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In this work, the investigators study the application of artificial intelligence systems on dental panoramic images for dental findings. An artificial intelligence system will be learned on an publicly available panoramic image dataset, and test against the investigators' local patient cohort as external test data. The investigators hypothesize the performance would be similar, if not identical to on the public data, and that the investigators' AI system is generalizable.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| All patients with relevant dental findings |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Dental Panoramic Radiographs | Radiation | Exposure to dental panoramic radiographs |
|
| Measure | Description | Time Frame |
|---|---|---|
| Area Under Curve for Receiver Operating Characteristics of Clinical Findings | The investigators use the AI model to infer whether a clinical finding (out of the six type of findings the investigators are interested in) is present in an imaging study from the test set. The result is compared against expert annotation and evaluated for receiver operating characteristics over the whole set. The area under curve will then be calculated and averaged across six type of findings to represent the overall efficacy of the model on detecting findings from panoramic images. From a scale of zero to one, zero is the worst this metric can be and one is the best. | 1 day |
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Exclusion Criteria:
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all patients with panoramic image exposure and not with exclusion criteria
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Taiwan University Hospital | Taipei | 100 | Taiwan |
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| ID | Term |
|---|---|
| D009057 | Stomatognathic Diseases |
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