NCT05187923

Computer Aided Tool for Diagnosis of Neck Masses in Children

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 1,500 (estimated)
• Sponsor: West China Hospital · Academic / Other
• Sex: All
• Age: 0 Years – 18 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Neck Mass
• Thyroglossal Duct Cysts
• Branchial Cleft Anomalies
• Dermoid and Epidermoid Cysts
• Infantile Hemangiomas
• Teratomas

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Artificial Intelligence Algorithm	Different machine learning and deep learning computer aided strategies for model construction and validation.

Timeline

• Start date: 2021-01-01
• Primary completion: 2024-12-31
• Completion: 2024-12-31
• First posted: 2022-01-12
• Last updated: 2022-01-27

Locations

1 site across 1 country: China