Computer Aided Tool for Diagnosis of Neck Masses in Children
Study Details
Study Description
Brief 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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
Diagnostic Test: Artificial Intelligence Algorithm
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. |
Diagnostic Test: Artificial Intelligence Algorithm
Different machine learning and deep learning computer aided strategies for model construction and validation.
|
Outcome Measures
Primary Outcome Measures
- The diagnostic accuracy of neck masses with AI-based screening tools in children [1 month]
The diagnostic accuracy of neck masses with AI-based screening tools in children.
Secondary Outcome Measures
- The diagnostic sensitivity of neck masses with AI-based screening tools in children [1 month]
The diagnostic sensitivity 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 specificity 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 positive 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]
The diagnostic negative predictive value of neck masses with AI-based screening tools in children
Eligibility Criteria
Criteria
Inclusion Criteria:
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Age up to 18 years old
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Receiving no treatment before diagnosis
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With written informed consent
Exclusion Criteria:
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Clinical data missing
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Unavailable radiological images
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Without written informed consent
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | West China Hospital, Sichuan University | Chengdu | Sichuan | China | 6100041 |
Sponsors and Collaborators
- West China Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- HX-20211023