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Completed

NCT Number: NCT06258798

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

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.

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Key information

Conditions

Oral Health Dental Caries Jaw Diseases Mouth Diseases Periapical Diseases Periodontal Diseases Stomatognathic Diseases Tooth Demineralization Tooth Diseases Tooth Loss

Age range

11 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Maxillofacial Surgery

Kielce, 25-375, Poland

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

Taking a dental X-ray

Radiation

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

Primary outcomes

  1. Sensitivity

    Time frame: Up to 6 weeks

    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

  2. Specificity

    Time frame: Up to 6 weeks

    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

  3. Precision of the AI algorithm

    Time frame: Up to 6 weeks

    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

Sponsors and collaborators

Lead sponsor

Hospital of the Ministry of Interior, Kielce, Poland

Other

Registry information

Official study title

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

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
Feb 14, 2024
Registry last updated
Apr 1, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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