Clinical trial · Observational
Computer Aided Tool for Diagnosis of Neck Masses in Children
NCT05187923CI-TRIAL-00056194unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Branchial Cleft Anomalies | — | UNRESOLVED | — |
| Dermoid and Epidermoid Cysts | — | UNRESOLVED | — |
| Infantile Hemangiomas | — | UNRESOLVED | — |
| Neck Mass | — | UNRESOLVED | — |
| Teratomas | Teratoma | ALIAS | 0.90 |
| Thyroglossal Duct Cysts | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Artificial Intelligence Algorithm | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- 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.
- interventionNames
- Diagnostic Test: Artificial Intelligence Algorithm
- label
- Prospective cohort
- description
- The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.
- interventionNames
- Diagnostic Test: Artificial Intelligence Algorithm
Primary outcomes (1)
- measure
- The diagnostic accuracy of neck masses with AI-based screening tools in children
- timeFrame
- 1 month
- description
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 0 Years
- Maximum age
- 18 Years
Show eligibility criteria text
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
References
Publications (0)
Data not yet available
No reference posted for this study.