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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Limb Deformity
• Intervention / Treatment: Other: No interventions

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 Type: Observational
• Enrollment (Anticipated): 9000

Contacts and Locations

• Study Contact: Name: Bo Ning, PhD; Phone Number: +86 13585700275; Email: ningbo@fudan.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: No older than 18 years (Child, Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Children with limb deformity

Description

Inclusion Criteria:

Children with limb deformity

Exclusion Criteria:

Children without limb deformity

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
limb deformity children; the imaging of limb deformity diagnosis by AI	Other: No interventions; It is an observational study. No interventions.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Children's Hospital of Fudan University
• Investigators: Principal Investigator: Bo Ning, PhD, Children's Hospital of Fudan University

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Anticipated): December 1, 2022
• Primary Completion (Anticipated): September 1, 2023
• Study Completion (Anticipated): December 1, 2024

Study Registration Dates

• First Submitted: August 22, 2020
• First Submitted That Met QC Criteria: August 22, 2020
• First Posted (Actual): August 26, 2020

Study Record Updates

• Last Update Posted (Actual): April 12, 2022
• Last Update Submitted That Met QC Criteria: April 10, 2022
• Last Verified: April 1, 2022

More Information

Terms related to this study

• Keywords: Limb deformity; Pediatrics ; Artificial intelligence
• Additional Relevant MeSH Terms: Congenital Abnormalities
• Other Study ID Numbers: ningbo1528

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No