An interventional study of Artificial intelligence augmented ultrasound in Developmental Dysplasia of Hip, sponsored by Murdoch Childrens Research Institute. Not yet recruiting. Per ClinicalTrials.gov, last updated 2026-10-07.
Sponsored by Murdoch Childrens Research Institute · Not applicable, Interventional, and Screening
The goal of this trial is to pilot a portable ultrasound device that uses artificial intelligence to screen for hip dysplasia. Researchers will gather data to understand the feasibility of performing a larger trial in birthing hospitals. It will also seek to collect initial data on how well the scan compares to the standard hip screening performed soon after birth.
Participants will:
A recent study by our team showed that at The Royal Women's Hospital, Melbourne, screening at birth using the standard combination of neonatal hip examination and risk-based referral for ultrasound failed to detect 52% (n=100) of cases of Developmental Dysplasia of the Hip (DDH) and that 98.5% (n=2,637) of infants undergoing a screening ultrasound, due to perceived increased risk, do not have DDH. Further, this research team replicated in a regional setting at University Hospital, Geelong in Victoria (n=1,207), that 55.6% of cases of DDH were missed, and of those sent for diagnostic ultrasound scans 92.5% did not have DDH (unpublished data). Together, this means many infants are being scanned, but an unacceptably high proportion of cases are still being missed.
Late detected dysplasia is often resistant to conservative treatment. This form of dysplasia is unpredictable in its presentation and may require surgical intervention to obtain a contained and stable joint. Such patients are at a higher risk of developing degenerative hip disease in early adult life and can suffer considerable disability, often failing to reach their full potential. Thus, many initiatives have been taken to improve our current screening programs, including clinical education programs, streamlined access, and incorporation of hip examinations into child health assessments. However, none of these initiatives has effectively reduced the rate of late detection of dysplasia. Despite selective screening protocols being in place, the incidence of late-diagnosed DDH has increased in South Australia, showing a significant rise from 0.22 per 1000 live births (1988-2003) to 0.77 per 1000 live births (2003-2009).
One part of the solution is optimising screening protocols for DDH in existing care models. A possible solution is utilising artificial intelligence to aid in screening decisions. One such new tool is the Exo Iris, a portable ultrasound device supported by real-time AI-augmented analysis to screen for hip dysplasia. Importantly, use of this device does not require extensive training and could be performed by midwives or paediatricians in standard neonate hip examinations. Initial work has shown that AI could successfully identify the standard plane, make measurements, and classify the hip as normal or abnormal. Scans are simple to conduct, add little time to the overall consultation and are non-invasive without the use of ionising radiation. Importantly, non-experts can easily be trained to use the technology; they are cost-effective and can be used in any clinical environment connected to a standard tablet.
Initial Canadian data suggests that DDH detection rates suggests that artificial intelligence (AI) analysis for hip dysplasia are on par with orthopaedic specialists. Of the infants flagged for follow-up there were 6 subsequently referred to specialist clinics after repeat scan and all were treated for DDH (5 harnessed, 1 surgical intervention). Of these the six infants detected, only two presented with well documented risk factors for increased risk of DDH (female sex, Indigenous, breech, family history), which may not have been detected without initial portable ultrasound screening.
Further to this, Retuve is a new open-source software tool that uses AI-analysis to measures standard indices on hip ultrasound images collected from any manufacturer's ultrasound probe, which can help users make hip screening decisions. This platform generates novel imaging parameters beyond current standards that may also be helpful in further understanding undetected late presentations. However, as this is a relatively new tool there has been little research to fully evaluate its performance and its potential utility as a screening tool.
There is clear scope for this technology to revolutionise screening in Australia by reducing the number of cases of DDH missed and the number of costly conventional ultrasound scans; however, there is limited data assessing the feasibility and accuracy of the new AI analysis strategies for DDH screening in the context of neonatal screening. To date, although the Exo Iris and embedded hip AI software is indicated for use in infants 0-6 months there is a dearth of data examining neonates, and no data exists evaluating it in the context of selective neonatal screening.
This study will provide foundational pilot evidence on the accuracy and feasibility of using the Exo Iris probe and an alternate AI software platform as part of standard neonatal screening. In addition to this, long-term follow-up as part of the VicHip parent study will aid in understanding potential parameters associated with missed presentations. To do this, 100 infants will be recruited from Monash Medical Centre, Victoria, Australia and will undergo both the AI-ultrasound scan that will be analysed both by the embedded software and Retuve and results from the AI analyses will be compared to a 6-week full diagnostic ultrasound.
107 studies on the registry are indexed under Hip Dislocation; 36 are open to participants now.
This study's planned enrollment of 100 is above the median of 86 across 64 interventional studies indexed under Hip Dislocation.
Browse Hip Dislocation studies →Murdoch Childrens Research Institute is the lead sponsor of 99 studies on the registry; 34 are open to participants now.
Of its 5 completed or terminated interventional studies of FDA-regulated products, 3 (60%) have results posted.
