Dental Caries, Oral Health, Periapical Diseases, Tooth Loss
Conditions
Brief 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.
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.
Interventions
RADIATIONTaking a dental X-ray
Dental X-rays taken in patients with indications confirmed by a written referral.
Sponsors
Hospital of the Ministry of Interior, Kielce, Poland
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
11 Years to No maximum
Healthy volunteers
No
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | 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 |
| Specificity | 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 |
| Precision of the AI algorithm | 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 |
Countries
Poland
Outcome results
None listed