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.
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 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;
Patients visiting the all the three centers during the study period with symptoms of caries as their chief complaint.
Other
Prospective
Not listed
Sensitivity, specificity, and accuracy of caries diagnosis
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.
Miss rate and false positive rate of caries diagnosis
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
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.
Click on the lead sponsor to view all of their trials.
This trial is not recruiting at the moment. You can still explore other options:
