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

Dental Caries Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06258798
• Other study ID #: CT/2023/1
• Status: Recruiting
• Start date: January 1, 2024
• Est. completion date: March 1, 2024

Study information

• Verified date: February 2024
• Source: Hospital of the Ministry of Interior, Kielce, Poland
• Contact: Maciej Sikora, dr hab.
• Phone: +48 41 260 55 85
• Email: sikora-maciej[@]wp.pl
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1025
• Est. completion date: March 1, 2024
• Est. primary completion date: February 23, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 11 Years and older

Eligibility

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)

Related Conditions & MeSH terms

• Dental Caries
• Oral Health
• Periapical Diseases
• Tooth Loss

Intervention

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

Locations

Country	Name	City	State
Poland	Department of Maxillofacial Surgery	Kielce

Sponsors (1)

Lead Sponsor	Collaborator
Hospital of the Ministry of Interior, Kielce, Poland

Country where clinical trial is conducted

Poland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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
Primary	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