Developmental Dysplasia of HipNCT06647225
Using Artificial Intelligence to Screen for Hip Dysplasia
A study testing Artificial intelligence augmented ultrasound in people with Developmental Dysplasia of Hip, run by Murdoch Childrens Research Institute at 1 site in Australia.
Trial summary
The goal of this clinical trial is to learn if an ultrasound scan using artificial intelligence can accurately screen for hip dysplasia. Researchers will compare the artificial intelligence ultrasound results to the standard ultrasound measures to see if the artificial intelligence ultrasound scan can accurately… Read more about this study
- Who is it for?
- Ages 4 to 20 weeksHealthy volunteers can take part
- Conditions the study is for
- Developmental Dysplasia of Hip
- What is being tested?
- Artificial intelligence augmented ultrasound
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 240 participants
- Where
- 1 siteAustralia
- Study dates
- 6 Dec 2024to Nov 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 4 to 20 weeks
- All sexes
- Healthy volunteers accepted
- Child
Who may be able to join
- Enrolled in the VicHip study
- Is 4-20 weeks of age at enrolment
- Is attending The Royal Children's Hospital for the purpose of the potential diagnosis of DDH
- Has a diagnostic (standard) hip ultrasound on the day of their out-patient appointment
- Has a legally acceptable representative capable of understanding the informed consent document and providing consent on the participant's behalf.
Who cannot join
- Participants will be excluded from enrolment if:
- They are currently receiving treatment for DDH
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Melissa Formosa contact
Australia1 site, 1 recruiting
- Recruiting
Royal Children's Hospital
Parkville, Victoria, 3052
Natalie K Hyde, BBiomedSc, PhD, +61 3 9936 6246, natalie.hyde@mcri.edu.au
About this study
Official title: Artificial Intelligence Augmented Ultrasound for Developmental Dysplasia of the Hip: a Validity Study
The goal of this clinical trial is to learn if an ultrasound scan using artificial intelligence can accurately screen for hip dysplasia. Researchers will compare the artificial intelligence ultrasound results to the standard ultrasound measures to see if the artificial intelligence ultrasound scan can accurately screen for hip dysplasia.
It will also seek to understand how parents feel about their children undergoing this scan.
Participants will:
- Have an additional ultrasound performed on their child at their scheduled outpatient's appointment for hip dysplasia
- Complete a short questionnaire about the experience of having the measurement performed on their child
Read the detailed description
Initial screening for Developmental Dysplasia of the Hip (DDH) in Australia is performed most often by general practitioners and paediatricians shortly after birth and by maternal child health care nurses (MCHN) throughout the first year of life. These physical examinations consist of the Ortolani and Barlow tests and the examination of the thigh and gluteal creases. A recent meta-analysis reported the sensitivity of these tests as 36%, which indicates there is potential for a large proportion of cases to go undetected when solely relying on these examinations. Moreover, there currently are no formalised processes by which standards of practice are taught, assessed, or maintained. Thus, there is a clear need for a less operator-dependent screening protocol that can be performed within the current models of infant care. While some countries utilise universal ultrasound screening, this too is limited by access to care, as devices are not portable and thus cannot be used in current care models. Furthermore, it requires a specialist operator, substantially increasing cost. The screening program's limited nature, combined with the need for more consensus among international healthcare providers regarding the best method for managing DDH, has produced highly mixed clinical practices.
One part of the solution is optimising screening protocols for DDH in existing care models. Each state in Australia has established MCHN care protocols that provide access care for young children. While physical screening for DDH in these visits is standard practice, there remains considerable scope for improvement in the accuracy and reliability of these screening methods. Selective screening relies on several clinical associations with DDH to identify which patients receive ultrasound screening. Still, it has been shown to detect only 50% of infants with dysplasia. The MCHN screening program relies on clinical examination alone to detect dysplasia, an inferior identification method. Universal screening has a higher rate of detection of dysplasia but is expensive, single point in time (so misses the development of dysplasia) and results in higher levels of treatment.
