Intraoperative Cone‐Beam Computed Tomography and Multi‐Slice Computed Tomography in Temporal Bone Imaging for Surgical Treatment
Bibliographic record
Abstract
OBJECTIVE: Conventional computed tomography (CT) imaging is the standard imaging technique for temporal bone diseases, whereas cone-beam CT (CBCT) imaging is a very fast imaging tool with a significant less radiation dose compared with conventional CT. We hypothesize that a system for intraoperative cone-beam CT provides comparable image quality to diagnostic CT for identifying temporal bone anatomical landmarks in cadaveric specimens. STUDY DESIGN: Cross-sectional study. SETTING: University tertiary care facility. SUBJECTS AND METHODS: Twenty cadaveric temporal bones were affixed into a head phantom and scanned with both a prototype cone-beam CT C-arm and multislice helical CT. Imaging performance was evaluated by 3 otologic surgeons and 1 head and neck radiologist. Participants were presented images in a randomized order and completed landmark identification questionnaires covering 21 structures. RESULTS: CBCT and multislice CT have comparable performance in identifying temporal structures. Three otologic surgeons indicated that CBCT provided statistically equivalent performance for 19 of 21 landmarks, with CBCT superior to CT for the chorda tympani and inferior for the crura of the stapes. Subgroup analysis showed that CBCT performed superiorly for temporal bone structures compared with CT. The radiologist rated CBCT and CT as statistically equivalent for 18 of 21 landmarks, with CT superior to CBCT for the crura of stapes, chorda tympani, and sigmoid sinus. CONCLUSION: CBCT provides comparable image quality to conventional CT for temporal bone anatomical sites in cadaveric specimens. Clinical applications of low-dose CBCT imaging in surgical planning, intraoperative guidance, and postoperative assessment are promising but require further investigation.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".