Abstract
Background
For knee osteoarthritis, the commonly used radiology severity criteria Kellgren–Lawrence lead to variability among surgeons. Most existing diagnosis models require preprocessed radiographs and specific equipment.
Methods
All enrolled patients diagnosed with KOA who met the criteria were obtained from **** Hospital. This study included 2579 images shot from posterior–anterior X-rays of 2,378 patients. We used RefineDet to train and validate this deep learning-based diagnostic model. After develo** the model, 823 images of 697 patients were enrolled as the test set. The whole test set was assessed by up to 5 surgeons and this diagnostic model. To evaluate the model’s performance we compared the results of the model with the KOA severity diagnoses of surgeons based on K-L scales.
Results
Compared to the diagnoses of surgeons, the model achieved an overall accuracy of 0.977. Its sensitivity (recall) for K-L 0 to 4 was 1.0, 0.972, 0.979, 0.983 and 0.989, respectively; for these diagnoses, the specificity of this model was 0.992, 0.997, 0.994, 0.991 and 0.995. The precision and F1-score were 0.5 and 0.667 for K-L 0, 0.914 and 0.930 for K-L 1, 0.978 and 0.971 for K-L 2, 0.981 and 0.974 for K-L 3, and 0.988 and 0.985 for K-L 4, respectively. All K-L scales perform AUC > 0.90. The quadratic weighted Kappa coefficient between the diagnostic model and surgeons was 0.815 (P < 0.01, 95% CI 0.727–0.903). The performance of the model is comparable to the clinical diagnosis of KOA. This model improved the efficiency and avoided cumbersome image preprocessing.
Conclusion
The deep learning-based diagnostic model can be used to assess the severity of KOA in portable devices according to the Kellgren–Lawrence scale. On the premise of improving diagnostic efficiency, the results are highly reliable and reproducible.
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Introduction
Knee osteoarthritis (KOA) is a degenerative joint disease with a prevalence ranging from 4 to 12% [1,2,3]. Currently, for orthopaedic surgeons, knee weight-bearing standing X-ray radiographs as a standard method for evaluating KOA remain the most common radiology examination method due to their safety, popularity and low cost [4,5,6]. Currently, accurate KOA diagnosis and assessment are highly based on radiographic evidence [7, 8].
Currently, the Kellgren–Lawrence scale (K-L) is most commonly used to diagnose and determine the severity of KOA based on joint space narrowing, osteophytes, sclerosis, and definite bony deformities on X-rays in radiology examinations [9, 10]. However, the classification criteria of the K-L scales are subjective [11]. In clinical use, different doctors or the same doctor at different times may often obtain similar results rather than identical results on the same X-ray. Many clinical studies involving KOA diagnosis have ensured reliability by increasing the number of repeated diagnoses [12,13,14,15,16]. Moreover, the total numbers of X-ray examinations are much higher in large hospitals, which is a heavy burden on radiologists and surgeons. Therefore, many rapid diagnosis and assessment models have been developed in collaboration with image analysis. The models can identify images via digital processing techniques to make the artificial intelligence process more accurate and cost-effective [13]. The technology includes knee joint recognition and image processing based on deep learning [14]. Swiecicki et al. [15] developed a diagnostic model based on the two-stage Faster R-CNN model to assess the severity of KOA from both posterior–anterior (PA) and lateral (LAT) views. Tiulpin et al. [16] also developed a diagnostic model based on the ResNet34 model to detect KOA from original PA views of knees. Similarly, Norman et al. [17] developed a KOA diagnostic model based on DenseNet, which uses the feature more effectively in deep technology. Current studies show that the existing diagnostic models can achieve satisfactory accuracy [15,16,17]. However, those models rely on preprocessed, highly optimized digital images in specific software and hardware, which may not be feasible in most clinical scenarios and affects the actual use value in some ways [18,19,20,32]. Second, although the study shows that this model has good reliability and reproducibility in radiology assessment, the specific diagnosis still needs other evidence, such as the patient’s clinical manifestations, laboratory tests, and other imaging findings [12]. Third, in the process of use, due to the restrictions of the algorithm, there may be some photos in which the entire knee joints cannot be identified, and the diagnostic model does not display a diagnosis if the algorithm detects implants or no object. Therefore, there may be a subset that is not identified as implants or knee joints, which requires rephotographing or further diagnosis by surgeons. Fifth, compared to similar studies, our sample size is relatively small and from a single hospital, and outcomes from a larger cohort and multiple countries may be different. Sixth, we propose the deep-learning-based RefineDet for object detection and classification, but the outcomes may be different based on other algorithms.
Conclusion
The deep learning-based diagnostic model can be used to assess the severity of KOA in portable devices. On the premise of improving the diagnostic efficiency, the results are highly reliable and reproducible.
Availability of data and materials
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- KOA:
-
Knee osteoarthritis
- K-L:
-
Kellgren–Lawrence
- PA X-ray:
-
Posterior–anterior view standing bearing X-ray
- AUC:
-
Area under curve
- LAT:
-
Lateral
- ARM:
-
Anchor refinement module
- ODM:
-
Object detection module
- TCB:
-
Transfer connection block
- IoU:
-
Intersection over Union
- TKA:
-
Total knee arthroplasty
- UKA:
-
Unicompartmental knee arthroplasty
- I-F:
-
Internal fixation
- E–F:
-
External fixation
- PFA:
-
Patellofemoral arthroplasty
- CHD:
-
Coronary heart disease
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Funding
This work was supported by a grant from The National Natural Science Foundation of China (No. 81902250, 82072464) and General Hospital of the People’s Liberation Army (No. 2019MBD-042).
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YW, GZ, and JY designed the study. JY and MN followed up with the patients and collected the relevant data and images. YW and JY analyzed and interpreted the data. JY and QJ wrote the manuscript. JY, QJ and MN prepared figures. The authors read and approved the final manuscript.
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This study was approved by the ethics committee of General Hospital of PLA. All patients signed the informed consent to participate in the study.
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Yang, J., Ji, Q., Ni, M. et al. Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning. J Orthop Surg Res 17, 540 (2022). https://doi.org/10.1186/s13018-022-03429-2
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DOI: https://doi.org/10.1186/s13018-022-03429-2