A possible solution is portable artificial intelligence (AI)-augmented ultrasound. Recently technology has been developed to support a portable ultrasound device to screen DDH that uses AI-enabled technology to screen for DDH rapidly and accurately. Prior data has demonstrated that physicians and nurses could operate the device following training from expert sonographers. With its low-cost and ease of operation (with simple training) by healthcare providers such as MCHNs, it could significantly augment the physical screening. Thus, there is clear potential for an affordable, repeatable, and accessible screening methodology to be translated into clinical care. Initial Canadian data is promising. Pilot data suggests that DDH detection rates with this technology is on par with the detection rates of orthopaedic specialists. However, as this study was performed in a community setting and only those participants referred to orthopaedic clinics had a standard ultrasound measure performed, this pilot was unable to compare this screening technique with current gold standard diagnostic measures across the whole cohort, nor determine device sensitivity or predictive values. To demonstrate that this technology is fit for purpose, it is imperative that the rate of false negatives is also understood, as this is what will lead to late presentation, - which is what screening ultimately endeavours to prevent. Moreover, in an Australian context an important consideration in a wider roll-out is whether this technology would be accepted for uptake by clinicians and parents.
The proposed project will seek to gather pilot data to assess the validity and feasibility of this technology within a population of infants aged 4-20 weeks flagged at risk for DDH and referred to the Royal Children's Hospital. This will enable the recruitment of a sufficient number of cases of DDH to determine the sensitivity of the device. While the sensitivity and specificity of the device in this at-risk population may not be generalizable to the wider community the information gathered will then inform and refine a larger study of this technology in a community setting such as tertiary (birthing hospitals) and primary (MCHN clinics) care. If it can be demonstrated that it is feasible to implement this technology into existing care models, there is clear scope for this technology to revolutionize DDH screening. Thus, this project seeks to determine how well the device performs (sensitivity, specificity and predictive value) and the the clinical acceptability of this measure within the patient population.
- Conditions
- Developmental Dysplasia of Hip
- Being tested
- Artificial intelligence augmented ultrasound
- Keywords
- hip dysplasia, artificial intelligence, ultrasound, screening, infants
- Medical subject headings
- Hip Dislocation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence augmented ultrasoundDevice
The hip ultrasound is performed using a handheld device (Exo Iris) that uses 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. When there are enough images for analysis the operator is notified that the scan is complete.
| Study group | Role | What they receive |
|---|---|---|
| All active participants | Other | All participants will undergo an AI-augmented ultrasound and there will be no active comparator Receives: Device: Artificial intelligence augmented ultrasound |
What is being measured
The primary outcome is the main question the study is designed to answer.
Artificial Intelligence augmented ultrasound screening test capability: Sensitivity
Artificial intelligence (AI) augmented ultrasound results will be compared to standard 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). Groups will be defined as follows:
- True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports.
- False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports
- False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports
- True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
Measured: 1 day, both ultrasound scans will be performed on the same day
Artificial Intelligence augmented ultrasound screening test capability: Specificity
Artificial intelligence (AI) augmented ultrasound results will be compared to standard 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). Groups will be defined as follows:
- True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports.
- False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports
- False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports
- True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
Measured: 1 day, both ultrasound scans will be performed on the same day
Artificial Intelligence augmented ultrasound screening test capability: Positive predictive value (PPV)
Artificial intelligence (AI) augmented ultrasound will be compared to standard ultrasound to calculate PPV ([number of true positive cases detected/(number of true positive cases detected + number of false positive cases predicted) X 100). Groups will be defined as follows:
- True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports.
- False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports
- False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports
- True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
Measured: 1 day, both ultrasound scans will be performed on the same day
Artificial Intelligence augmented ultrasound screening test capability: Negative predictive value (NPV)
Artificial intelligence (AI) augmented ultrasound will be compared to standard ultrasound to calculate NPV [number of true negative cases detected/(number of false negatives detected + number of true negative cases detected) X 100. Groups will be defined as follows:
- True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports.
- False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports
- False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports
- True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
Measured: 1 day, both ultrasound scans will be performed on the same day
Secondary outcomes (9)
Factors associated with differences in device sensitivity
Sensitivity will be calculated between groups:
- Degree of dysplasia as defined by the Graf classification
- Sex
- Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks)
- Number of scans performed by device operator (less than or greater than 60 scans).
