Validity of Machine Learning in Detecting Complicated Appendicitis in a Resource-Limited Setting: Findings from Vietnam

Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model

BACKGROUND

Acute appendicitis (AAp) is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures. Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms; hence, negative AAp and complicated AAp are the primary concerns in research on AAp. In other terms, further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.

AIM

To use a Stochastic Gradient Boosting (SGB)-based machine learning (ML) algorithm to tell the difference between AAp patients who are complicated and those who are not, and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.

METHODS

This study analyzed an open access data set containing 140 people, including 41 healthy controls, 65 individuals with uncomplicated AAp, and 34 individuals with complicated AAp. We analyzed some demographic data (age, sex) of the patients and the following biochemical blood parameters: White blood cell (WBC) count, neutrophils, lymphocytes, monocytes, platelet count, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, mean platelet volume, neutrophil-to-immature granulocyte ratio, ferritin, total bilirubin, immature granulocyte count, immature granulocyte percent, and neutrophil-to-immature granulocyte ratio. We tested the SGB model using n-fold cross-validation. It was implemented with an 80-20 training-test split. We used variable importance values to identify the variables that were most effective on the target.

RESULTS

The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%, a micro aera under the curve (AUC) of 94.7%, a sensitivity of 94.7%, and a specificity of 100%. In distinguishing complicated AAp patients from uncomplicated ones, the model achieved an accuracy of 78.9%, a micro AUC of 79%, a sensitivity of 83.3%, and a specificity of 76.9%. The most useful biomarkers for confirming the AA diagnosis were WBC (100%), neutrophils (95.14%), and the lymphocyte-monocyte ratio (76.05%). On the other hand, the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin (100%), WBC (96.90%), and the neutrophil-immature granulocytes ratio (64.05%).

CONCLUSION

The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients. Although the model's accuracy in the classification of complicated AAp is moderate, the high variable importance obtained is clinically significant. We need further prospective validation studies, but the integration of such ML algorithms into clinical practice may improve diagnostic processes.

Complicated appendicitis in low- and lower-middle-income countries: a systematic review and meta-analysis

BACKGROUND

Acute appendicitis is a common surgical emergency worldwide, with significant variations in prevalence, presentation and outcomes between high-income countries (HIC) and low- and middle-income countries (LMIC). Complicated appendicitis has significant implications for low- and lower-middle-income countries due to limited healthcare resources. While there is extensive evidence for HICs, the evidence for low- and lower-middle-income countries is lacking. This systematic review aimed to compare the prevalence of complicated appendicitis between low-income countries (LICs) and lower-middle-income countries (LoMICs).

METHODS

A systematic review was conducted following PRISMA guidelines (PROSPERO CRD42024526007). Observational studies and randomized controlled trials published in PubMed, MEDLINE, Embase and Scopus (1990-2024) were retrieved. A grey literature search of Google Scholar and the Cochrane Library was also performed to identify existing reviews on the topic. The primary outcome investigated was the incidence of complicated appendicitis.

RESULTS

Eighty-seven articles with 25 582 participants were included. Meta-analyses of outcomes comparing LICs to LoMICs identified an increased pooled proportion of complicated appendicitis (34% (95% CI 27%-41%) vs. 23% (95% CI 19%-27%) P < 0.001), increased post-operative morbidity (19% (95% CI 13%-27%) vs. 13% (95% CI 8%-20%): P < 0.01) and mortality (OR 2.36).

CONCLUSION

Appendicitis remains a major burden in LICs and LoMICs with higher rates of morbidity and mortality. These findings highlight critical deficiencies in surgical access and delivery, underscoring the need for targeted interventions to improve outcomes in these settings.

Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling

PURPOSE

The distinction between complicated appendicitis (CAP) and uncomplicated appendicitis (UAP) remains challenging. The purpose of this study was to construct a safe and economical diagnostic model that can accurately and rapidly differentiate between CAP and UAP.

METHODS

Patient data from 773 appendectomies were retrospectively collected, important features were selected using random forests, and the data were divided into training and test sets in a 3:1 ratio. An integrated learning algorithm, Extreme Gradient Boosting (XGBoost), was introduced to predict the risk of CAP and compared with Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (CART) algorithms. A comprehensive comparison of the four algorithms was performed using model performance metrics such as the area under the receiver's operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, negative predictive value(NPV), positive predictive value(PPV),calibration curves, and clinical decision curve analysis (DCA).

RESULT

The results show that all four prediction models exhibit some predictive ability. The XGBoost model showed the best prediction with AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.914, 0.855, 0.865, 0.846,0.848 and 0.897, respectively, followed by the SVM model with results of AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.882, 0.819, 0.865, 0.779, 0.770 and 0.871, respectively. XGBoost and SVM models show very good calibration. The XGBoost model showed better net clinical benefit compared to the DCA curves of the other models.

CONCLUSION

Predictive models based on the XGBoost algorithm have good performance in predicting the risk of acute appendicitis progressing to complicated appendicitis, which helps to optimize clinical decision making.

A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review

Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This study reviews AI applications across all stages of appendicitis care, from triage to postoperative management, using sources from PubMed/MEDLINE, IEEE Xplore, arXiv, Web of Science, and Scopus, covering publications up to 14 February 2025. AI models have demonstrated potential in triage, enabling rapid differentiation of appendicitis from other causes of abdominal pain. In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. These tools also predict disease severity, aiding decisions between conservative management and surgery. Radiomics further enhances diagnostic precision by analyzing imaging data. Intraoperatively, AI applications are emerging to support real-time decision-making, assess procedural steps, and improve surgical training. Postoperatively, ML models predict complications such as abscess formation and sepsis, facilitating early interventions and personalized recovery plans. This is the first comprehensive review to examine AI's role across the entire appendicitis treatment process, including triage, diagnosis, severity prediction, intraoperative assistance, and postoperative prognosis. Despite its potential, challenges remain regarding data quality, model interpretability, ethical considerations, and clinical integration. Future efforts should focus on developing end-to-end AI-assisted workflows that enhance diagnosis, treatment, and patient outcomes while ensuring equitable access and clinician oversight.

Progress in the application of machine learning in CT diagnosis of acute appendicitis

Acute appendicitis represents a prevalent condition within the spectrum of acute abdominal pathologies, exhibiting a diverse clinical presentation. Computed tomography (CT) imaging has emerged as a prospective diagnostic modality for the identification and differentiation of appendicitis. This review aims to synthesize current applications, progress, and challenges in integrating machine learning (ML) with CT for diagnosing acute appendicitis while exploring prospects. ML-driven advancements include automated detection, differential diagnosis, and severity stratification. For instance, deep learning models such as AppendiXNet achieved an AUC of 0.81 for appendicitis detection, while 3D convolutional neural networks (CNNs) demonstrated superior performance, with AUCs up to 0.95 and an accuracy of 91.5%. ML algorithms effectively differentiate appendicitis from similar conditions like diverticulitis, achieving AUCs between 0.951 and 0.972. They demonstrate remarkable proficiency in distinguishing between complex and straightforward cases through the innovative use of radiomics and hybrid models, achieving AUCs ranging from 0.80 to 0.96. Even with these advancements, challenges remain, such as the "black-box" nature of artificial intelligence, its integration into clinical workflows, and the significant resources required. Future directions emphasize interpretable models, multimodal data fusion, and cost-effective decision-support systems. By addressing these barriers, ML holds promise for refining diagnostic precision, optimizing treatment pathways, and reducing healthcare costs.

Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity

PURPOSE

This study evaluates the effectiveness of machine learning (ML) algorithms for improving the preoperative diagnosis of acute appendicitis in children, focusing on the accurate prediction of the severity of disease.

METHODS

An anonymized clinical and operative dataset was retrieved from the medical records of children undergoing emergency appendectomy between 2014 and 2021. We developed an ML pipeline that pre-processed the dataset and developed algorithms to predict 5 appendicitis grades (1 - non-perforated, 2 - localized perforation, 3 - abscess, 4 - generalized peritonitis, and 5 - generalized peritonitis with abscess). Imputation strategies were used for missing values and upsampling techniques for infrequent classes. Standard classifier models were tested. The best combination of imputation strategy, class balancing technique and classification model was chosen based on validation performance. Model explainability was verified by a pediatric surgeon. Our model's performance was compared to another pediatric appendicitis severity prediction tool.

RESULTS

The study used a retrospective cohort including 1980 patients (60.6 % males, average age 10.7 years). Grade of appendicitis in the cohort was as follows: grade 1-70 %; grade 2-8 %; grade 3-7 %; grade 4-7 %; grade 5-8 %. Every combination of 6 imputation strategies, 7 class-balancing techniques, and 5 classification models was tested. The best-performing combined ML pipeline distinguished non-perforated from perforated appendicitis with 82.8 ± 0.2 % NPV and 56.4 ± 0.4 % PPV, and differentiated between severity grades with 70.1 ± 0.2 % accuracy and 0.77 ± 0.00 AUROC. The other pediatric appendicitis severity prediction tool gave an accuracy of 71.4 %, AUROC of 0.54 and NPV/PPV of 71.8/64.7.

CONCLUSION

Prediction of appendiceal perforation outperforms prediction of the continuum of appendicitis grades. The variables our models primarily rely on to make predictions are consistent with clinical experience and the literature, suggesting that the ML models uncovered useful patterns in the dataset. Our model outperforms the other pediatric appendicitis prediction tools. The ML model developed for grade prediction is the first of this type, offering a novel approach for assessing appendicitis severity in children preoperatively. Following external validation and silent clinical testing, this ML model has the potential to enable personalized severity-based treatment of pediatric appendicitis and optimize resource allocation for its management.

