The Role of Artificial Intelligence in Predicting Flap Outcomes in Plastic Surgery: Protocol of a Systematic Review
Bibliographic record
Abstract
Introduction: Free flap surgery encompasses reconstruction of diverse tissue defects. Flap failure and complications such as infection and ischemia remain a concern following flap surgery, with the current post-operative standard of care being frequent bedside monitoring. Artificial intelligence such as machine learning models could help support surgeons in postoperative monitoring and predicting complications. The purpose of this systematic review is to provide the framework for a review analyzing the existing literature behind the use of artificial intelligence in assessing flap surgery outcomes and predicting postoperative complications. Methods: A systematic review will be conducted using EMBASE and MEDLINE (1974 to October 2021) to identify relevant literature. This will include studies investigating Artificial Intelligence and machine learning models used in the postoperative setting of flap surgery. Primary outcomes will include evaluating the accuracy of evaluating outcomes following flap surgery based on these models, including: flap success, healing and complications up to 1 month following surgery. Secondary outcomes include the analysis of benefits and drawbacks of using machine learning models for outcomes following flap surgery. Studies will be screened by two independent reviewers; risk of bias will be assessed using the Cochrane risk of bias tool with methodological quality assessed using the QUADAS-2 tool. Discussion: This protocol will provide the framework for a review summarizing the current literature exploring the role of Artificial Intelligence for flap surgery outcomes. Results will help provide surgeons with an overview of current applications and identify areas of potential further research and development. Conclusion: As current clinical practice is regular bedside monitoring, integrating Artificial Intelligence could make the process more efficient, accurate and safer for patients and reduce labour burden or healthcare system costs. This review can help identify areas of potential and improvement which could further aid achieving successful outcomes following flap surgery.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".