ArticleHernia : the journal of hernias and abdominal wall surgery2026
SurgiCut-AI: an AI-driven tool for predicting surgical site infections following ventral hernia repair using pre- and intra-operative parameters.
Article in Hernia : the journal of hernias and abdominal wall surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Can preoperative optimization improve abdominal wall surgery outcomes? a qualitative systematic review.Hernia : the journal of hernias and abdominal wall surgery · 2026Pooled it
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Machine learning-based prediction of carbapenem-resistantFrontiers in pharmacology · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSurgical Site Infection (SSI) is the common consequence of hernia surgery. This study aimed to develop an artificial intelligence (AI)-driven predictive model for SSI following open ventral hernia repair using pre- and intra-operative parameters for early detection.
methodsA prospective analysis of ventral hernia patients was conducted. Demographic, pre-operative and intra-operative parameters were collected. Feature selection using Recursive Feature Elimination (RFE) identified key predictors. Five models, eXtreme Gradient Boosting (XGBoost), logistic regression, support vector machine (SVM), Adaptive Boosting and random forest (RF), were trained and validated.
resultsAmong 253 patients, 22 were SSI-positive. RF demonstrated the highest predictive power (area under receiver operating characteristic curve = 0.82) and diagnostic odds ratio (DOR = 55). Seven key predictors were identified: defect size area, HbA1c, lymphocytes, neutrophil-to-lymphocyte ratio, blood loss (ml), platelet count and serum albumin. Decision curve analysis showed an estimated risk stratification at an 8.3% cutoff " https://surgicut-ai.netlify.app/ ".
conclusionThe model's high accuracy supports its potential for clinical decision-making. External validation is required for real-world application. Integrating this AI model into bedside tools may enhance SSI prevention, especially in resource-limited settings.
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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.