Counted across the registry records on this site, refreshed daily.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
All infants will undergo the AI-augmented ultrasound measure
Device: Artificial intelligence augmented ultrasound
The hip ultrasound is performed using a handheld device (Exo Iris) that is a pocket-sized ultrasound probe and is run through an application on an IoS (Apple mobile) operation system. A real-time algorithm detects and records the anatomical landmarks.
Feasibility of the AI-ultrasound method as determined by a study-specific questionnaire administered to care givers at Day 1
Caregiver perspectives will be captured via a study-specific questionnaire administered at Day 1 and this will enable determination of the feasibility of the AI-ultrasound method.
Time frame: Day 1
Number of infants unable to be scanned with the AI ultrasound
The proportion of infants unable to be successfully scanned with the AI-ultrasound will be calculated.
Time frame: Day 1
Reasons for failure to obtain AI ultrasound scan as determined by the performing research assistant
The reasons why infants were unable to be scanned as determined by the research assistant performing the AI scan will be documented as follows: Unsettled baby, technical failure, body habitus or other.
Time frame: Day 1
Proportion of infants lost to follow-up between the AI-ultrasound and 6-week (corrected) diagnostic scan
The proportion of infants that had an initial AI scan at Day 1 but did not return for a scan at Week 6 will be calculated.
Time frame: Week 6
Specificity of AI-ultrasound device as determined by comparison of the geometric measures (femoral head coverage and alpha angle) and expert reviews between the Day 1 and Week 6 scans
AI-ultrasound results will be compared to 6-week diagnostic ultrasound imaging to calculate specificity (\[number of true negative cases detected/(number of false positive cases detected + number of true negatives cases detected\] X 100). Hips will be classified according to the AI-recommendations, "unremarkable" i.e. no follow-up required or "follow-up recommended" to define negative and positive cases; those that receive an "unremarkable" classification will be defined as negative cases, and those that receive a "follow-up recommended" scan will be considered as positive cases for the reliability calculations. Diagnosis will be defined from the 6-week ultrasound scan and will be defined by both expert conclusion and geometric measures (femoral head coverage and alpha angle). Calculations will be performed at both the hip and individual level.
Time frame: Day 1, Week 6
Sensitivity of AI-ultrasound measure determined by comparison of the geometric measures (femoral head coverage and alpha angle) and expert reviews between the Day 1 and Week 6 scans
AI-ultrasound results will be compared to 6-week diagnostic ultrasound imaging to calculate sensitivity (\[number of true positive cases detected/(number of true positive cases detected + number of false negative cases detected)\] X 100). Hips will be classified according to the AI-recommendations, "unremarkable" i.e. no follow-up required or "follow-up recommended" to define negative and positive cases; those that receive an "unremarkable" classification will be defined as negative cases, and those that receive a "follow-up recommended" scan will be considered as positive cases for the reliability calculations. Diagnosis will be defined from the 6-week ultrasound scan and will be defined by both expert conclusion and geometric measures (femoral head coverage and alpha angle). Calculations will be performed at both the hip and individual level.
Time frame: Day 1, Week 6
The correlation between the alpha angle degree as reported by the AI-ultrasound analysis and expert analysis of the 6-week diagnostic ultrasound scan
Dependent on the distribution of the data Pearsons or Spearman's Rho correlation coefficients will be reported.
Time frame: Day 1, Week 6
The correlation between the percentage femoral head coverage as reported by the AI-ultrasound analysis and expert analysis of the 6-week diagnostic ultrasound scan
Dependent on the distribution of the data Pearsons or Spearman's Rho correlation coefficients will be reported.
Time frame: Day 1, Week 6
Difference in specificity of standard neonatal screening and the AI-ultrasound scan to discriminate for DDH
The specificity of standard neonatal screening and the AI-ultrasound screening will be compared.
Time frame: Day 1
Difference in specificity of Retuve and the AI-ultrasound scan to discriminate for DDH
The specificity of an alternate AI-analysis platform will be compared to the AI-ultrasound scan results.
Time frame: Day 1
No study locations are listed for this record.
Plan to share: Yes — The de-identified data set collected for this analysis of this trial will be available 12 months after publication of the primary outcome. The study protocol and informed consent form will also be available.
Supporting information: Study protocol, Informed consent form
Time frame: 12 months after the publication of data until the data is deleted (at minimum 1 year after the youngest participant turns 25 years of age).
Access criteria: The data must be obtained from the Murdoch Children's Research Institute. Prior to releasing any data the following are required: a data access agreement must be signed between relevant parties, the Study Management Group must see and approve the data analysis plan describing how the data will be analysed, there must be an agreement around appropriate acknowledgment and any additional costs involved must be covered. Should the Study Management Group be unavailable, this role is delegated to the Murdoch Children's Research Institute. Data will only be shared with a recognized research organisation which has approved the proposed analysis plan.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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Murdoch Childrens Research Institute