How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024
Abstract
:1. Introduction
2. Methods
- For RQ1 “What are the principal models currently employed to design diagnostic models for knee osteoarthritis based on radiographic images?”, data from eligible publications include (1) research topic, (2) materials and methods used by the authors, and (3) metric evaluation.
- For RQ2 “What are the potential factors for enhancing the accuracy of traditional machine learning (ML) and deep learning (DL) models developed in recently published research articles?”: (1) Textual descriptions (terms in the full text of the publication) highlighting the draft choices made by the authors.
- For RQ3, “What are the principal challenges and prospective avenues for further research?”: (1) the novelty of the research, (2) the experiments that yielded the best and worst results for each approach, and (3) limitations and discussion.
3. Results
3.1. Data Availability
3.2. ROI Detection/Segmentation
3.3. Knee Osteoarthritis Detection
3.3.1. Deep Learning Models
3.3.2. Hybrid Models
3.3.3. Comparative Analysis of Reviewed Methods and Techniques
- A.
- In the initial phase of the knee osteoarthritis identification process, studies employing methodologies that did not require ROI localization utilized databases of readily available images of individual knees as input to the proposed model. Furthermore, in studies based on ROI detection, manual selection or additional deep learning models for localization are employed before the classification process. Both types of studies, with and without ROI detection, employ pre-processing of the input images with resizing according to the requirements of the models used.
- -
- As demonstrated in Table 6, the methods that employed ROI detection exhibited a slight advantage over the other methods, with the highest accuracy observed in [43] at 99.81%. In this study, the edges of the knee bone are first detected and then filtered to suppress noise without losing essential image information. In the second step, the region of interest (ROI) was identified based on pixel density. Finally, the traditional machine learning K-nearest neighbor (KNN) classifier was constructed to classify knee arthrosis.
- -
- In [44], the manual selection of the ROI before the classification of knee osteoarthritis did not yield satisfactory results. In this approach, the lowest accuracy among studies employing ROI detection was obtained with an average multi-class accuracy of 61.71%.
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- A brief analysis of Table 6 allows us to conclude that approaches that did not employ ROI detection achieved performances comparable to those obtained with methods developed with ROI localization. The best performances were obtained in [28] with a weighted kappa coefficient of 0.99 and MAE of 0.0256 using a set of deep learning models. The approach used in [45] exhibited the lowest performance, with a balanced accuracy of 64.13 ± 0.88. This approach employed a Siamese network and a semi-supervised learning technique.
- B.
- In the second phase of feature extraction and classification of knee OA, the most successful techniques were based on additive methodologies for extraction, including cartilage thickness estimation in [6] and Hu invariant moments used in [43].
- -
- -
- Table 6 reveals that the common methodologies used in the different approaches are the use of a multitude of models, with the selection of the optimal model or the adoption of the result obtained by the ensemble of models. The DenseNet and ResNet deep learning models are the most commonly adopted in the proposed approaches, their performance being compared with that of other models. However, they are not the optimal candidates for this purpose. The DenseNet model achieved the best performance in the study [34], using DenseNet169, with an accuracy of 95.93%. In contrast, the ResNet model demonstrated the best performance in [47], using ResNet152V2, with an accuracy of 95.88%.