Measured: 1 day, all data will be collected from day of scan
Factors associated with differences in device specificity
Analyses will stratified to look at differences in specificity between groups:
- Degree of dysplasia as defined by the Graf classification
- Sex
- Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks)
- Number of scans performed by device operator (less than or greater than 60 scans).
Measured: 1 day, all data will be collected from day of scan
Factors associated with differences in device positive predictive value
Positive predictive value will be compared between groups:
- Degree of dysplasia as defined by the Graf classification
- Sex
- Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks)
- Number of scans performed by device operator (less than or greater than 60 scans).
Measured: 1 day, all data will be collected from day of scan
Factors associated with differences in device negative predictive value
Negative predictive value will be compared between groups:
- Degree of dysplasia as defined by the Graf classification
- Sex
- Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks)
- Number of scans performed by device operator (less than or greater than 60 scans).
Measured: 1 day, all data will be collected from day of scan
Device operator reliability in performing successful scans
Device operators' reliability will be recorded as percentage of scans performed that return a suboptimal result. This will be done by graphing number of scans performed by operators (operator experience) (x axis) against proportion of sub-optimal scans (y axis) to visually identify if a steady state is achieved.
Measured: 12 months or entire study duration
Acquisition of successful scans
The total proportion of infants unable to be scanned with the Artificial Intelligence augmented ultrasound device and reasons why scans were unsuccessful will from the entire sample. A higher frequency of successful scan acquisition will indicate better device performance.
Measured: 12 months or entire study duration
Time taken to acquire scan
Time to acquire the image will be calculated from the initiation of the scan to the time that the software indicates image acquisition is complete. The time to receive results will be calculated from the time of completion acquisition to the time the final recommendation is provided. A lower successful scan time will be indicative of higher feasibility.
Measured: 1 day, calculated at time of scan
Caregiver perspectives on their infant undergoing the artificial intelligence augmented ultrasound
Caregivers will be asked to answer a purpose-built survey that has been piloted in Canadian studies (3 questions rated from 0-10, where 10 indicates a more positive experience) in addition to Australian-specific closed and open-ended questions.
Measured: 1 day, caregivers will be asked to complete immediately following the scan
Operator perspectives on performing the artificial intelligence augmented ultrasound
Operators will be asked to complete the 10-item System Usability Questionnaire which measures the perceived ease of using technological devices. Scores are calculated on a 5-point Likert scale where 1=Strongly disagree and 5=Strongly agree. A single composite score out of 100 is calculated from all 10 items and indicates the overall useability of the device, where a higher score indicates better useability. In addition to this, two open-ended questions (Are there any further comments you would like to make about what you liked about the device?" and "are there any further comments you would like to make about what you didn't like about the device?") will be asked.
Measured: At the conclusion of their involvement in the study device (up to 12 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Oct 2024
- Study start6 Dec 2024
- Primary completionNov 2026 (estimated)
- Study completionNov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Developmental Dysplasia of HipStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- All participants will undergo the artificial intelligence ultrasound measures in addition to standard care.
- Masking
- None (open label)
- Enrolment
- 240 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Murdoch Childrens Research Institute
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Leo T Donnan, Principal investigator, Murdoch Childrens Research Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06647225
- Sponsor's protocol ID
- 107227
- Status last verified
- Apr 2026
- First submitted
- 14 Oct 2024
- Last updated
- 8 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Undecided: The researchers may choose to share data with other groups that are using the same device, all IPD would be deidentified and would be subject to further ethical approval. The researchers have sought consent from participants to allow data collected to be used for future research.
