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| Name | Class |
|---|---|
| Ain Shams University | OTHER |
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Using a sequence of bitewing radiographs, Artificial intelligence assists in identifying interproximal caries. For the identification of dental caries in bitewing, periapical, and panoramic radiographs, a trained deep learning network will be created This study aimed to investigate the reliability of a novel Artificial Intelligence model based on deep learning in the detection of Proximal Caries using Digital Bitewing Radiographs. (BW).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group 1 Artificial Intelligence Deep learning that is applied in Diagnosis of the proximal Caries |
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| Group 2 : Digital Bitewing manually annotated by human experts |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries | Diagnostic Test | Artificial intelligence was used as a deep-learning diagnostic tool to detect proximal caries on digital bitewing radiographs. The system analyzed images and generated probability scores and visual markers for suspected lesions. Its performance was compared with expert examiner diagnoses as the reference standard. AI results were used for evaluation only and did not influence patient treatment decisions. |
| Measure | Description | Time Frame |
|---|---|---|
| Reliability of the artificial intelligence model in detecting proximal caries on digital bitewing radiographs | cross-sectional assessment at baseline, with no follow-up period |
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Inclusion Criteria:
Exclusion Criteria:
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Patients attending the Faculty dental out patient clinic who required bitewing radiographic examination for routine diagnosis or treatment planning.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Ain Shams University | Cairo | 11331 | Egypt |
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| Manual annotation of Digital Bitewing Radiograph by human experts | Diagnostic Test | Digital bitewing radiographs were manually annotated by calibrated human experts to identify the presence and location of proximal caries. Annotations were performed using standardized diagnostic criteria and dedicated imaging software to mark suspected lesions. These expert markings served as the reference standard for comparison with the artificial intelligence outputs. Inter-examiner agreement was assessed, and disagreements were resolved by consensus. |
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| ID | Term |
|---|---|
| D003731 | Dental Caries |
| ID | Term |
|---|---|
| D017001 | Tooth Demineralization |
| D014076 | Tooth Diseases |
| D009057 | Stomatognathic Diseases |
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| ID | Term |
|---|---|
| D001185 | Artificial Intelligence |
| ID | Term |
|---|---|
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
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