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References | Methodology | Architecture | With/Wit-Hout ROI | Classes | Dataset | Sample Size | Metric |
---|---|---|---|---|---|---|---|
Gornale S.S. et al. [43] | ▪ Multi-class classification using traditional ML models and Hu’s invariant moments. | ◦ KNN and Decision Tree | With | 5 | [6] | 2000 | Accuracy (KNN/DT) = 99.80%/95.75 (Medical expert-I) and 98.65%/95.4 (Medical expert-II) |
Fatema et al. [46] | ▪ Multi-class classification using optimal features and traditional ML models. | ◦ DT, RF, KNN, GB, XGB | With | 5 | Mendeley and Kaggle | 8660 | Best model (XGB): precision: 99.43% |
Tariq et al. [28] | ▪ Multi-class classification using an ensemble of deep learning models. | ◦ DenseNet161, DenseNet121, ResNet34,VGG19. | Without | 5 | OAI | 9786 | Best model (Ensemble): Weighted kappa = 0.99, MAE = 0.0256 |
Raza A. et al. [48] | ▪ Multi-class classification using traditional ML models and 10-fold cross-validation. | ◦ KNN, SVM, Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost | Without | 4 | Mendeley | 5778 | Best model (XGBoost and ensemble model) Accuracy: 98.90% |
Gornale S.S. et al. [49] | ▪ Multi-class classification using DL model and Gradient conjugate. | ◦ Artificial neural networks (ANN) + Gradient conjugate/Gradient descent/Quasi-Newton | With | 5 | [6] | 1650 | Accuracy: 98.7% (surgeon-1), 98.2% (surgeon-2) |
Kalpana V et al. [47] | ▪ Multi-class classification using DL models. | ◦ DenseNet, EfficientNetB7, Inception, MobileNet, NASNet, ResNet152V2, VGG19, Xception | Without | 5 | Local dataset | 8250 | Best accuracy (MobileNet): 98.36% |
Touahema S. et al. [50] | ▪ MeedKnee: Multi-class classification using DL model. | ◦ Xception | 5 | Without | OAI | 5000 | Accuracy: 97.20% |
Wani and Saini [22] | ▪ Multi-class classification using DL models and adjustable ordinal loss | ◦ VGG, DenseNet, ResNet, and Inception V3 | 5 | With | OAI | 1656 | Best model (VGG19): Accuracy = 96.7%, MAE = 0.344 |
Salama et al. [51] | ▪ Multi-class classification using DL model and JSW annotation. | ◦ U-Net | 5 | With | OAI | 100 | Accuracy:96.3% |
El-Ghany et al. [34] | ▪ Multi-classification and binary classifications using DL models. | ◦ DenseNet169, Xception, ResNet50, DenseNet121, InceptionResNetV2, InceptionV3 | 2/3 | Without | OAI | 8891/6224 | Best model (DenseNet169) Accuracy: 95.93% (3-class), 93.78 (2-class) |
Anitha et al. [52] | ▪ Multi-class classification and joint space detection using DL model. | ◦ RCNN | 5 | With | Kaggle | 167 | Accuracy: 95% |
Al-rimy et al. [33] | ▪ Binary and Multi-class classification using DL model and the gradual cross-entropy (GCE) loss. | ◦ DenseNet169 | 2/5 | Without | OAI | 4982 | Accuracy: 0.9408 (2-class), 0.9179 (3-class), 0.6274 (5-class) |
Ruikar et al. [53] | ▪ OACnet: Multi-class classification using DL model. | ◦ Deep neural network and handcrafted feature engineering (joint space narrowing, bone spur, sclerosis, and deformation) | 5 | With | OAI | 9492 | Accuracy (DNN-OCnet) = 83.74%, accuracy (DNN-Ocnet + Handcrafted features) = 92.7% |
Olsson et al. [8] | ▪ Multi-class classification using DL model. | ◦ ResNet35 | Without | 5 | Local dataset | 6403 | AUC > 0.80 for the 5 grades KL—Sensitivity > 92% except for grade KL4 with 84% and a specificity between 61% and 88% |
Yoon et al. [24] | ▪ MediAI-OA: Binary, Multi-classification suing JSN Rate assessment. | ◦ HRNet (JSN), NASNet | With | 2/5 | OAI | 45,003 | Accuracy: 92% (2-class), 83% (4-class) |
Ahmed and Mohammed [54] | ▪ Multi-class classification using Convolutional Neural Networks (CNNs). | ◦ VGG16, VGG19, ResNet50 | Without | 5 | Mendeley | 1650 | Best validation accuracy ResNet50: 91.51% |
M. and Goswami. [55] | ▪ Multi-class classification using CNN model and image sharpening process. | ◦ Inception-Resnet-v2 | With | 5 | OAI | 8260 | Accuracy: 91.03% |
