Using augmented reality to improve pre-surgical decisionmaking among breast cancer patients
Bibliographic record
Abstract
Most breast cancer patients will undergo surgery as part of their treatment plan. To improve their chances of survival, they typically need to decide on their treatment plan within eight weeks of diagnosis. Patients often review post-operative images of others to gauge potential outcomes, but these images provide only limited insight into their own possible results resulting in many revision surgeries and patients who are dissatisfied with their surgical outcomes. In this dissertation, we explore the use of augmented reality (AR) as a decision-support tool to help patients visualize different surgical procedures on their own bodies. To that end, we developed Breamy, an AR app that uses both marker-based and markerless AR visualizations. Breamy uses photogrammetry to create a patient-specific 3D model. This model, along with various treatment options, is projected directly onto the patient’s body to help visualize different surgical options. We surveyed 165 women on their views about the concept of Breamy and found positive results in terms of the need for such an application. We also ran a preliminary study with six participants to evaluate the usability of Breamy and its potential as a decision-aid tool. The findings of these studies suggest that AR can be an effective decision-support tool, helping to better align patient expectations with likely outcomes. For example, in our preliminary study, 90\% of participants believed that an AR application with personalized surgical information could improve patient comprehension and decision-making. Similarly, in our second study, five out of six participants reported that AR visualization enhanced their understanding of surgery's potential effects on their bodies and boosted their confidence in the decision-making process. The results of our studies underscore the potential of Breamy to transform the surgical decision-making process, ultimately leading to greater patient satisfaction and improved surgical 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".