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Augmented Ultrasound for Developmental Dysplasia of the Hip: a Validity Study
- ClinicalTrials.gov ID
- NCT06647225
- Acronym / sponsor ID
- 107227
- Conditions
- Developmental Dysplasia of Hip
- Keywords
- hip dysplasia, artificial intelligence, ultrasound, screening, infants
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 240 participants (estimated)
- Lead sponsor
- Murdoch Childrens Research InstituteUniversity, hospital or other
- Study start
- 6 Dec 2024 (actual)
- Primary completion
- Nov 2026 (estimated)
- Study completion
- Nov 2026 (estimated)
- First posted
- 17 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this clinical trial is to learn if an ultrasound scan using artificial intelligence can accurately screen for hip dysplasia. Researchers will compare the artificial intelligence ultrasound results to the standard ultrasound measures to see if the artificial intelligence ultrasound scan can accurately screen for hip dysplasia.
It will also seek to understand how parents feel about their children undergoing this scan.
Participants will:
- Have an additional ultrasound performed on their child at their scheduled outpatient's appointment for hip dysplasia
- Complete a short questionnaire about the experience of having the measurement performed on their child
Detailed description
Initial screening for Developmental Dysplasia of the Hip (DDH) in Australia is performed most often by general practitioners and paediatricians shortly after birth and by maternal child health care nurses (MCHN) throughout the first year of life. These physical examinations consist of the Ortolani and Barlow tests and the examination of the thigh and gluteal creases. A recent meta-analysis reported the sensitivity of these tests as 36%, which indicates there is potential for a large proportion of cases to go undetected when solely relying on these examinations. Moreover, there currently are no formalised processes by which standards of practice are taught, assessed, or maintained. Thus, there is a clear need for a less operator-dependent screening protocol that can be performed within the current models of infant care. While some countries utilise universal ultrasound screening, this too is limited by access to care, as devices are not portable and thus cannot be used in current care models. Furthermore, it requires a specialist operator, substantially increasing cost. The screening program's limited nature, combined with the need for more consensus among international healthcare providers regarding the best method for managing DDH, has produced highly mixed clinical practices.
One part of the solution is optimising screening protocols for DDH in existing care models. Each state in Australia has established MCHN care protocols that provide access care for young children. While physical screening for DDH in these visits is standard practice, there remains considerable scope for improvement in the accuracy and reliability of these screening methods. Selective screening relies on several clinical associations with DDH to identify which patients receive ultrasound screening. Still, it has been shown to detect only 50% of infants with dysplasia. The MCHN screening program relies on clinical examination alone to detect dysplasia, an inferior identification method. Universal screening has a higher rate of detection of dysplasia but is expensive, single point in time (so misses the development of dysplasia) and results in higher levels of treatment.
A possible solution is portable artificial intelligence (AI)-augmented ultrasound. Recently technology has been developed to support a portable ultrasound device to screen DDH that uses AI-enabled technology to screen for DDH rapidly and accurately. Prior data has demonstrated that physicians and nurses could operate the device following training from expert sonographers. With its low-cost and ease of operation (with simple training) by healthcare providers such as MCHNs, it could significantly augment the physical screening. Thus, there is clear potential for an affordable, repeatable, and accessible screening methodology to be translated into clinical care. Initial Canadian data is promising. Pilot data suggests that DDH detection rates with this technology is on par with the detection rates of orthopaedic specialists. However, as this study was performed in a community setting and only those participants referred to orthopaedic clinics had a standard ultrasound measure performed, this pilot was unable to compare this screening technique with current gold standard diagnostic measures across the whole cohort, nor determine device sensitivity or predictive values. To demonstrate that this technology is fit for purpose, it is imperative that the rate of false negatives is also understood, as this is what will lead to late presentation, - which is what screening ultimately endeavours to prevent. Moreover, in an Australian context an important consideration in a wider roll-out is whether this technology would be accepted for uptake by clinicians and parents.
The proposed project will seek to gather pilot data to assess the validity and feasibility of this technology within a population of infants aged 4-20 weeks flagged at risk for DDH and referred to the Royal Children's Hospital. This will enable the recruitment of a sufficient number of cases of DDH to determine the sensitivity of the device. While the sensitivity and specificity of the device in this at-risk population may not be generalizable to the wider community the information gathered will then inform and refine a larger study of this technology in a community setting such as tertiary (birthing hospitals) and primary (MCHN clinics) care. If it can be demonstrated that it is feasible to implement this technology into existing care models, there is clear scope for this technology to revolutionize DDH screening. Thus, this project seeks to determine how well the device performs (sensitivity, specificity and predictive value) and the the clinical acceptability of this measure within the patient population.
