Osteoarthritis severity of the hip by computer-aided grading of radiographic images

Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis

Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.

Double acetabular wall--a misleading point for hip arthroplasty: an anatomical, radiological, clinical study

PURPOSE

Despite the great attention focused on cup positioning in primary total hip arthroplasty (PTHA), it is surprising to find so few studies that have dealt with cup placement. A common thwarting problem for correct cup placement during PTHA is the existence of osteophytes, which obscure the anatomical landmarks. In this study we aimed to evaluate the morphology of acetabular osteophyte formation in patients with osteoarthritis.

METHOD

We evaluated 276 patients with hip complaints, using plain X-rays and CT scans.

RESULTS

Of these patients, 57 underwent surgery. We developed a staging system for central osteophytes in hip osteoarthritis based on the radiographic and anatomical findings of our patients.

CONCLUSION

We recommend routine use of CT scans for patients scheduled for PTHA in order to assess the stage of osteophyte before surgery and, thus, reduce the risk of failure resulting from the interrupted acetabular landmarks.

A computer analysis method for correlating knee X-rays with continuous indicators

PURPOSE

To develop an image analysis method that can automatically find correlations between a set of plain radiographs and continuous clinical or physiological indicators.

METHODS

Knee X-rays taken from the Baltimore Longitudinal Study of Aging are used in this study. The computer analysis method is based on the WND-CHARM image feature set filtered by using the Pearson correlation of each feature with the continuous variable, and the estimated value is determined by a weighted nearest neighbor interpolation.

RESULTS

Experimental results using 300 radiographs show that the proposed method can correlate knee X-rays with physiological indicators such as sex, age, height, weight, and BMI. For instance, the Pearson correlation between the X-ray images and the height and weight were 0.59 and 0.62, respectively.

CONCLUSIONS

Using computer analysis, X-ray images can be correlated to continuous physiological variables that might not have a direct and straightforward link to the visual content of the radiograph. This approach of radiology image analysis can be used in population studies for detecting biomarkers and also in genome-wide association studies for studying the link between genes and anatomy.

Progression analysis and stage discovery in continuous physiological processes using image computing

We propose an image computing-based method for quantitative analysis of continuous physiological processes that can be sensed by medical imaging and demonstrate its application to the analysis of morphological alterations of the bone structure, which correlate with the progression of osteoarthritis (OA). The purpose of the analysis is to quantitatively estimate OA progression in a fashion that can assist in understanding the pathophysiology of the disease. Ultimately, the texture analysis will be able to provide an alternative OA scoring method, which can potentially reflect the progression of the disease in a more direct fashion compared to the existing clinically utilized classification schemes based on radiology. This method can be useful not just for studying the nature of OA, but also for developing and testing the effect of drugs and treatments. While in this paper we demonstrate the application of the method to osteoarthritis, its generality makes it suitable for the analysis of other progressive clinical conditions that can be diagnosed and prognosed by using medical imaging.

Early detection of radiographic knee osteoarthritis using computer-aided analysis

OBJECTIVE

To determine whether computer-based analysis can detect features predictive of osteoarthritis (OA) development in radiographically normal knees.

METHOD

A systematic computer-aided image analysis method weighted neighbor distances using a compound hierarchy of algorithms representing morphology (WND-CHARM) was used to analyze pairs of weight-bearing knee X-rays. Initial X-rays were all scored as normal Kellgren-Lawrence (KL) grade 0, and on follow-up approximately 20 years later either developed OA (defined as KL grade=2) or remained normal.

RESULTS

The computer-aided method predicted whether a knee would change from KL grade 0 to grade 3 with 72% accuracy (P<0.00001), and to grade 2 with 62% accuracy (P<0.01). Although a large part of the predictive signal comes from the image tiles that contained the joint, the region adjacent to the tibial spines provided the strongest predictive signal.

CONCLUSION

Radiographic features detectable using a computer-aided image analysis method can predict the future development of radiographic knee OA.

Knee x-ray image analysis method for automated detection of osteoarthritis

We describe a method for automated detection of radiographic osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence (KL) classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades (normal, doubtful, minimal, and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org.

Biometric identification using knee X-rays

Identification of people often makes use of unique features of the face, fingerprints and retina. Beyond this, a similar identifying process can be applied to internal parts of the body that are not visible to the unaided eye. Here we show that knee X-rays can be used for the identification of individual persons. The image analysis method is based on the wnd-charm algorithm, which has been found effective for the diagnosis of clinical conditions of knee joints. Experimental results show that the rank-10 identification accuracy using a dataset of 425 individuals is ~56%, and the rank-1 accuracy is ~34%. The dataset contained knee X-rays taken several years apart from each other, showing that the identifiable features correspond to specific persons, rather than the present clinical condition of the joint.