ArticleOncotarget2018
Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review.International wound journal · 2024Pooled it
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- A Machine Learning Model to Predict Complications Following Head and Neck Microvascular Surgery.Laryngoscope investigative otolaryngology · 2026Article
- Deep learning Algorithm for Wound assessment after total kNee (DAWN) arthroplasty : a prospective study protocol.Bone & joint open · 2026Article
- Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models.NPJ digital medicine · 2025Article
- Review
- Artificial Intelligence in Microsurgical Planning: A Five-Year Leap in Clinical Translation.Journal of clinical medicine · 2025Review
- [Application of iceberg theory in the diagnosis and treatment of refractory wounds at the surgical site after craniotomy].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2025Article
- Enhancing Infection Control in ICUS Through AI: A Literature Review.Health science reports · 2025Article
- Age Reduction After Face-Lift Surgery in Chinese Population: An Outcome Study Using Artificial Intelligence and Objective Observer-Based Assessment.Aesthetic plastic surgery · 2024Article
- Decoding the Impact of AI on Microsurgery: Systematic Review and Classification of Six Subdomains for Future Development.Plastic and reconstructive surgery. Global open · 2024Article
- Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?The Journal of international medical research · 2024Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Development of a knowledge-based healthcare-associated infections surveillance system in China.BMC medical informatics and decision making · 2023Article
- A History of Innovation: Tracing the Evolution of Imaging Modalities for the Preoperative Planning of Microsurgical Breast Reconstruction.Journal of clinical medicine · 2023Review
- The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction.Annals of surgical oncology · 2023Review
- A Surgeon's Guide to Artificial Intelligence-Driven Predictive Models.The American surgeon · 2023Article
- Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications.The American surgeon · 2023Article
- A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future.Plastic and reconstructive surgery. Global open · 2022Article
- Influence of Renal Impairment on the Success of Reconstruction Using Microvascular Grafts-A Retrospective Study of 251 Free Flaps.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to develop an effective surgical site infection (SSI) prediction model in patients receiving free-flap reconstruction after surgery for head and neck cancer using artificial neural network (ANN), and to compare its predictive power with that of conventional logistic regression (LR). MATERIALS AND
methodsThere were 1,836 patients with 1,854 free-flap reconstructions and 438 postoperative SSIs in the dataset for analysis. They were randomly assigned tin ratio of 7:3 into a training set and a test set. Based on comprehensive characteristics of patients and diseases in the absence or presence of operative data, prediction of SSI was performed at two time points (pre-operatively and post-operatively) with a feed-forward ANN and the LR models. In addition to the calculated accuracy, sensitivity, and specificity, the predictive performance of ANN and LR were assessed based on area under the curve (AUC) measures of receiver operator characteristic curves and Brier score.
resultsANN had a significantly higher AUC (0.892) of post-operative prediction and AUC (0.808) of pre-operative prediction than LR (both
conclusionThe post-operative prediction by ANN had the highest overall performance in predicting SSI after free-flap reconstruction in patients receiving surgery for head and neck cancer.
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