Study design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- All participants will undergo the artificial intelligence ultrasound measures in addition to standard care.
| Arm | Type | Receives |
|---|---|---|
| All active participants | Other | Device: Artificial intelligence augmented ultrasoundAll participants will undergo an AI-augmented ultrasound and there will be no active comparator |
Who may qualify
- Ages
- 4 to 20 weeks
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
Key inclusion criteria (5 listed)
- Enrolled in the VicHip study
- Is 4-20 weeks of age at enrolment
- Is attending The Royal Children's Hospital for the purpose of the potential diagnosis of DDH
- Has a diagnostic (standard) hip ultrasound on the day of their out-patient appointment
- Has a legally acceptable representative capable of understanding the informed consent document and providing consent on the participant's behalf.
Key exclusion criteria (2 listed)
- They are currently receiving treatment for DDH
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence augmented ultrasoundThe hip ultrasound is performed using a handheld device (Exo Iris) that uses 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. When there are enough images for analysis the operator is notified that the scan is complete. | Device | All active participants |
Primary outcome
Artificial Intelligence augmented ultrasound screening test capability: Sensitivity
Time frame: 1 day, both ultrasound scans will be performed on the same day
Artificial intelligence (AI) augmented ultrasound results will be compared to standard 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). Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
Artificial Intelligence augmented ultrasound screening test capability: Specificity
Time frame: 1 day, both ultrasound scans will be performed on the same day
Artificial intelligence (AI) augmented ultrasound results will be compared to standard 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). Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
Artificial Intelligence augmented ultrasound screening test capability: Positive predictive value (PPV)
Time frame: 1 day, both ultrasound scans will be performed on the same day
Artificial intelligence (AI) augmented ultrasound will be compared to standard ultrasound to calculate PPV ([number of true positive cases detected/(number of true positive cases detected + number of false positive cases predicted) X 100). Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
Artificial Intelligence augmented ultrasound screening test capability: Negative predictive value (NPV)
Time frame: 1 day, both ultrasound scans will be performed on the same day
Artificial intelligence (AI) augmented ultrasound will be compared to standard ultrasound to calculate NPV [number of true negative cases detected/(number of false negatives detected + number of true negative cases detected) X 100. Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
9 secondary outcomes
Factors associated with differences in device sensitivity
Time frame: 1 day, all data will be collected from day of scan
Factors associated with differences in device specificity
Time frame: 1 day, all data will be collected from day of scan
Factors associated with differences in device positive predictive value
Time frame: 1 day, all data will be collected from day of scan
Factors associated with differences in device negative predictive value
Time frame: 1 day, all data will be collected from day of scan
Device operator reliability in performing successful scans
Time frame: 12 months or entire study duration
Acquisition of successful scans
Time frame: 12 months or entire study duration
Time taken to acquire scan
Time frame: 1 day, calculated at time of scan
Caregiver perspectives on their infant undergoing the artificial intelligence augmented ultrasound
Time frame: 1 day, caregivers will be asked to complete immediately following the scan
Operator perspectives on performing the artificial intelligence augmented ultrasound
Time frame: At the conclusion of their involvement in the study device (up to 12 months)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Apr 2026.
- Individual participant dataSharing planned: Undecided. The researchers may choose to share data with other groups that are using the same device, all IPD would be deidentified and would be subject to further ethical approval. The researchers have sought consent from participants to allow data collected to be used for future research.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Melissa Formosa contact
Leo T Donnan principal investigator
Murdoch Childrens Research Institute
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence augmented ultrasound in people with Developmental Dysplasia of Hip, run by Murdoch Childrens Research Institute at 1 site in Australia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 8 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Australia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Natalie K Hyde, BBiomedSc (Hons), PhD (61 3 9936 6246, natalie.hyde@mcri.edu.au), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Artificial Intelligence augmented ultrasound screening test capability: Sensitivity (time frame: 1 day, both ultrasound scans will be performed on the same day). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Apr 2026; AzTrials last refreshed its copy on 27 Oct 2026.