Clinical Applications of Three-Dimensional Photography in Breast Surgery
Bibliographic record
Abstract
Three-dimensional imaging in breast surgery has several uses clinically. The most practical applications are for the evaluation of breast asymmetries, both congenital and acquired, and for the evaluation of factors affecting breast shape in augmentation mammaplasty. Other uses of three-dimensional imaging that we have found clinically helpful are for evaluation of patients desiring reduction mammaplasty and for evaluation of patients undergoing unilateral breast reconstruction to determine the expander and permanent implant size that gives the best symmetry with the contralateral breast. We present five cases in which we investigate the use of three-dimensional imaging clinically by using the images to determine quantitative information about the breast, such as volume or projection. Overall, three-dimensional imaging is very helpful in providing objective information about the breast for use in preoperative planning. In addition, by analyzing clinical cases, it can provide objective data about the breast and surgical mammaplasty (especially augmentation mammaplasty) that may help surgeons better understand those factors that contribute to breast shape and influence surgical outcomes. There are currently some limitations of this system, influenced by patients with significant ptosis or obesity, which may introduce errors into the three-dimensional data, making them unreliable. However, we believe three-dimensional imaging has great clinical potential in surgical mammaplasty.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".