Computer Aided Tool for Diagnosis of Neck Masses in Children

Study Overview
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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.

Official Title
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Computer Aided Tool for Diagnosis of Neck Masses in Children

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

Intervention / Treatment
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* Diagnostic Test: Artificial Intelligence Algorithm


Participation Criteria
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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

Ages Eligible for Study
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Minimum Age: 0 Years
Maximum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Retrospective cohort<br>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. | Diagnostic Test: Artificial Intelligence Algorithm<br>* Different machine learning and deep learning computer aided strategies for model construction and validation.<br>|
| Prospective cohort<br>The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort. | Diagnostic Test: Artificial Intelligence Algorithm<br>* Different machine learning and deep learning computer aided strategies for model construction and validation.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| 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 | 
| 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 |