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Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
Study Purpose
This study aims to develop an AI program that can classify oral findings into Normal/variation 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.
Recruitment Criteria
Healthy volunteers are participants who do not have a disease or condition, or related conditions or symptoms
An interventional clinical study is where participants are assigned to receive one or more interventions (or no intervention) so that researchers can evaluate the effects of the interventions on biomedical or health-related outcomes.
An observational clinical study is where participants identified as belonging to study groups are assessed for biomedical or health outcomes.
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Inclusion Criteria:
Exclusion Criteria:
• Patients less than 18 years oldTrial Details
This trial id was obtained from ClinicalTrials.gov, a service of the U.S. National Institutes of Health, providing information on publicly and privately supported clinical studies of human participants with locations in all 50 States and in 196 countries.
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Phase 2: Studies that gather preliminary data on effectiveness (whether the drug works in people who have a certain disease or condition) and additional safety data.
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Phase 4: Studies occurring after FDA has approved a drug for marketing, efficacy, or optimal use.
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The person who is responsible for the scientific and technical direction of the entire clinical study.
Category of organization(s) involved as sponsor (and collaborator) supporting the trial.
The disease, disorder, syndrome, illness, or injury that is being studied.
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 practitioner's 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.
Arms
: normal/variations of normal anatomical landmarks
patients that have normal oral findings or variations of normal anatomical landmarks such as: leukoedema, fordyce granules, linea alba, physiological pigmentations, torus palatinus, torus mandibularis, geographic tongue, fissured tongue
: low risk referral
patients that needs referral for a low risk of malignant transformation disease, such as: hemangiomas, fibromas, oral apthous ulcers, candidal infections, pemphigus valgaris, petechiae, frictional keratosis, smokers' melanosis.
: high risk referral
patients that needs referral for a high risk of malignancy or a premalignant disease, such as: oral lichen planus, leukoplakia, erythroplakia, squamous cell carcinoma.
Interventions
Diagnostic Test: - Artificial intelligence based program
the AI based program is based on image analysis
Contact Information
This trial has no sites locations listed at this time. If you are interested in learning more, you can contact the trial's primary contact:
Noran A AbdelMoaty, MsC
[email protected]
+201063021131
For additional contact information, you can also visit the trial on clinicaltrials.gov.
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