Computer Aided Tool for Diagnosis of Neck Masses in Children

Recruiting

• Conditions: Teratomas; Branchial Cleft Anomalies; Neck Mass; Infantile Hemangiomas; Thyroglossal Duct Cysts; Dermoid and Epidermoid Cysts
• Interventions: Diagnostic Test: Artificial Intelligence Algorithm

• Registration Number: NCT05187923
• Lead Sponsor: West China Hospital

Brief Summary

The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for neck masses using machine learning and deep learning techniques on clinical information and radiological images in children.

Detailed Description

This study is a retrospective-prospective design by West China Hospital, Sichuan University, including clinical data and radiological images. A retrospective database was enrolled for patients with definite histological diagnosis and available radiological images from June 2010 and December 2020. The investigators have constructed deep learning and machine learning diagnostic models on this retrospective cohort and validated it internally. A prospective cohort would recruit patients found neck masses since January 2021. The proposed computer aided diagnostic models would also be validated in this prospective cohort externally. The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for neck masses using machine learning and deep learning techniques on clinical data and radiological images in children.

Study Timeline

• Study Start: 2021-01-01
• Study First Submitted: 2021-12-24
• Study First Submitted QC: 2021-12-24
• Status Verified: 2022-01
• Last Update Submitted: 2022-01-11
• Study First Posted: 2022-01-12
• Last Update Posted: 2022-01-27
• Primary Completion: 2024-12-31
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1500

Inclusion Criteria

• Age up to 18 years old
• Receiving no treatment before diagnosis
• With written informed consent

Exclusion Criteria

• Clinical data missing
• Unavailable radiological images
• Without written informed consent

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Prospective cohort	Artificial Intelligence Algorithm	The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.
Retrospective cohort	Artificial Intelligence Algorithm	The internal cohort was retrospectively enrolled in West China Hospital, Sichuan University from June 2010 and December 2020. It is a training and internal validation cohort.

Primary Outcome Measures

Name	Time	Method
The diagnostic accuracy of neck masses with AI-based screening tools in children	1 month	The diagnostic accuracy of neck masses with AI-based screening tools in children.

Secondary Outcome Measures

Name	Time	Method
The diagnostic specificity of neck masses with AI-based screening tools in children	1 month	The diagnostic specificity of neck masses with AI-based screening tools in children.
The diagnostic positive predictive value of neck masses with AI-based screening tools in children	1 month	The diagnostic positive predictive value of neck masses with AI-based screening tools in children.
The diagnostic sensitivity of neck masses with AI-based screening tools in children	1 month	The diagnostic sensitivity of neck masses with AI-based screening tools in children.
The diagnostic negative predictive value of neck masses with AI-based screening tools in children	1 month	The diagnostic negative predictive value of neck masses with AI-based screening tools in children

Trial Locations

Locations (1)

West China Hospital, Sichuan University; 🇨🇳 Chengdu, Sichuan, China