Comparative evaluation of accuracy between conventional method and artificial neural network modelling method (AI) in the diagnosis of gingivitis .
Trial Snapshot
- Phase
- Not Applicable
- Status
- Not yet recruiting
- Sponsor
- Enrollment
- 1,000
- Locations
- 1
- Primary Endpoint
- To assess and compare the diagnostic accuracy of an Artificial Neural Network (AI-based tool) with conventional clinical methods (Gingival Index and Plaque Index) for the detection of gingivitis in patients.
Study Overview
Brief Summary
This study aims to evaluate the diagnostic accuracy of an AI-based mobile application developed to detect gingivitis from standardized intraoral photographs, compared with the traditional clinical probing method. Patients will be assessed using both the AI tool and conventional periodontal probing to determine gingival inflammation. The primary objective is to assess sensitivity, specificity, and agreement between the AI-based diagnosis and clinician-based probing, thereby validating the utility of the app as a non-invasive screening tool for gingivitis in clinical and community settings.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Masking
- None
Eligibility Criteria
- Ages
- 18.00 Year(s) to 60.00 Year(s) (—)
- Sex
- All
Inclusion Criteria
- •1.Patients of age 14-75yrs groups are included.
- •2.Patients of all genders are included.
- •3.Patients with normal occlusion.
- •4.Patients who diagnosed to have gingivitis and have 24 or more teeth.
Exclusion Criteria
- •1.Patients under the age of 14 yrs are not included in the study.
- •2.Patients with other gingival diseases such as localized periodontitis , aggressive periodontitis, chronic periodontitis, and necrotizing periodontal diseases 3.Patients with all systemic diseases and pre historic medications are not included in the study.
Outcomes
Primary Outcomes
To assess and compare the diagnostic accuracy of an Artificial Neural Network (AI-based tool) with conventional clinical methods (Gingival Index and Plaque Index) for the detection of gingivitis in patients.
Time Frame: Single time point: | Diagnostic evaluations (both conventional and AI-based) were performed during the same outpatient visit, without any follow-up. | T0 (Baseline): On the day of enrollment, each patient underwent: | Clinical examination using Gingival Index & Plaque Index | Intraoral imaging for AI-based diagnosis
Secondary Outcomes
No secondary outcomes reported
Investigators
Dr Panshul Kharche
NIMS Dental College