Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs

PURPOSE

Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (KL) classifications. However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs from CT images.

METHODS

Automatic grading of the hip OA severity was performed using deep learning-based models. The models were trained to predict the disease grade using two grading schemes, i.e., predicting the Crowe and KL grades separately, and predicting a new ordinal label combining both grades and representing the disease progression of hip OA. The models were trained in classification and regression settings. In addition, the model uncertainty was estimated and validated as a predictor of classification accuracy. The models were trained and validated on a database of 197 hip OA patients, and externally validated on 52 patients. The model accuracy was evaluated using exact class accuracy (ECA), one-neighbor class accuracy (ONCA), and balanced accuracy.

RESULTS

The deep learning models produced a comparable accuracy of approximately 0.65 (ECA) and 0.95 (ONCA) in the classification and regression settings. The model uncertainty was significantly larger in cases with large classification errors ( P < 6 e - 3 ).

CONCLUSIONS

In this study, an automatic approach for grading hip OA severity from CT images was developed. The models have shown comparable performance with high ONCA, which facilitates automated grading in large-scale CT databases and indicates the potential for further disease progression analysis. Classification accuracy was correlated with the model uncertainty, which would allow for the prediction of classification errors. The code will be made publicly available at https://github.com/NAIST-ICB/HipOA-Grading .

Anterior and posterior hip osteoarthritis: prevalence and potential value of CT compared to radiographs

PURPOSE

To determine the added value of computed tomography (CT) to identify severe hip osteoarthritis (OA).

MATERIALS AND METHODS

A retrospective query of all cases of hip or knee arthroplasty planning CTs between January 2018 and March 2022 was performed. Age, sex, and symptoms were collected from the medical record. CTs were evaluated for the degree of osteoarthritis and classified using an adapted Kellgren-Lawrence (KL) grading system in the anterior, posterior, superior, and superomedial hip. Frontal hip or pelvis radiographs within 1 year of the CT were also graded.

RESULTS

There were 265 eligible hips in 178 subjects, age 66 ± 11 (range 31-93) years, with 85/178 (48%) males and 93/178 (52%) females, and 127/265 (48%) right and 138/265 (52%) left hips. The posterior hip joint was the most common location for grade 2/3 OA (20%), followed by superior hip joint (14%). Anterior or posterior grade 2/3 OA occurred concurrently with superior or superomedial grade 2/3 OA in 32/68 (47%) of hips. Grade 2/3 OA was detected on CT more commonly than on XR both in the superior (14 vs 8.6%, P = 0.0016) and superomedial (8.7 vs 4.8%, P = 0.016) hip joint. Of the 71 symptomatic hips, 22 (31%) hips demonstrated either anterior and/or posterior grade 2/3 OA on CT, and 9 (9/22, 41%) of these hips had superior or superomedial grade 0/1 OA.

CONCLUSION

CT may be warranted when the patient has pain suggestive of osteoarthritis not detected on radiographs.

Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis

OBJECTIVES

As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare them with clinicians' performance.

MATERIALS AND METHODS

A search in PubMed and Scopus was performed to find studies published up to April 2022 that evaluated and/or validated an AI algorithm for the detection or classification of OA. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the involved joint and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Prediction Model Study Risk of Bias Assessment Tool reporting guidelines.

RESULTS

Of the 61 studies included, 27 studies with 91 contingency tables provided sufficient data to enter the meta-analysis. The pooled sensitivities for AI algorithms and clinicians on internal validation test sets were 88% (95% confidence interval [CI]: 86,91) and 80% (95% CI: 68,88) and pooled specificities were 81% (95% CI: 75,85) and 79% (95% CI: 80,85), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 94% (95% CI: 90,97) and 91% (95% CI: 77,97), respectively.

CONCLUSION

Although the results of this meta-analysis should be interpreted with caution due to the potential pitfalls in the included studies, the promising role of AI as a diagnostic adjunct to radiologists is indisputable.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis. Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05). Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.

Systematic review of computed tomography parameters used for the assessment of subchondral bone in osteoarthritis

OBJECTIVE

To systematically review the published parameters for the assessment of subchondral bone in human osteoarthritis (OA) using computed tomography (CT) and gain an overview of current practices and standards.

DESIGN

A literature search of Medline, Embase and Cochrane Library databases was performed with search strategies tailored to each database (search from 2010 to January 2023). The search results were screened independently by two reviewers against pre-determined inclusion and exclusion criteria. Studies were deemed eligible if conducted in vivo/ex vivo in human adults (>18 years) using any type of CT to assess subchondral bone in OA. Extracted data from eligible studies were compiled in a qualitative summary and formal narrative synthesis.

RESULTS

This analysis included 202 studies. Four groups of CT modalities were identified to have been used for subchondral bone assessment in OA across nine anatomical locations. Subchondral bone parameters measuring similar features of OA were combined in six categories: (i) microstructure, (ii) bone adaptation, (iii) gross morphology (iv) mineralisation, (v) joint space, and (vi) mechanical properties.

CONCLUSIONS

Clinically meaningful parameter categories were identified as well as categories with the potential to become relevant in the clinical field. Furthermore, we stress the importance of quantification of parameters to improve their sensitivity and reliability for the evaluation of OA disease progression and the need for standardised measurement methods to improve their clinical value.