Limb DeformityNCT04527029
Early Intelligent Diagnosis of Limb Deformity in Children by AI and Clinic Application
An observational study of Limb Deformity, run by Children's Hospital of Fudan University.
Trial summary
The limb deformity in children include congenital limb malformations or acquired from the damage of epiphyseal plate which caused by tumor, inflammation and trauma. Due to the complexity of the disease itself, rapid dynamic development and the characteristics of children's growth and development, the deformities are… Read more about this study
- Who is it for?
- Ages Up to 18 years
- Conditions the study is for
- Limb Deformity
- How many people
- 9,000 participants
- Where
- No sites listed yet
- Study dates
- Mar 2025to 1 Dec 2027
- Phase
- Observational
- 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 Up to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: Children with limb deformity
Who may be able to join
- Children with limb deformity
Who cannot join
- Children without limb deformity
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: The Studies of Early Intelligent Diagnosis of Limb Deformity in Children by AI and Clinic Application
The limb deformity in children include congenital limb malformations or acquired from the damage of epiphyseal plate which caused by tumor, inflammation and trauma. Due to the complexity of the disease itself, rapid dynamic development and the characteristics of children's growth and development, the deformities are constantly changing. In addition, the serious lack of clinical diagnosis and treatment resources in the Department of Pediatric Orthopedics has led to the misdiagnosis and improper treatment of children's limb deformities. Thus, its necessary to find an intelligent way to help doctor to early diagnosis of limb deformity and provide a proper treatment in children.
Read the detailed description
The extraction and application of big data of children's limb deformities, intelligent labeling of image data, precise positioning, and perfecting the anatomical data of children's limb deformities.Improve the positioning accuracy of key points in X-ray images of children's limb deformities by means of step-by-step supervision to improve the accuracy of diagnosis.Realize an intelligent report generation system that combines patient background information, establish an end-to-end auxiliary diagnosis and treatment suggestion demonstration application system; realize a full set of artificial intelligence solutions for children's skeletal deformities, early screening and diagnosis of children, and forming an intelligent referral system of children's limb deformities.
- Conditions
- Limb Deformity
- Being tested
- No interventions
- Keywords
- Limb deformity; Pediatrics ; Artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionsOther
It is an observational study. No interventions.
| Study group | Role | What they receive |
|---|---|---|
| limb deformity children | the imaging of limb deformity diagnosis by AI Receives: Other: No interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Deformity detection
It is a binary variable (1/0). The radiographic features of children would be evaluated by artificial Intelligence. If the deformity was detected, variable would be setted into 1.
Measured: At enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Aug 2020
- Study startMar 2025 (estimated)
- Primary completionSep 2026 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Limb DeformityStudies of the same condition, open and under way.
- More trials run by Children's Hospital of Fudan UniversityThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 9,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Children's Hospital of Fudan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Bo Ning, PhD, Principal investigator, Children's Hospital of Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04527029
- Sponsor's protocol ID
- ningbo1528
- Status last verified
- Feb 2025
- First submitted
- 22 Aug 2020
- Last updated
- 20 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Mirskaia NB, Kolomenskaia AN, Siniakina AD. [Prevalence and medical and social importance of disorders and diseases of the musculoskeletal systems in children and adolescents (review of literature)]. Gig Sanit. 2015 Jan-Feb;94(1):97-104. Russian.
PubMed 26031051Theofilatos K, Pavlopoulou N, Papasavvas C, Likothanassis S, Dimitrakopoulos C, Georgopoulos E, Moschopoulos C, Mavroudi S. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering. Artif Intell Med. 2015 Mar;63(3):181-9. doi: 10.1016/j.artmed.2014.12.012. Epub 2015 Feb 18.
PubMed 25765008Silverman BG, Hanrahan N, Bharathy G, Gordon K, Johnson D. A systems approach to healthcare: agent-based modeling, community mental health, and population well-being. Artif Intell Med. 2015 Feb;63(2):61-71. doi: 10.1016/j.artmed.2014.08.006. Epub 2014 Sep 11.
PubMed 25801593Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battie MC, Fairbank J, McCall I; Genodisc Consortium. ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J. 2017 May;26(5):1374-1383. doi: 10.1007/s00586-017-4956-3. Epub 2017 Feb 6.
PubMed 28168339Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep Learning for Health Informatics. IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21. doi: 10.1109/JBHI.2016.2636665. Epub 2016 Dec 29.
PubMed 28055930Rahmathulla G, Nottmeier EW, Pirris SM, Deen HG, Pichelmann MA. Intraoperative image-guided spinal navigation: technical pitfalls and their avoidance. Neurosurg Focus. 2014 Mar;36(3):E3. doi: 10.3171/2014.1.FOCUS13516.
