Automatically diagnosing hip conditions from x-rays using landmark detection
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...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
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IEEE
2021
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| Summary: | 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. |
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