The Use of Artificial Intelligence in the Dental X-rays Analysis

Hospital of the Ministry of Interior, Kielce, Poland1,025 enrolled

Overview

This cross-sectional study aims to perform a population-based assessment of the incidence of decay, dental fillings, root canal fillings, endodontic lesions, implants, implant and dental abutment crowns, pontic crowns, and missing teeth, considering the location. Patients with indications for dental X-ray confirmed by a written referral and with permanent dentition will participate in the study. Then, the X-rays will be analyzed by the dentists and the AI-based software after the data has been anonymized. The results will be compared to determine the AI algorithm's sensitivity, specificity, and precision.

Study Type

OBSERVATIONAL

Enrollment

1,025

Conditions

Oral Health Dental Caries Periapical Diseases Tooth Loss

Interventions

Taking a dental X-rayRADIATION

Dental X-rays taken in patients with indications confirmed by a written referral.

Eligibility

Sex: ALLMin age: 11 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Indications for dental X-ray confirmed by a written referral from the dentist or physician (both screening tests and tests performed for treatment purposes were allowed) * Permanent dentition (after exfoliation is completed) Exclusion Criteria: * Patients with mixed dentition (exfoliation has not finished)

Locations (1)

Department of Maxillofacial Surgery

Kielce, Poland

Outcomes

Primary Outcomes

Sensitivity

Sensitivity (also known as recall or true positive rate) is the proportion of actual positive cases that are correctly predicted as positive. It evaluates the performance of an AI algorithm. Formally it can be calculated with the following equation: Sensitivity = TP / (TP+FN) True positive (TP) - a test result that correctly indicates the presence of a condition or characteristic False Negative (FN) - a test result which wrongly indicates that a particular condition or characteristic is absent

Time frame: Up to 6 weeks

Specificity

Specificity (also known as true negative rate) - is the proportion of actual negative cases that are correctly predicted as negative. It evaluates the performance of an AI algorithm. Formally it can be calculated by the equation below: Specificity = TN / (TN + FP) True negative (TN) - a test result that correctly indicates the absence of a condition or characteristic False positive (FP) - a test result which wrongly indicates that a particular condition or characteristic is present

Time frame: Up to 6 weeks

Precision of the AI algorithm

Precision is an evaluation metric used to assess the performance of machine learning algorithm for AI. It measures how accurate the algorithm is. We will use the number of true positives (TP) and false positives (FP) to calculate precision using the following formula: Precision = TP / (TP + FP) True positive (TP) - a test result that correctly indicates the presence of a condition or characteristic False positive (FP) - a test result that wrongly indicates that a particular condition or characteristic is present

Time frame: Up to 6 weeks

Data from ClinicalTrials.gov

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