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Developmental dysplasia of the hip (DDH) encompasses a broad spectrum of abnormal hip development during infancy and early development. The definition encompasses a wide range of severity, from mild acetabular dysplasia without hip dislocation to frank hip dislocation, and is the most common pediatric hip disorder, affecting 1 - 3% of all infants. When left untreated or missed during early screening, DDH can lead to debilitating long term complications, including early-onset osteoarthritis of the hip, pain, limping, and the need for a total hip replacement in early adulthood.
Imaging plays a vital role in screening, initial diagnosis and further workup of DDH. Different screening strategies have been described, including clinical examination alone, selective ultrasound screening and universal ultrasound screening. Accurate diagnosis and classification of DDH requires expertise of a specialized pediatric orthopedic surgeon and pediatric radiologist. Nationwide, a significant shortage of well-qualified pediatric radiologists exists, as there are approximately 950 pediatric radiologists nationally which comprise 3.5% of practicing US radiologists, limiting access to care.. To be adopted in clinical practice, Artificial Intelligence (AI) applications must address unmet needs or improve on existing solutions. Given the dearth of specialists, AI techniques show promise to be incorporated in the clinical workflow of studies performed for DDH diagnosis and screening, or as an add-on to the radiologist’s interpretation for DDH studies.
A 10-week-old female infant with a history of breech presentation at birth, and equivocal findings of hip instability on physical examination of the hip. Ultrasound of the hip is performed to evaluate developmental dysplasia of the hips. An algorithm evaluates the images, classifies the hip, and sends a message to the PACS.
The images are obtained from the ultrasound modality and sent to PACS and the AI algorithm or model. The algorithm or model analyzes the images, and detects and classifies the abnormality, and sends a message to the PACS.
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Procedure(s) |
US, Hips |
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Sex at birth |
Male, Female |
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Age |
4 weeks to 6 months |
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Indication |
Evaluation of symptomatic child, screening, follow up |
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Risk factors |
Breech presentation, family history of parent and/or sibling with DDH, neuromuscular pathologies, treatment monitoring, oligohydramnios, asymmetric skin folds in the medial thigh |
DICOM Study
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Procedure |
US, both hips |
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Views |
6 views, coronal neutral, coronal flexion, transverse flexion in adduction, transverse flexion in abduction, coronal flexion posterior acetabulum without and with stress |
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Data Type |
DICOM |
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Modality |
US |
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Body Region |
Hip |
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Anatomic Focus |
Hip Joint |
Detection of femoral head position
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RadElement ID |
RDE1258 |
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Definition |
Detection of femoral head position |
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Data Type |
Categorical |
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Value Set |
|
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Units |
N/A |
Stability
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RadElement ID |
RDE1259 |
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Definition |
Stability |
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Data Type |
Categorical |
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Value Set |
|
|
Units |
N/A |
Acetabular Morphology
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RadElement ID |
RDE1260 |
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Definition |
Acetabular Morphology |
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Data Type |
Categorical |
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Value Set |
|
|
Units |
N/A |
Alpha angle
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RadElement ID |
RDE1261 |
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Definition |
Alpha angle |
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Data Type |
Numerical |
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Value Set |
N/A |
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Units |
Degrees |
Beta angle
|
RadElement ID |
RDE1262 |
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Definition |
Beta angle |
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Data Type |
Numerical |
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Value Set |
N/A |
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Units |
Degrees |
Femoral head coverage
|
RadElement ID |
RDE1263 |
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Definition |
Detection of femoral head coverage |
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Data Type |
Categorical |
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Value Set |
|
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Units |
N/A |
Algorithms that utilize 3D ultrasound data obtained in rest and stress, instead of 2D images in coronal and transverse planes and algorithms that perform automated acetabular segmentation in addition to classifying hip abnormalities. This approach will significantly eliminate the need for dependence on operator expertise for image acquisition.
Deployment of autonomous AI-directed ultrasound system for imaging and classification of DDH.