Computer Aided Tool for Diagnosis of Neck Masses in Children

NCT ID: NCT05187923

Last Updated: 2022-01-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-01-01

Study Completion Date

2024-12-31

Brief Summary

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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

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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

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

Study Design

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Observational Model Type

COHORT

Study Time Perspective

OTHER

Study Groups

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Retrospective cohort

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.

Artificial Intelligence Algorithm

Intervention Type DIAGNOSTIC_TEST

Different machine learning and deep learning computer aided strategies for model construction and validation.

Prospective cohort

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

Artificial Intelligence Algorithm

Intervention Type DIAGNOSTIC_TEST

Different machine learning and deep learning computer aided strategies for model construction and validation.

Interventions

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Artificial Intelligence Algorithm

Different machine learning and deep learning computer aided strategies for model construction and validation.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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
Minimum Eligible Age

0 Years

Maximum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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West China Hospital

OTHER

Sponsor Role lead

Responsible Party

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Yuhan Yang

Associate Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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West China Hospital, Sichuan University

Chengdu, Sichuan, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Yuhan Yang, MD

Role: CONTACT

8613258389785

Facility Contacts

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Yuhan Yang, MD

Role: primary

8613258389785

Other Identifiers

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HX-20211023

Identifier Type: -

Identifier Source: org_study_id

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