Yunus et al. [17] | ▪Multi-class classification using a hybrid model and features extraction with LPB. | ◦ Darknet53, Alexnet, ROI localization: YOLOv2 ONNX, Final classification: SVM, KNN | With | 5 | OAI | 3795 | Accuracy = 90.6%, Precision = 85%, Sensitivity = 91% |
Nguyen Huu et al. [20] | ▪ Binary classification using the CNN model. | ◦ VGG16 | With | 2 | OAI | 2874 | Accuracy = 89% |
Yong et al. [56] | ▪ Multi-class classification using DL model and ordinal regression. | ◦ Ordinal regression module (ORM) applied on VGG, ResNet, DenseNet, ResNext, GoogLeNet, and Mobilenet. | Without | 5 | OAI | 4130 | Best model (DenseNet161): ACCMacro = 88.09% |
Abdo et al. [29] | ▪ Binary and multi-class classification using DL model. | ◦ DNN | Without | 2/3 | OAI/[6] | 9737/1650 | 3D-CNN (binary classification) (OAI-I/OAI-II): Accuracy = 85.50%/83.% |
Yildirim and Mutlu [57] | ▪ Multi-class classification using DL model and textural-based feature extraction. | ◦ Darknet53 combined with HOG and LBP | Without | 5 | Kaggle | 1650 | Accuracy: 83.6% |
Bhat and Suhasini [58] | ▪ Multi-class classification using a hybrid model. | ◦FFNN), Combination of Deep Belief Networks (DBN), RBM (Restricted Boltzmann Machine), and Multi SVM. | With | 5 | Local dataset | 126 | Average precision: FFNN: 76%, DBN-RBM: 83%, Multi SVM: 74.67% |
Wang Yu et al. [21] | ▪ Multi-class classification based on a two-step classification strategy with DL models and High-pass filter. | ◦ VGG, ResNet-50 | With | 5 | OAI | 8892 | Average accuracy: 81.41% |
Bayramoglu et al. [15] | ▪ Binary classification (OA, no OA) using traditional ML model and texture descriptors. | ◦ Logistic Regression combined with texture descriptors: FD, Shannon Entropy, Gabbor, Haralik, Ondelette, Tamura, and Local Binary Pattern (LBP) | With | 2 | OAI/MOST | 9012/3644 | AUC of 0.840 (OA: 0, 825, no OA: 0.852) AP = 0.804 (0.786, 0.820). |
Riad et al. [31] | ▪ Binary classification using traditional ML model and texture analysis approach. | ◦ KNN, SVM, Radial Basis Function (RBF) kernels | With | 2 | OAI | 688 | Accuracy 80.38% |
Bonakdari et al. [59] | ▪ KOA structural progressors using a traditional ML model combined with gender, serum biomarkers, age, BMI, and inflammatory factors. | ◦ KNN, Random Forest, Decision Tree, Extreme Learning Machine, and SVM. | Without | 2 | OAI/Napro-xen cohort | 677/44 | Accuracy: >80% using SVM |
Norman et al. [26] | ▪ Multi-class classification using DL model and demographic variables. | ◦ DenseNet after 15 epochs (DN15) and DenseNet with demographic input vector after 8 epochs: age, sex, and race (DenseNet-DEM-8) | With | 4 | OAI | 39,593 | Sensitivity/Specificity: No OA, Mild OA, Moderate OA, Severe OA: 83.7%/86.1%, 70.2%/83.8%, 68.9%/97.1%, and 86.0%/99.1%, respectively. |
Pi et al. [60] | ▪ Multi-class classification using an ensemble of DL models and mix voting algorithm. | ◦ DenseNet-161, EfficienNet-b5, EfficienNet-V2-s, RegNet-Y-8GF, ResNet-101, ResNex, WideResNet-50-2, ShuffleNet-V2- × 2-0 | Without | 5 | OAI | 8260 | Accuracy: 76.93% |
Kwon et al. [7] | ▪ Multi-class classification using DL model and gait analysis data. | ◦ Inception-ResNet-v2 using gait analysis for features extraction with NCA, support vector machine (SVM) classifier, and cubic kernel function. | Without | 5 | Local dataset | 215 | Sensitivity = 0.70, precision = 0.76, F1-score = 0.71 |
Wahyuningrum et al. [3] | ▪ Multi-class classification using DL model and LSTM. | ◦VGGNet, ResNet, DenseNet combined with LSTM | With | 5 | OAI | 5148 | Average accuracy (VGG16-LSTM) = 75.28% |
Nguyen et al. [13] | ▪ Binary classification using deep supervised and semi-supervised learning. | ◦ Deep Siamese Neural Network ResNet34 | With | 2 | OAI | 39,902 | Best result (BA): Semixup (SSL): 71.0 ± 0.8, SL without semixup: 70.9 ± 0.8%. |
Wang Yifan et al. [1] | ▪ Multi-class classification using a novel learning scheme, Estimating Label Confidence, and hybrid loss function. | ◦ ResNet34 and DenseNet121 | With | 5 | OAI | 8302 | Mean accuracy (DenseNet121\ResNet34) = 70.13%/68.32%. |