PubMed 24580004
Study at a glance
From the registry record, last updated by the sponsor on 20 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Studies of Early Intelligent Diagnosis of Limb Deformity in Children by AI and Clinic Application
- ClinicalTrials.gov ID
- NCT04527029
- Acronym / sponsor ID
- ningbo1528
- Conditions
- Limb Deformity
- Keywords
- Limb deformity; Pediatrics ; Artificial intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 9,000 participants (estimated)
- Lead sponsor
- Children's Hospital of Fudan UniversityUniversity, hospital or other
- Study start
- Mar 2025 (estimated)
- Primary completion
- Sep 2026 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 26 Aug 2020 (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 limb deformity in children include congenital limb malformations or acquired from the damage of epiphyseal plate which caused by tumor, inflammation and trauma. Due to the complexity of the disease itself, rapid dynamic development and the characteristics of children's growth and development, the deformities are constantly changing. In addition, the serious lack of clinical diagnosis and treatment resources in the Department of Pediatric Orthopedics has led to the misdiagnosis and improper treatment of children's limb deformities. Thus, its necessary to find an intelligent way to help doctor to early diagnosis of limb deformity and provide a proper treatment in children.
Detailed description
The extraction and application of big data of children's limb deformities, intelligent labeling of image data, precise positioning, and perfecting the anatomical data of children's limb deformities.Improve the positioning accuracy of key points in X-ray images of children's limb deformities by means of step-by-step supervision to improve the accuracy of diagnosis.Realize an intelligent report generation system that combines patient background information, establish an end-to-end auxiliary diagnosis and treatment suggestion demonstration application system; realize a full set of artificial intelligence solutions for children's skeletal deformities, early screening and diagnosis of children, and forming an intelligent referral system of children's limb deformities.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| limb deformity children | – | Other: No interventionsthe imaging of limb deformity diagnosis by AI |
Who may qualify
- Ages
- Up to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- Children with limb deformity
Key inclusion criteria (1 listed)
- Children with limb deformity
Key exclusion criteria (1 listed)
- Children without limb deformity
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 |
|---|---|---|
| No interventionsIt is an observational study. No interventions. | Other | limb deformity children |
Primary outcome
Deformity detection
Time frame: At enrollment
It is a binary variable (1/0). The radiographic features of children would be evaluated by artificial Intelligence. If the deformity was detected, variable would be setted into 1.
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 20 Feb 2025.
- Background: PubMed 26031051Mirskaia NB, Kolomenskaia AN, Siniakina AD. [Prevalence and medical and social importance of disorders and diseases of the musculoskeletal systems in children and adolescents (review of literature)]. Gig Sanit. 2015 Jan-Feb;94(1):97-104. Russian.
- Background: PubMed 25765008Theofilatos K, Pavlopoulou N, Papasavvas C, Likothanassis S, Dimitrakopoulos C, Georgopoulos E, Moschopoulos C, Mavroudi S. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering. Artif Intell Med. 2015 Mar;63(3):181-9. doi: 10.1016/j.artmed.2014.12.012. Epub 2015 Feb 18.
- Background: PubMed 25801593Silverman BG, Hanrahan N, Bharathy G, Gordon K, Johnson D. A systems approach to healthcare: agent-based modeling, community mental health, and population well-being. Artif Intell Med. 2015 Feb;63(2):61-71. doi: 10.1016/j.artmed.2014.08.006. Epub 2014 Sep 11.
- Background: PubMed 28168339Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battie MC, Fairbank J, McCall I; Genodisc Consortium. ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J. 2017 May;26(5):1374-1383. doi: 10.1007/s00586-017-4956-3. Epub 2017 Feb 6.
- Background: PubMed 28055930Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep Learning for Health Informatics. IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21. doi: 10.1109/JBHI.2016.2636665. Epub 2016 Dec 29.
- Background: PubMed 24580004Rahmathulla G, Nottmeier EW, Pirris SM, Deen HG, Pichelmann MA. Intraoperative image-guided spinal navigation: technical pitfalls and their avoidance. Neurosurg Focus. 2014 Mar;36(3):E3. doi: 10.3171/2014.1.FOCUS13516.
- Individual participant dataSharing planned: No
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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.
Bo Ning, PhD principal investigator
Children's Hospital of Fudan University
Questions about this trial
What is this clinical trial studying?
An observational study of Limb Deformity, run by Children's Hospital of Fudan University.
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 1 inclusion and 1 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: opening soon, as of the sponsor's last update on 20 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
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 Bo Ning, PhD (+86 13585700275, ningbo@fudan.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Deformity detection (time frame: At enrollment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04527029, which also lists 6 linked publications.
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 20 Feb 2025.