Groupe pour l’Avancement de la Microchirurgie Canada (GAM) Abstracts presented at the 43 <sup>rd</sup> Annual Meeting / 43 <sup>e</sup> Réunion annuelle June 19, 2024 Halifax, NS / N-É
Bibliographic record
Abstract
PURPOSE: The current gold standard for free flap monitoring is clinical examination, the accuracy of which is dependent on the assessor.The purpose of this study is to demonstrate the validity of data augmentation using artificial intelligence (AI) generated images of postoperative free flaps and assess the effectiveness of a custom-trained AI model in assessing free flap viability.METHOD: Images of postoperative free flaps were obtained from a literature search and labelled as healthy or congested.The dataset was split into a 80:20 ratio for training and testing.To expand the training dataset, data augmentation was done by creating images of flaps using Midjourney, a generative image AI software.Two attending plastic surgeons graded the quality of each AI flap image on a scale of 1 to 5; only those with a total score of 10 were included.Microsoft Azure AI, an image recognition service, was used to train and test the model on the dataset.For each test image, a probability of 80% or higher was considered a correct answer.RESULT: Seventy-one flap images were collected -58 images (30 healthy, 28 congested) were used for training and 13 images (7 healthy, 6 congested) were used for testing.Thirty images of flaps
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".