Computer Aided Tool for Diagnosis of Neck Masses in Children

Infantile Hemangiomas Clinical Trial

Official title:

Computer Aided Tool for Diagnosis of Neck Masses in Children

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05187923
• Other study ID #: HX-20211023
• Status: Recruiting
• Start date: January 1, 2021
• Est. completion date: December 31, 2024

Study information

• Verified date: January 2022
• Source: West China Hospital
• Contact: Yuhan Yang, MD
• Phone: 8613258389785
• Email: yyh_1023[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1500
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 18 Years

Eligibility

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

Related Conditions & MeSH terms

• Cysts
• Epidermal Cyst
• Hemangioma
• Hemangioma, Capillary
• Infantile Hemangiomas
• Neck Mass
• Port-Wine Stain
• Teratoma

Intervention

Diagnostic Test:
• Artificial Intelligence Algorithm
Different machine learning and deep learning computer aided strategies for model construction and validation.

Locations

Country	Name	City	State
China	West China Hospital, Sichuan University	Chengdu	Sichuan

Sponsors (1)

Lead Sponsor	Collaborator
West China Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The diagnostic accuracy of neck masses with AI-based screening tools in children	The diagnostic accuracy of neck masses with AI-based screening tools in children.	1 month
Secondary	The diagnostic sensitivity 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
Secondary	The diagnostic specificity of neck masses with AI-based screening tools in children	The diagnostic specificity of neck masses with AI-based screening tools in children.	1 month
Secondary	The diagnostic positive predictive value 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
Secondary	The diagnostic negative predictive value 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