Accuracy of an Artificial Intelligence-assisted Diagnostic System for Caries Diagnosis: a Prospective Multicenter Clinical Study

This clinical trial was designed as a prospective, multicenter, multi-reader multi-case (MRMC), superiority, parallel-controlled study. Participants who met the trial criteria and signed the informed consent form were enrolled. The trial group involved diagnoses of caries on panoramic radiographs using an artificial intelligence-assisted diagnostic system, while the control group involved diagnoses made by dental practitioners specializing in operative dentistry and endodontics with five years of experience, who interpreted oral panoramic radiographs to determine the presence and severity of caries.

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Key information

Conditions

Dental Caries Artificial Intellegence Diagnosis Disease Machine Learning Pathologic Processes Pathological Conditions, Signs and Symptoms Stomatognathic Diseases Tooth Demineralization Tooth Diseases

Age range

18 year–70 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

Patients presenting with clinical manifestations of caries as their chief complaint;
Age ≥18 and ≤70 years, irrespective of gender;
Oral panoramic radiographs showing a complete dentition, specifically with the second molars and all premolars intact in each quadrant;
On the oral panoramic radiographs, the number of teeth with restorations or fillings does not exceed one in any quadrant;
Oral panoramic radiographs that are clear, easy to interpret, and free from significant artifacts;
Participants who voluntarily agree to partake, can comprehend the purpose of the study, and are capable of signing an informed consent form.

Exclusion criteria

Oral panoramic radiographs that are unclear, with overlapping, blurring, or artifacts present;
Insufficient number of teeth available for study;
Severe tooth wear or erosion leading to significant alteration in tooth morphology;
Presence of supernumerary teeth, microdontia, or missing teeth;
Conditions not suitable for oral radiography, such as pregnancy or undergoing radiation therapy for tumors;
Limited mouth opening that precludes clinical examination;
Neurological disorders, psychiatric illnesses, or psychological impairments;
Participation in another clinical trial within the last three months;
Any other condition deemed by the researchers as unsuitable for inclusion in the study.

Treatment and study plan

Primary outcomes

Sensitivity, specificity, and accuracy of caries diagnosis

Time frame: Immediately after the completion of all participant information collection.

Sensitivity: Sensitivity assesses the diagnostic method's ability to identify the disease, that is, how many true cases of caries can be correctly identified among all actual cases of caries.

Specificity: Specificity evaluates the diagnostic tool's ability to recognize the absence of disease, meaning how many true non-caries cases can be correctly identified among all actual non-caries cases.

Accuracy: Accuracy evaluates the overall correctness of the diagnostic tool in diagnosing the disease.

Secondary outcomes

Miss rate and false positive rate of caries diagnosis

Time frame: Immediately after the completion of all participant information collection.

Miss rate (False Negative Rate): The miss rate refers to the proportion of true caries cases that the diagnostic method fails to correctly identify among all actual cases of caries. In this study, the effectiveness of the artificial intelligence-assisted diagnostic system is evaluated by comparing the miss rate of caries diagnosis between the artificial intelligence-assisted diagnostic system and dentists with five years of experience.

False positive rate: The false positive rate refers to the proportion of cases that are incorrectly identified as having caries among all actual non-caries cases. In this study, the effectiveness of the artificial intelligence-assisted diagnostic system is evaluated by comparing the false positive rate of caries diagnosis between the artificial intelligence-assisted diagnostic system and dentists with five years of experience.
Accuracy of Caries Severity Level Diagnosis

Time frame: Immediately after the completion of all participant information collection.

This study utilizes the International Caries Detection and Assessment System (ICDAS) to evaluate the grading of caries severity, categorizing caries severity into five levels: E0, E1, E2, D1, and D2. Wherein: E0 indicates no apparent signs of caries; E1 represents early caries, primarily initial enamel lesions; E2 designates enamel caries without involvement of the dentin; D1 denotes superficial dentin caries, not reaching half of the dentin thickness; D2 signifies deep dentin caries, reaching or exceeding half of the dentin thickness. This study will assess and compare the diagnostic accuracy at each grading level between the trial group and the control group.

Sponsors and collaborators

Lead sponsor

Zhejiang Provincial People's Hospital

Other

Registry information

Important dates

Study start: 2024
Primary completion: 2026
Study completion: 2027
First posted: May 24, 2024
Registry last updated: Jul 10, 2024

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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