A new study published in Annals of Gastroenterological Surgery asks whether machine learning could tip that balance. Researchers from the University Hospital of Split in Croatia analyzed the records of 1,547 adults operated on for suspected appendicitis between January 2020 and June 2024, ultimately assembling a rigorously curated dataset of 623 patients. Of these, 66 had a histologically normal appendix, 248 had uncomplicated appendicitis, and 309 had complicated appendicitis confirmed by pathologists. Crucially, every case was verified by histopathology, eliminating the diagnostic guesswork that plagues many prior studies in which patients were classified by clinical or radiological impression alone.

The team built and compared four machine learning models: logistic regression, random forest, balanced random forest, and XGBoost, an extreme gradient boosting algorithm. All models relied exclusively on routinely available clinical and laboratory data—age, sex, symptom duration, fever, nausea, vomiting, rebound tenderness, pain migration, white blood cell count, neutrophil and lymphocyte percentages, platelet indices, C-reactive protein, and serum sodium. No imaging data were included. Missing values, which ranged from zero to 5.3 percent across variables, were imputed with a bagged trees algorithm carefully configured to prevent data leakage between training and test sets.

The methodological centerpiece of the study was a nested cross-validation design, with five inner and outer folds repeated ten times, yielding fifty independent test evaluations. Hyperparameters were tuned exclusively on inner validation data, and performance was then measured on untouched outer test folds, keeping performance estimates unbiased. On top of that, the researchers applied bootstrap resampling with 2,000 iterations to confirm that results were stable and not artifacts of a particular split of the data.

The most distinctive innovation, however, was threshold optimization. Rather than accepting a default probability cutoff of 0.5, the team deliberately shifted decision thresholds to meet six predefined minimum sensitivity targets, ranging from 0.95 to 0.995. Within each outer fold, thresholds were derived from pooled inner validation predictions, fixed, and then applied unchanged to the test set. The logic is clinical, not statistical: in appendicitis, missing a true case is far more dangerous than operating on a healthy appendix, so a decision support tool must be calibrated to catch virtually every case of appendicitis while identifying the small minority of patients who might safely avoid immediate surgery.

The results were revealing. For detecting acute appendicitis, logistic regression outperformed the more complex algorithms, achieving an area under the ROC curve of 0.765. At the strictest sensitivity target of 0.995, the model detected 99.8 percent of appendicitis cases, with specificity of just 0.038. Translated to a clinical scale, using the observed case distribution, this operating point would miss approximately two appendicitis cases per 1,000 patients while correctly flagging about four patients without appendicitis who could be spared surgery. At the more lenient sensitivity target of 0.95, roughly 41 appendicitis cases per 1,000 would be missed, but approximately 24 patients per 1,000 without appendicitis would avoid an unnecessary operation. The numbers lay bare the inherent trade-off between diagnostic safety and operative selectivity.

For distinguishing complicated from uncomplicated disease, the random forest model performed best, with an AUC of 0.785 and a maximum achievable sensitivity of 0.997 paired with specificity of 0.057. This capability carries real clinical weight: complicated appendicitis generally demands urgent surgery and carries higher morbidity and mortality, whereas some centers now attempt conservative, non-operative management of uncomplicated cases. A model that reliably identifies complicated disease could support more urgent operative decisions, temper enthusiasm for non-operative strategies, or prompt additional imaging and closer monitoring when the predicted risk is ambiguous.

To address the notorious black-box problem that undermines clinical trust in machine learning, the researchers applied SHAP analysis, a framework from explainable artificial intelligence that quantifies each feature’s contribution to individual predictions. For appendicitis detection, neutrophil percentage, the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, sex, and lymphocyte percentage emerged as the most influential predictors. For complication prediction, C-reactive protein, age, symptom duration, and the neutrophil-to-lymphocyte ratio dominated. These rankings align closely with the pathophysiology of appendicitis, in which bacterial invasion of the appendiceal mucosa triggers a systemic inflammatory cascade, and they mirror the feature importances reported by other research groups using different algorithms and populations.

The authors are careful to frame what these models are and are not. The cohort consisted exclusively of adults already selected for surgery by a surgeon’s judgment, so the models should not be treated as stand-alone diagnostic tools for undifferentiated abdominal pain in the emergency department, nor as replacements for ultrasound or CT where those are routinely available. Instead, the researchers envision them as adjunctive decision-support and reassessment tools: a low-risk prediction should trigger repeat examination, laboratory reassessment, or additional imaging rather than discharge, particularly in settings with limited imaging access or after equivocal imaging findings. Notably, the superiority of the simple linear model over tree-based methods in this adult cohort contrasts with findings in pediatric populations, suggesting that inflammatory markers relate to appendicitis in a more linear fashion in adults and underscoring the need for population-specific models.

Limitations remain. The study was retrospective and single-center, and its performance may reflect local diagnostic pathways, surgeon thresholds, and imaging practices that do not transfer to other institutions. The dataset is also surgically enriched, meaning there are no true negative patients who avoided surgery, so specificity must be interpreted conditionally within the operated population. Still, the combination of histopathological verification, rigorous nested cross-validation, bootstrap stability testing, deliberate sensitivity-first threshold calibration, and transparent SHAP-based interpretability marks this work as a methodologically serious step toward machine learning that could meaningfully reduce negative appendectomies. The authors call for prospective, multicenter external validation before clinical deployment, along with future exploration of multimodal models integrating imaging and of tools to help select patients with uncomplicated appendicitis for non-operative management—a direction that could ultimately reshape how one of surgery’s most routine emergencies is decided.

Subject of Research: Machine learning with sensitivity-focused threshold optimization to reduce negative appendectomies in adults with suspected acute appendicitis

Article Title: Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Article References: Males, I., Kumric, M., Boban, Z., Vrdoljak, J., Pecenkovic, D., Ivanda, M., Grahovac, M., Pogorelic, Z., & Bozic, J. (2026). Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults. Annals of Gastroenterological Surgery, 10(5), 1631-1645. https://doi.org/10.1002/ags3.70225

Image Credits: AI Generated

DOI: 10.1002/ags3.70225

Keywords: machine learning, appendicitis, negative appendectomy, logistic regression, random forest, XGBoost, threshold optimization, SHAP explainability, clinical decision support, C-reactive protein, nested cross-validation, emergency surgery