Computer Aided Tool for Diagnosis of Neck Masses in Children

Sichuan University

Status: Enrolling

Conditions: Thyroglossal Duct Cysts; Dermoid and Epidermoid Cysts; Infantile Hemangiomas; Neck Mass; Branchial Cleft Anomalies; Teratomas

Treatments: Diagnostic Test: Artificial Intelligence Algorithm

Study type: Observational

Funder types: Other

Identifiers

NCT05187923

HX-20211023

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,500 estimated patients

Sex: All

Ages: Under 18 years old

Volunteers: No Healthy Volunteers

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

Trial design

1,500 participants in 2 patient groups

Retrospective cohort

Description:

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.

Treatment:

Diagnostic Test: Artificial Intelligence Algorithm

Prospective cohort

Description:

The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.

Treatment:

Diagnostic Test: Artificial Intelligence Algorithm