Near Infrared Fluorescence Imaging to Assess Breast Flap Perfusion and Prevention of Ischemia: Case Report
Bibliographic record
Abstract
Introduction: Breast skin flap ischemia and necrosis is a serious consequence of breast reconstruction with reported incidence of 10% to 15%. Current clinical method of assessing breast skin flap perfusion includes combination of skin color, temperature, dermal edge bleeding, and capillary refill. Given the high incidence of ischemia and the presence of certain cohort of patient with greater risk, there is a need for an objective method of assessing the skin flap perfusion. Near infrared fluorescence imaging using indocyanine green is a new technique of assessing skin flap perfusion. We present a first reported case where breast skin flap ischemia was objectively identified intraoperatively by NIRF imaging, and this ischemia was reversed through active intra- and postoperative measures, which was objectively again identified by NIRF imaging. Case presentation: A 36-year-old female patient underwent bilateral nipple sparing mastectomies with immediate reconstruction using tissue expanders. Clinical assessment of the breast skin flap was equivocal. NIRF imaging using SPY imaging system (Novadaq Technologies Inc, Toronto, ON, Canada) showed poor perfusion in the periareolar region. As a result, it was decided to completely deflate the expander, actively warm the skin flap and encourage cutaneous perfusion through use of topical glycerin trinitrate patch. A repeat NIRF imaging 48 hours showed 10-fold increase in perfusion in the skin flap. Conclusion: NIRF imaging is a useful tool in assessing breast skin perfusion and to predict potential ischemia, which can aid in surgeon's management of patient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".