NCT04527029

Brief 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 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.

Trial Health

65
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
9,000

participants targeted

Target at P75+ for all trials

Timeline
20mo left

Started Mar 2025

Typical duration for all trials

Status
not yet recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress43%
Mar 2025Dec 2027

First Submitted

Initial submission to the registry

August 22, 2020

Completed
4 days until next milestone

First Posted

Study publicly available on registry

August 26, 2020

Completed
4.5 years until next milestone

Study Start

First participant enrolled

March 1, 2025

Completed
1.5 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

September 1, 2026

Expected
1.2 years until next milestone

Study Completion

Last participant's last visit for all outcomes

December 1, 2027

Last Updated

February 20, 2025

Status Verified

February 1, 2025

Enrollment Period

1.5 years

First QC Date

August 22, 2020

Last Update Submit

February 19, 2025

Conditions

Limb Deformity

Keywords

Limb deformity; Pediatrics ; Artificial intelligence

Outcome Measures

Primary Outcomes (1)

  • 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.

    At enrollment

Study Arms (1)

limb deformity children

the imaging of limb deformity diagnosis by AI

Other: No interventions

Interventions

No interventionsOTHER

It is an observational study. No interventions.

limb deformity children

Eligibility Criteria

AgeUp to 18 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64)
Sampling MethodNon-Probability Sample
Study Population

Children with limb deformity

You may qualify if:

  • Children with limb deformity

You may not qualify if:

  • Children without limb deformity

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (6)

  • 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.

    PMID: 26031051BACKGROUND

  • Theofilatos 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.

    PMID: 25765008BACKGROUND

  • Silverman 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.

    PMID: 25801593BACKGROUND

  • Jamaludin 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.

    PMID: 28168339BACKGROUND

  • Ravi 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.

    PMID: 28055930BACKGROUND

  • Rahmathulla 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.

    PMID: 24580004BACKGROUND

Study Officials

  • Bo Ning, PhD

    Children's Hospital of Fudan University

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Bo Ning, PhD

CONTACT

+86 13585700275ningbo@fudan.edu.cn

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

August 22, 2020

First Posted

August 26, 2020

Study Start

March 1, 2025

Primary Completion (Estimated)

September 1, 2026

Study Completion (Estimated)

December 1, 2027

Last Updated

February 20, 2025

Record last verified: 2025-02

Data Sharing

IPD Sharing
Will not share