Evaluation of Diagnostic Accuracy of Artificial Intellige... | NCT06792747

Studies
Evaluation of Diagnostic Accuracy of Artificial Intelligence in Treatment Planning for Non-growing Class II Cases

NCT06792747

Sponsor

Cairo University

Status

Unknown status

Last Update Posted

Jan 27, 2025Actual

Enrollment

193Estimated

Phase

Not provided

Conditions

Class II Malocclusion

Interventions

Not provided

Countries

Not provided

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Jan 2024

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Not yet recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jan 2025Estimated

Primary Completion Date

Jun 2025Estimated

Completion Date

Dec 2025Estimated

First Submitted Date

Jan 20, 2025

First Submission Date that Met QC Criteria

Jan 20, 2025

First Posted Date

Jan 27, 2025Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Jan 20, 2025

Last Update Posted Date

Jan 27, 2025Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Israa Abuobieda Ibrahim Elbagari, Master Degree student at the department of orthodontics, Cairo UniversityPrincipal Investigator

Lead Sponsor

Cairo UniversityOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

FDAAA801 Violation

Not provided

Description Module

Brief Summary

The goal of this observational study is to evaluate the diagnostic accuracy of artificial intelligence in non-growing class II cases. The main question it aims to answer is:

Is Artificial Intelligence (AI) accurate in choosing a treatment modality for non-growing class II cases -whether to camouflage or surgical treatment?

participants already undergone orthodontic treatment, their pre-treatment and post-treatment records will be collected from the archive of orthodontic department at Cairo university

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

193Estimated

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Accuracy of Artificial intelligence in choosing\predicting the best treatment modality		from enrollment to the end of treatment at 1 year

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

cases of non-growing patients with class II malocclusion

Exclusion Criteria:

Growing patient with class II malocclusion

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Egyptian non-growing individuals with class II malocclusion

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Israa abuobieda elbagari, Msc candidate	Contact	+201129684395		israa.ibrahim@dentistry.cu.edu.eg
israa abuobieda elbagari, Msc candidate	Contact	+201129684395		iskyme722@gmail.com

Overall Officials

Not provided

Locations

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D057887	Overbite

Ancestor Terms

ID	Term
D008312	Malocclusion, Angle Class II
D008310	Malocclusion
D014076	Tooth Diseases
D009057	Stomatognathic Diseases

Browse Leaves

Not provided

Browse Branches

Not provided