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

Comparison of the Dental X-ray Analysis Performed by an Artificial Intelligence Algorithm and the Analysis Performed by Dentists

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, taking into account the location.

Study Overview

• Status: Recruiting
• Conditions: Dental Caries; Periapical Diseases; Tooth Loss; Oral Health
• Intervention / Treatment: Radiation: Taking a dental X-ray

Detailed Description

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 (Estimated): 1025

Contacts and Locations

• Study Contact: Name: Maciej Sikora, dr hab.; Phone Number: +48 41 260 55 85; Email: sikora-maciej@wp.pl

Study Locations

• Poland
  • Kielce, Poland, 25-375
    • Recruiting
    • Department of Maxillofacial Surgery
    • Contact: Maciej Sikora; Phone Number: +48 41 260 55 85; Email: sikora-maciej@wp.pl

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients included in the study were admitted to the radiology department in Kielce, a city in southern Poland with around 200.000 inhabitants.

Description

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)

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

One group of patients (double gate); Study design: Direction of data collection: retrospective; Number of gates (sets of eligibility criteria): double gate (AI, human); Participant sampling method: Consecutive; Method of allocating participants to index tests: Each participant received all index tests; Number of reference standards: Single test standard; Limited verification: Full verification (not limited)

Radiation: Taking a dental X-ray; Dental X-rays taken in patients with indications confirmed by a written referral.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	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	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	Up to 6 weeks

Collaborators and Investigators

• Sponsor: Hospital of the Ministry of Interior, Kielce, Poland
• Investigators: Study Chair: Maciej Sikora, Hospital of the Ministry of Interior, Wojska Polskiego 51, 25-375 Kielce, Poland

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2024
• Primary Completion (Estimated): February 23, 2024
• Study Completion (Estimated): March 1, 2024

Study Registration Dates

• First Submitted: January 25, 2024
• First Submitted That Met QC Criteria: February 12, 2024
• First Posted (Actual): February 14, 2024

Study Record Updates

• Last Update Posted (Actual): February 14, 2024
• Last Update Submitted That Met QC Criteria: February 12, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Stomatognathic Diseases; Periodontal Diseases; Mouth Diseases; Tooth Demineralization; Tooth Diseases; Jaw Diseases; Dental Caries; Tooth Loss; Periapical Diseases
• Other Study ID Numbers: CT/2023/1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No