ReviewAnnals of surgical oncology2023
The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction.
Review in Annals of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Diagnostic Accuracy of AI in Prediction and Assessment of Compromised Free Flaps: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The use of artificial intelligence in reconstructive surgery for head and neck cancer: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Pooled it
- A Machine Learning Model to Predict Complications Following Head and Neck Microvascular Surgery.Laryngoscope investigative otolaryngology · 2026Article
- Learning Curve and Technical Strategies in 100 Consecutive Head and Neck Free-Flap Reconstructions by an Early-Career Plastic Surgeon.Journal of plastic and reconstructive surgery · 2026Article
- The Gemini Eye in Microsurgery: Video-Based Capillary Refill and Chromatic Assessment for Free Flap Monitoring.Journal of clinical medicine · 2026Article
- Review
- Predicting Breast Reconstruction Readmission, Reoperation, and Prolonged Length of Stay: A Machine Learning Approach.Journal of surgical oncology · 2025Article
- Artificial Intelligence in Microsurgical Planning: A Five-Year Leap in Clinical Translation.Journal of clinical medicine · 2025Review
- Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.Veterinary surgery : VS · 2025Article
- Perioperative laboratory profiles predict complications after extensive head and neck reconstruction: a proof-of-concept study.Frontiers in oncology · 2025Article
- Decoding the Impact of AI on Microsurgery: Systematic Review and Classification of Six Subdomains for Future Development.Plastic and reconstructive surgery. Global open · 2024Article
- Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.Current oncology (Toronto, Ont.) · 2024Review
- Predicting reoperation and readmission for head and neck free flap patients using machine learning.Head & neck · 2024Article
- Transforming breast reconstruction: the pioneering role of artificial intelligence in preoperative planning.Gland surgery · 2023Article
- The Use of Machine Learning for Predicting Complications of Free Flap Head and Neck Reconstruction: Caution Needed.Annals of surgical oncology · 2023Article
- Innovative Strategies in Microvascular Head and Neck Reconstruction.Medicina (Kaunas, Lithuania) · 2023Review
- Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction.
methodsWe conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018. Data were used to develop and evaluate nine supervised ML algorithms in predicting overall complications, major recipient-site complication, and total flap loss.
resultsWe identified 4000 patients who met inclusion criteria. Overall, 33.7% of patients experienced a complication, 26.5% experienced a major recipient-site complication, and 1.7% suffered total flap loss. The k-nearest neighbors algorithm demonstrated the best overall performance for predicting any complication (AUROC = 0.61, sensitivity = 0.60). Regularized regression had the best performance for predicting major recipient-site complications (AUROC = 0.68, sensitivity = 0.66), and decision trees were the best predictors of total flap loss (AUROC = 0.66, sensitivity = 0.50).
conclusionsML accurately identified patients at risk of experiencing postsurgical complications, including total flap loss. Predictions from ML models may provide insight in the perioperative setting and facilitate shared decision making.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.