, Eo T, Kim W, Hwang D. Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study. Diagnostics. 2024; 14(17):1900. https://doi.org/10.3390/diagnostics14171900
Chicago/Turabian StyleWon, Hyeyeon, Hye Sang Lee, Daemyung Youn, Doohyun Park, Taejoon Eo, Wooju Kim, and Dosik Hwang. 2024. "Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study" Diagnostics 14, no. 17: 1900. https://doi.org/10.3390/diagnostics14171900