Unveiling incidental findings: A clinical review of a giant aneurysm detected by CBCT imaging
Bibliographic record
Abstract
Cone beam computed tomography (CBCT) is an advanced imaging modality that has transformed the field of dentistry with its high-resolution and three-dimensional (3D) imaging capabilities. It has proven to be an invaluable tool for diagnostic and treatment planning purposes, and the increased use of large CBCT imaging has led to a corresponding rise in unexpected incidental findings, especially in orthodontics and implant planning. These incidental findings may be clinically significant and indicate medical emergencies requiring immediate intervention or referral. This clinical review presents a case of a 64-year-old female who was referred for a CBCT examination to assess a radiopacity in the left maxillary sinus. Incidentally, the scan revealed a giant internal carotid artery (ICA) aneurysm in the right middle cranial fossa. Prompt recognition and proper management led to a successful intervention using endovascular surgical treatment, potentially averting a catastrophic rupture. This case demonstrates the critical role of CBCT imaging in identifying potentially life-threatening conditions beyond the primary area of interest. In our review, we underscore the ethical responsibilities of dental practitioners in managing incidental findings. Practitioners must ensure that CBCT scans in their entirety are fully assessed and interpreted, and must stay current with the guidelines for advanced imaging interpretation. Proper management entails the proper communication and documentation of incidental findings, and subsequent referral to the appropriate specialists. By fulfilling these responsibilities, dental professionals can continue to uphold the highest standards of patient care, reducing the risk of missed diagnoses and enhancing interdisciplinary collaboration for optimal patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".