Study: Detecting Hip Osteoarthritis on Clinical CT
A Study was conducted to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). Researchers developed and compared deep learning models to detect hip osteoarthritis on clinical CT.

The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images. The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher.

Results:
--Amongst the 5 models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images.

--Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve of 0.93.

--Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89.

Conclusively, rHOA can be detected using CT-based summation images that look like radiography. In addition, a reliable DL model can be optimized by merging CT-AP and X-ray images in the lack of extensive training data.

Source: https://link.springer.com/article/10.1007/s00198-021-06130-y
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