Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis

Bayramoglu, Neslihan; Nieminen, Miika T.; Saarakkala, Simo

doi:10.1016/j.ijmedinf.2021.104627

Cited by 36 publications

(25 citation statements)

References 28 publications

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“…Joint space narrowing (JSN) [18] Usually asymmetric, commonly happens at medial tibiofemoral and patellofemoral compartments Osteophyte formation [18] Formation of bone spurs Cyst/geode formation [18] Formation of fluid-filled cavities when synovial fluid is forced into subchondral bone Subchondral sclerosis [18] Increased bone density or thickening of bone when bone grows in the area originally belongs to cartilage Coronal tibiofemoral subluxation [18][19][20][21] Misaligned joint surface, causing altered shape of femoral condyles and tibial plateau Patellofemoral joint was detected by Bayramoglu et al [44] from knee X-ray images. First, the detection of patella was performed using BoneFinder ® software that works based on the random forest regression voting approach.…”

Section: Oa Features Descriptionmentioning

confidence: 99%

“…Cheung et al [49] have tested the segmentation ability of four models, namely CUMed-Vision, U-Net, DeepLabv3, and Res-U-Net. All four models were used to segment distal (ii) Detected patellofemoral joints on X-ray images [44] (ii) Quantification of qualitative OA features (ii) Local binary pattern [41,42,44] (iii) Random forest regression voting [44] (iv) Fully convolutional neural network [45,46] (iii) Detected cartilage X-ray images [41,42] (v) YOLOv2 network [46] Segmentation of knee joint components Diagnosis (i) Segmented knee cartilage from 2D ultrasound images [27,28] (i) Area measurement (i) Locally statistical level set method [28] (ii) Segmented knee cartilage from 2D MRI images [47] (ii) Volumetric measurement (ii) Automatic seed point detection [48] (iii) Segmented cartilage and meniscus from MRI images [29] (iii) Joint shape measurement (iii) Random walker [27] (iv) Segmented subchondral bone from multiple 2D MRI images [48] (iv) Quantification of measurable OA features (iv) Watershed (v) Segmented distal femur and proximal tibia from X-ray images [49] (v) Reconstruction of 3D knee joint model for simulation and joint loading study (v) Graph cut [27] (vi) Calculated joint space width on X-ray images [49] (vi) Finite element analysis (vi) Support vector machine classifier [43] (vii) Segmented femoral condyle cartilage from ultrasound images [50] (vii) Utilization of statistical and computational models (vii) Decision tree classifier [41,42] (viii) Segmented bones (femur and tibia) and cartilages (femoral and tibial cartilages) on MRI images [51] (viii) Active contour algorithm [42] (ix) Segmented knee bones, cartilage, and muscle tissues on MRI images [52,53] (ix) U-Net [29,47,…”

Section: Segmentation Of Knee Jointmentioning

confidence: 99%

“…Prognosis (i) Estimated future knee OA incidence (i) Risk stratification (i) Random forest classifier [64,65] (a) 30 months [64] (ii) Selection of data from suitable time points to indicate short-term and long-term OA changes (ii) Logistic regression classifier [66][67][68][69] (b) 48 months [70] (iii) OA feature change detection (iii) Support vector machine classifier [66] (c) 8 years (iv) Discovery of pain-associated imaging features (iv) XGBoost model [49] (ii) Predicted medial JSN progression [66] (v) Multilayer perceptron [67,71] (iii) Predicted radiographic joint space loss progression [67] (vi) LASSO regression [39] (iv) Predicted knee OA onset and knee OA deterioration [71] (vii) Artificial neural network [70] (v) Discriminated between progressors and nonprogressors [72] (viii) Deep CNN [44,72,73] (vi) Predicted pain [73,74] (ix) DenseNet CNN [68] (vii) Predicted risk of progressive pain and structural change [65] (x) Gradient boosting machine [44,70] (viii) Predicted total knee replacement (TKR) incidence [68,75] (xi) Duo classifier [65] (xii) DeepSurv [75] (xiii) Dynamic functional mixedeffects model [54] femur and proximal tibia. Res-U-Net gave the best segmentation outcome with the highest mean intersection over union score at 0.989.…”

Section: Without Interventionmentioning

confidence: 99%

“…CNN with HOG coupled with K-nearest neighbour classifier produced the best accuracy at 97.14% for all KL grades. Bayramoglu et al [44] exemplified automated diagnosis of patellofemoral OA using gradient boosting machine (GBM) and deep CNN.…”

Section: Classification Of Knee Oa Severitymentioning

confidence: 99%

See 3 more Smart Citations

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

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Section: Oa Features Descriptionmentioning

confidence: 99%

Section: Segmentation Of Knee Jointmentioning

confidence: 99%

Section: Without Interventionmentioning

confidence: 99%

Section: Classification Of Knee Oa Severitymentioning

confidence: 99%

See 2 more Smart Citations

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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“…A recent study proposed by Bayramoglu et al [95] tackled the potential of analyzing patellar bone texture to predict patellofemoral osteoarthritis. Using knee lateral view radiographs, a ML model, and DCNNs, the authors obtained promising results, demonstrating that the analyzed texture features contained useful information of the patellar bone structure and could be used as additional imaging biomarkers in osteoarthritis diagnostics.…”

Section: Orthopedics and Rheumatologymentioning

confidence: 99%

Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

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Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network

Melekoodappattu,

Kandambeth Puthiyapurayil,

Vylala

et al. 2023

Cell Biochemistry & Function

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An advanced approach that capitalizes on the synergies between multimodal feature fusion and the dual‐path network is presented in this manuscript. Our proposed methodology harnesses a combination of potent techniques, merging the benefits of nonlinear mapping and expansive perception. The foundation of our methodology lies in leveraging well‐established pretrained models, namely EfficientNet‐B7, ResNet‐152, and a meticulously crafted custom convolutional neural network (CNN), to effectively extract salient features from the data. These models are combined in a two‐stage ensemble approach. We employ maximum variance unfolding (MVU) to select the most relevant attributes from the extracted features. In this study, we propose a hybrid approach that integrates a generative adversarial network and Neural Autoregressive Distribution Estimation (NADE‐K) with a CNN. The resulting two‐stage ensemble hybrid CNN model achieves an accuracy of 99.63%. The implementation of the two‐stage ensemble hybrid CNN with MVU demonstrates significant improvements in brain tumor classification.

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Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis

Cited by 36 publications

References 28 publications

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Clinical Applications of Artificial Intelligence—An Updated Overview

Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network

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