Artificial Intelligence driven handheld echo to detect hip dysplasia
One out of 7 children that have developmental hip dysplasia (DDH) will not be correctly diagnosed since the diagnosis is based on family history, delivery complications, and physical examination. By imaging the hips with AI driven handheld echo that requires no echo experience and automatically generates the diagnosis, we will boost the detection rate.
DDH affects 3% of infants under six months and alters hip biomechanics, placing excessive strain on the articular cartilage and therefore leading to early osteoarthritis. DDH causes 16% of total hip replacements in young individuals (<50 years old). Early detection of DDH allows for effective treatment and is performed at the Child Health Care (CHC) centers. Screening is based on identifying risk factors (physical examination, breech position, family history) and when an infant is deemed at risk, it is referred to a hospital for an ultrasound. Currently, approximately one out of seven infants with DDH remains undiagnosed at an early stage. The objective of this project is to enhance early detection of DDH.
Ultrasonographic examination stands as the global gold standard for diagnosing DDH. Since a well-trained sonographer is crucial for accurate ultrasound-based classification of DDH, diagnosis cannot be performed at the CHC center because it requires months of training. Within this project, artificial intelligence will used in combination with a smartphone and an ultrasound probe to enable a guided ultrasound acquisition of the infant hip to establish a screening system at the CHC centers, provide appropriate care at the right place, minimize interrater variances, enhance sensitivity, and contribute to cost reduction.
Successful introduction of ultrasound at the CHC center will improve the sensitivity of early detection of DDH and therefore allows effective early treatment. This will improve quality of life for 14% of infants with DDH which are currently not timely diagnosed.