LEVEL OF EVIDENCE: 3

Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis

Background: Nonsurgical treatment of uncomplicated appendicitis is a reasonable option in many cases despite the sparsity of robust, easy access, externally validated, and multimodally informed clinical decision support systems (CDSSs). Developed by OpenAI, the Generative Pre-trained Transformer 3.5 model (GPT-3) may provide enhanced decision support for surgeons in less certain appendicitis cases or those posing a higher risk for (relative) operative contra-indications. Our objective was to determine whether GPT-3.5, when provided high-throughput clinical, laboratory, and radiological text-based information, will come to clinical decisions similar to those of a machine learning model and a board-certified surgeon (reference standard) in decision-making for appendectomy versus conservative treatment. Methods: In this cohort study, we randomly collected patients presenting at the emergency department (ED) of two German hospitals (GFO, Troisdorf, and University Hospital Cologne) with right abdominal pain between October 2022 and October 2023. Statistical analysis was performed using R, version 3.6.2, on RStudio, version 2023.03.0 + 386. Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. Statistical significance was defined as p < 0.05. Results: There was agreement between the surgeon’s decision and GPT-3.5 in 102 of 113 cases, and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT-3.5. The estimated model training accuracy was 83.3% (95% CI: 74.0, 90.4), while the validation accuracy for the model was 87.0% (95% CI: 66.4, 97.2). This is in comparison to the GPT-3.5 accuracy of 90.3% (95% CI: 83.2, 95.0), which did not perform significantly better in comparison to the machine learning model (p = 0.21). Conclusions: This study, the first study of the “intended use” of GPT-3.5 for surgical treatment to our knowledge, comparing surgical decision-making versus an algorithm found a high degree of agreement between board-certified surgeons and GPT-3.5 for surgical decision-making in patients presenting to the emergency department with lower abdominal pain.

Appendicitis Diagnosis: Ensemble Machine Learning and Explainable Artificial Intelligence-Based Comprehensive Approach

Appendicitis is a condition wherein the appendix becomes inflamed, and it can be difficult to diagnose accurately. The type of appendicitis can also be hard to determine, leading to misdiagnosis and difficulty in managing the condition. To avoid complications and reduce mortality, early diagnosis and treatment are crucial. While Alvarado’s clinical scoring system is not sufficient, ultrasound and computed tomography (CT) imaging are effective but have downsides such as operator-dependency and radiation exposure. This study proposes the use of machine learning methods and a locally collected reliable dataset to enhance the identification of acute appendicitis while detecting the differences between complicated and non-complicated appendicitis. Machine learning can help reduce diagnostic errors and improve treatment decisions. This study conducted four different experiments using various ML algorithms, including K-nearest neighbors (KNN), DT, bagging, and stacking. The experimental results showed that the stacking model had the highest training accuracy, test set accuracy, precision, and F1 score, which were 97.51%, 92.63%, 95.29%, and 92.04%, respectively. Feature importance and explainable AI (XAI) identified neutrophils, WBC_Count, Total_LOS, P_O_LOS, and Symptoms_Days as the principal features that significantly affected the performance of the model. Based on the outcomes and feedback from medical health professionals, the scheme is promising in terms of its effectiveness in diagnosing of acute appendicitis.

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis

The diagnosis of acute appendicitis and concurrent surgery referral is primarily based on clinical presentation, laboratory and radiological imaging. However, utilizing such an approach results in as much as 10-15% of negative appendectomies. Hence, in the present study, we aimed to develop a machine learning (ML) model designed to reduce the number of negative appendectomies in pediatric patients with a high clinical probability of acute appendicitis. The model was developed and validated on a registry of 551 pediatric patients with suspected acute appendicitis that underwent surgical treatment. Clinical, anthropometric, and laboratory features were included for model training and analysis. Three machine learning algorithms were tested (random forest, eXtreme Gradient Boosting, logistic regression) and model explainability was obtained. Random forest model provided the best predictions achieving mean specificity and sensitivity of 0.17 ± 0.01 and 0.997 ± 0.001 for detection of acute appendicitis, respectively. Furthermore, the model outperformed the appendicitis inflammatory response (AIR) score across most sensitivity-specificity combinations. Finally, the random forest model again provided the best predictions for discrimination between complicated appendicitis, and either uncomplicated acute appendicitis or no appendicitis at all, with a joint mean sensitivity of 0.994 ± 0.002 and specificity of 0.129 ± 0.009. In conclusion, the developed ML model might save as much as 17% of patients with a high clinical probability of acute appendicitis from unnecessary surgery, while missing the needed surgery in only 0.3% of cases. Additionally, it showed better diagnostic accuracy than the AIR score, as well as good accuracy in predicting complicated acute appendicitis over uncomplicated and negative cases bundled together. This may be useful in centers that advocate for the conservative treatment of uncomplicated appendicitis. Nevertheless, external validation is needed to support these findings.

Current use of artificial intelligence in the diagnosis and management of acute appendicitis

INTRODUCTION

Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives.

EVIDENCE ACQUISITION

We performed a literature search on articles published from 2018 to September 2023. We included only original articles.

EVIDENCE SYNTHESIS

Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis.

CONCLUSIONS

AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

BACKGROUND

To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes.

MAIN BODY

A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics.

RESULTS

In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues.

CONCLUSION

AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.