Ignite Creation Date:
2024-05-06 @ 8:17 PM
Last Modification Date:
2024-10-26 @ 3:24 PM
Study NCT ID:
NCT06325514
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-03-26
First Post:
2024-03-16
Brief Title:
Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
Sponsor:
Cairo University
Organization:
Cairo University
Study Overview
Official Title:
The Application of an Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This study aims to develop an AI program that can classify oral findings into Normalvariation of normal or an oral disease by clinical photos analysis aiding in lowering the percentages of false positive and false negative diagnosis of oral diseases
Detailed Description:
Early diagnosis of oral lesions particularly oral cancer is crucial for enhancing prognosis facilitating early intervention and care with the intention of lowering disease-related mortality
Since conventional oral examination COE is the most used method in identifying oral lesions the average dental practitioners experience is a decisive factor in early diagnosis
Visual examination lacks specificity and sensitivity since its highly subjective Unfortunately Studies show that the majority of dentists lack expertise in early detection of the disease resulting in false negative diagnosis of oral lesions
General practitioners are found to either delay the referral of a suspected oral lesion to an Oral Medicine specialist or referring numerous false positive cases unnecessarily pushing the patients into a state of anxiousness and cancer phobia False positive referrals overburden the specialists which will eventually cause delayed diagnosis of true positive cases due to the oversaturation with false positive ones
diagnostic research scope shifts towards noninvasive easy chair side methods with higher accuracy for early detection of oral lesions Recent approaches towards using machine based programs indicate that this machine-learning method may be useful in the detection and diagnosis of oral cancer
Since conventional oral examination COE is the most used method in identifying oral lesions the average dental practitioners experience is a decisive factor in early diagnosis
Visual examination lacks specificity and sensitivity since its highly subjective Unfortunately Studies show that the majority of dentists lack expertise in early detection of the disease resulting in false negative diagnosis of oral lesions
General practitioners are found to either delay the referral of a suspected oral lesion to an Oral Medicine specialist or referring numerous false positive cases unnecessarily pushing the patients into a state of anxiousness and cancer phobia False positive referrals overburden the specialists which will eventually cause delayed diagnosis of true positive cases due to the oversaturation with false positive ones
diagnostic research scope shifts towards noninvasive easy chair side methods with higher accuracy for early detection of oral lesions Recent approaches towards using machine based programs indicate that this machine-learning method may be useful in the detection and diagnosis of oral cancer
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None