Automatically diagnosing hip conditions from x-rays using landmark detection

Conference item

When patients present with symptoms of hip pain a clinician might diagnose a condition called femoroacetabular impingement (FAI), where the ball and socket of the hip joint rub together during movement. To diagnose FAI a doctor inspects an x-ray, and records the angles between certain key points in the image. If the angles are `too big' then FAI is diagnosed. We anticipate that these key points can be located in an x-ray using deep learning and thus the angles measured and FAI diagnosed automatically. In this paper we deploy a stacked hourglass network to automatically locate key-points in hip x-rays, which we then use to automatically diagnose FAI in a patient. On a test set of 112 hips our algorithm diagnoses cam impingement, one of two types of FAI, correctly 90% of the time. To our knowledge this is the first time any kind of FAI has been automatically diagnosed.

Authors: McCouat, J; Voiculescu, I; Glyn-Jones, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
• Pages: 179-182
• Publication date: 2021-05-25
• Acceptance date: 2021-01-08
• Event title: International Symposium on Biomedical Imaging (ISBI)
• Event location: Nice, France
• Event website: https://biomedicalimaging.org/2021/
• Event start date: 2021-04-13
• Event end date: 2021-04-16
• DOI: 10.1109/ISBI48211.2021.9433959
• EISSN: 1945-8452
• ISSN: 1945-7928
• EISBN: 978-1-6654-1246-9
• ISBN: 978-1-6654-2947-4
• Language: English
• Keywords: FFR