Early Intelligent Diagnosis of Limb Deformity in Children by AI and Clinic Application

Limb Deformity Clinical Trial

Official title:

The Studies of Early Intelligent Diagnosis of Limb Deformity in Children by AI and Clinic Application

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT04527029
• Other study ID #: ningbo1528
• Status: Not yet recruiting
• Start date: December 2022
• Est. completion date: December 1, 2024

Study information

• Verified date: April 2022
• Source: Children's Hospital of Fudan University
• Contact: Bo Ning, PhD
• Phone: +86 13585700275
• Email: ningbo[@]fudan.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical 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 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 9000
• Est. completion date: December 1, 2024
• Est. primary completion date: September 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 18 Years

Eligibility

Inclusion Criteria:

Children with limb deformity Exclusion Criteria: Children without limb deformity

Related Conditions & MeSH terms

• Congenital Abnormalities
• Limb Deformity

Intervention

Other:
• No interventions
It is an observational study. No interventions.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Children's Hospital of Fudan University

References & Publications (6)

Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battié 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. — View Citation

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. Review. Russian. — View Citation

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. Review. — View Citation

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. Review. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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