Smartphone Thermal Imaging as an Adjunct to Identify Free Flap Perforators and Assisting Flap Design: A Pilot Study
Bibliographic record
Abstract
ABSTRACT Background The main technique for identification of free flap perforator vessels is Doppler sonography, which is not always accurate, user dependent and affected by the patient's body habitus. Methods Adult patients undergoing head and neck resection and free flap reconstruction at two academic institutions were enrolled. Doppler sonography was used to identify perforators, and were marked using a skin marker. The donor site was cooled down for 3 min using a sterile iced saline bag. FLIR‐ONE (FLIR Systems Inc., Wilsonville, OR) camera was used to assess for “hot spots” during a 3–5 min period of re‐warming as a surrogate for cutaneous blood flow. The distance between the Doppler signal location, and the “hot spot” was recorded. The position of the perforator was then identified intraoperatively and the distances between the surgical position, the Doppler and “hot spot” were recorded. Results A total of 28 patients were included. For all flap types, FLIR thermal imaging measurements consistently tended to be closer to the surgical site compared to Doppler ultrasound. In anterolateral thigh flaps (n = 20), thoracodorsal artery perforator flaps (n = 5), and fibula osteocutaneous flaps (n = 3), absolute mean differences ranged from 0.62 to 1.33 cm, with trends favoring FLIR. While paired t‐tests did not reach statistical significance, both methods correlated with intraoperatively identified skin perforators, and distances generally ranged between 0 and 2 cm. Conclusion We demonstrate that a smartphone‐based thermal imaging system has the potential to serve as an adjunct for identifying flap perforators, with the possibility of reducing operative times and minimizing patient morbidity. Level of Evidence Level 3.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".