Tiulpin et al. [27] | ▪KOA prediction and progression using traditional ML model and clinical data. | ◦ Se-resnext101 32 × 4d/Se-resnet50/Inceptionv4. Final classification with GBM. | With | 3 | OAI/MOST | 4928/3918 | AP (CNN + clinical data) = 70%, Ap (se-resnext50 32 × 4 d) alone = 63%, and 68% when combined with clinical data |
Hu et al. [61] | ▪ Predicting KOA longitudinal progression during 4 years using adversarial evolving neural. | ◦ ResNet18, VGG19/ResNet50/Vit | Without | 5 | OAI | 3294 | Best accuracy: A-ENN(VGG19): 62.7% and 64.6%, 63.9%, 63.2%, 61.8%, and 60.2% for progression 12-month, 24-month, 36-month, and 48-month, respectively. |
Raisuddin et al. [45] | ▪ Multi-class classification using Deep Active Learning using Consistency Regularization (CR). | ◦ SSL deep Siamese VGG | Without | 5 | OAI | 9003 | Balanced accuracy = 64.13 ± 0.88 |
Cueva et al. [44] | ▪ Multi-class classification using computer-assisted diagnostic (CAD) based on the DL model. | ◦ Deep Siamese ResNet34 | With | 5 | [6]/Local dataset | 9182/376 | Average multi-class accuracy: 61.71%. |
4. Discussion
- i.
- Dataset preparation: The initial step in the process of selecting a suitable database is to guarantee an accurate diagnosis of knee osteoarthritis from radiographic images. This can be achieved by using mixed and balanced datasets with external validation, which is a first step towards reliable training. The classification of knee osteoarthritis can be simplified by merging the KL0 and KL1 grades into a single category [13,15,20,26,48]. Although KL1 has no real significance, it reduces the model’s accuracy as it is challenging to differentiate it from KL0.
- ii.
- Model selection: In addition to preparing a suitable database, the type of model chosen, hyperparameters, features extraction and selection, ROI localization, and JSN quantification are the most important factors in effectively improving the accuracy of the trained model. In light of these promising outcomes, the deployment of diverse iterations of YOLLO for real-time ROI detection and localization is becoming increasingly prevalent.
- iii.
- Visualization: The reliability of a diagnosis is no longer solely based on its accuracy; it is also based on the visualization of areas of deficiency. The orientation of new research towards a precise and justified diagnosis is analogous to that of the specialist doctors who base their final diagnosis on well-defined symptoms. This approach could therefore be used to confirm the degree of knee osteoarthritis by visualizing the key elements that have contributed to the identification of this condition. This would provide greater confidence in the developed approach.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial intelligence |
CNNs | Convolutional neural networks |
DCNN | Deep convolutional neural network |
KL | Kellgren and Lawrence |
KOA | Knee Osteoarthritis |
OAI | Osteoarthritis initiative |
TL | Transfer learning |
RELU | Linear rectified unit |
ROI | Region of interest |
RMSE | Root-Mean-Squared Error |
SGD | Stochastic gradient descent |
A-ENN | Adversarial evolving neural network |
DAL | Deep active learning |
LSTM | Long short-term memory |
ORM | Ordinal regression module |
HRNet | High-resolution network |
CBAM | Convolutional mass attention module |
KNN | K-Nearest neighbors |
DT | Decision tree |
RF | Random forest |
RQ | Research question |
EC | Eligibility criteria |
MAE | Mean absolute error |
SC | Screening criteria |
OARSI | Osteoarthritis research society international |
WOMAC | Western ontario and McMaster universities |
IKDC | International Knee Documentation Committee |
KOOS | Knee outcomes in osteoarthritis scores |
FFNN | Feed Forward Neural Network |
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Category 1 | Category 2 |
---|---|
Knee osteoarthritis | Automated |
Artificial intelligence | Detection |
Deep learning | Progression |
Machine learning | |
X-ray images | |
Radiographic images | |
Plain radiograph |
ID | Filtering Criteria |
---|---|
SC1 | The article must include at least the Title, Abstract, Source, Year, and Doi. |
SC2 | Must be in English. |
SC3 | Abstract must discuss the implementation of artificial intelligence in knee osteoarthritis diagnosis. |
SC4 | The article must have been published between January 2018 and May 2024. |
SC5 | Should not be just a review, survey, preprint, or roadmap. |
ID | Eligibility Criteria |
---|---|
EC1 | Does not address the implementation of artificial intelligence in the KOA diagnosis. |
EC2 | Is not based on X-ray images. |
EC3 | Full text not accessible. |
Dataset | Description | Labeled Images | Availability |
---|---|---|---|
OAI | ▪ Osteoarthritis Initiative (OAI) dataset: Multicenter, ten-year observational study of 4796 participants (Men and Women age = 45–79 years) | 4446 (KL and OARSI) | https://nda.nih.gov/oai/ (accessed on 5 January 2023) |
MOST | ▪ Multicenter Osteoarthritis Study (MOST): PA and lateral knees of 2026 participants (Men and Women average age = 50–79 years) | 2920(KL) | https://most.ucsf.edu (accessed on 5 January 2023) |
Gornale SS et al. [6] | ▪ Fixed-flexion digital knee X-ray images collected from various Karnataka hospitals, in India (known as Medical-expert) | 1650 (KL) | contact authors |
Kwon et al. [7] | ▪ 728 limbs with gait analysis data of 364 participants (men and women age ≥ 20 years) collected from Seoul National University Hospital (Korea) from 2013 to 2017 | 215 (KL) | contact authors |
Olsson et al. [8] | ▪ 6103 radiographic exams collected from Danderyd University Hospital, Stockholm, Sweden | 6403 (KL) | contact authors |
Schwartz et al. [9] | ▪ Fixed-flexion PA knee X-ray images collected from an outpatient clinic at a large academic joint arthroplasty practice, in Arizona, USA (from 2016 to 2019) | 4755 (IKDC) | contact authors |
Tiulpin et al. [10] | ▪ 81 subjects, collected from Oulu University Hospital, Finland | 370 (KL) | ClinicalTrials.gov, ID: NCT02937064 |
Yang et al. [11] | ▪ 2378 participants of the Chinese People’s Liberation Army General Hospital, Beijing, China, collected from January 2020 to January 2021 (men and women age ≥ 40) | 2579 (KL) | contact author |
References | Method | Dataset (X-ray Images) | Metric |
---|---|---|---|
Dalia et al. [18] |
| 400 X-ray images from OAI | Recall = 93%. |
Gornale S.S. et al. [6] |
| Local dataset: 1650 X-ray images | Not available |
Nagaraj and Jeyakumar [19] |
| Local dataset of 25 X-ray images | Best models: Center rectangle, Seed point selection: SP = 100%, SE = 0% |
Nguyen Huu et al. [20] |
| OAI | Train accuracy = 97%, mAP = 97%, IoU = 85% |
Bayramoglu et al. [15] |
| OAI + MOST | Not available |
Tiulpin et al. [10] |
| 370 X-ray images from Oulu University Hospital, Finland | Precision (resolution = 2.5) = 93.48% ± 0.44 |
Wang Yu et al. [21] |
| OAI | Accuracy = 96%, recall = 0.92 |
Wani and Saini [22] |
| OAI | Mean Jaccard index = 0.858, recall = 92.2 |
Zhang et al. [23] |
| OAI | IoU = 0.86 |
Yoon et al. [24] |
| OAI | Not available |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Touahema, S.; Zaimi, I.; Zrira, N.; Ngote, M.N. How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024. Appl. Sci. 2024, 14, 6333. https://doi.org/10.3390/app14146333
Touahema S, Zaimi I, Zrira N, Ngote MN. How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024. Applied Sciences. 2024; 14(14):6333. https://doi.org/10.3390/app14146333
Chicago/Turabian StyleTouahema, Said, Imane Zaimi, Nabila Zrira, and Mohamed Nabil Ngote. 2024. "How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024" Applied Sciences 14, no. 14: 6333. https://doi.org/10.3390/app14146333
APA StyleTouahema, S., Zaimi, I., Zrira, N., & Ngote, M. N. (2024). How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024. Applied Sciences, 14(14), 6333. https://doi.org/10.3390/app14146333