Search Results (18)

Background: Accurate diagnosis of acute appendicitis in children remains challenging due to variable presentations and limitations of existing clinical scoring systems. While machine learning (ML) offers a promising approach to enhance diagnostic precision, most prior studies have been limited by small sample sizes, single-center data, or a lack of external validation. Methods: This prospective, multicenter study included 8586 pediatric patients to develop a machine learning-based diagnostic model using routinely available clinical and hematological parameters. A separate, prospectively collected external validation cohort of 3000 patients was used to assess model performance. The Random Forest algorithm was selected based on its superior performance during model comparison. Diagnostic accuracy, sensitivity, specificity, Area Under Curve (AUC), and calibration metrics were evaluated and compared with traditional scoring systems such as Pediatric Appendicitis Score (PAS), Alvarado, and Appendicitis Inflammatory Response Score (AIRS). Results: The ML model outperformed traditional clinical scores in both development and validation cohorts. In the external validation set, the Random Forest model achieved an AUC of 0.996, accuracy of 0.992, sensitivity of 0.998, and specificity of 0.993. Feature-importance analysis identified white blood cell count, red blood cell count, and mean platelet volume as key predictors. Conclusions: This large, prospectively validated study demonstrates that a machine learning-based scoring system using commonly accessible data can significantly improve the diagnosis of pediatric appendicitis. The model offers high accuracy and clinical interpretability and has the potential to reduce diagnostic delays and unnecessary imaging. Full article

Background: Pediatric acute appendicitis (AAP) is a common cause of abdominal pain in children, yet accurate classification into negative, uncomplicated, and complicated forms remains clinically challenging. Misclassification may lead to unnecessary surgeries or delayed treatment. This study aims to evaluate and compare the diagnostic accuracy of five machine learning models (AdaBoost, XGBoost, Stochastic Gradient Boosting, Bagged CART, and Random Forest) for classifying pediatric AAP subtypes. Methods: In this retrospective observational study, a dataset of 590 pediatric patients was analyzed. Demographic information and laboratory parameters—including C-reactive protein (CRP), white blood cell (WBC) count, neutrophils, lymphocytes, and appendiceal diameter—were included as features. The cohort consisted of negative (19.8%), uncomplicated (49.2%), and complicated (31.0%) AAP cases. Five ensemble machine learning models (AdaBoost, XGBoost, Stochastic Gradient Boosting, Bagged CART, and Random Forest) were trained on 80% of the dataset and tested on the remaining 20%. Model performance was evaluated using accuracy, sensitivity, specificity, and F1 score, with cross-validation employed to ensure result stability. Results: Random Forest demonstrated the highest overall accuracy (90.7%), sensitivity (100.0%), and specificity (61.5%) for distinguishing negative and uncomplicated AAP cases. Meanwhile, XGBoost outperformed other models in identifying complicated AAP cases, achieving an accuracy of 97.3%, sensitivity of 100.0%, and specificity of 78.3%. The most influential biomarkers were neutrophil count, appendiceal diameter, and WBC levels, highlighting their predictive value in AAP classification. Conclusions: ML models, particularly Random Forest and XGBoost, exhibit strong potential in aiding pediatric AAP diagnosis. Their ability to accurately classify AAP subtypes suggests that ML-based decision support tools can complement clinical judgment, improving diagnostic precision and patient outcomes. Future research should focus on multi-center validation, integrating imaging data, and enhancing model interpretability for broader clinical adoption. Full article

Background: Acute appendicitis (AA) is a common surgical emergency worldwide. Over the past few decades, diagnostic imaging has become a cornerstone in the identification of acute appendicitis, significantly contributing to the reduction in unnecessary laparotomies and associated healthcare costs. This study aimed to investigate the influence of serum and spot urine 5-hydroxyindoleacetic acid (5-HIAA) levels, as well as other established clinical and biochemical parameters on the diagnosis of acute appendicitis. Methods: This prospective study was conducted between January and November 2023, evaluating 97 patients diagnosed with acute appendicitis. Serum and spot urine 5-HIAA levels, level of white blood cell (WBC), neutrophils, lymphocytes, platelets, C-reactive protein (CRP), and Alvarado score were analyzed. Patients were further allocated to subgroups based on their Alvarado scores, the onset time of the symptoms, and pathological findings to statistically assess the relationship between the parameters. Results: The mean age of the patients was 34.6 ± 14.8 years. Of the patients, 57 (58.8%) were male, and 40 (41.2%) were female. Spot urine 5-HIAA levels exhibited statistically significant variation among different symptom onset time groups, with elevated levels observed in patients presenting within the first 12 h of symptom onset (p < 0.001). Neutrophil counts were significantly different among Alvarado score groups (p < 0.001), whereas CRP levels significantly increased with the onset time of the symptoms (p < 0.001). Conclusions: Increased spot urine 5-HIAA is supportive of the diagnosis of AA in patients presenting within the first 12 h of symptom onset. Hematological parameters, especially CRP, may provide more reliable information regarding disease severity and progression. Full article

Pediatric appendicitis remains a challenging condition to diagnose accurately due to its varied clinical presentations and the non-specific nature of symptoms, particularly in younger patients. Traditional diagnostic approaches often result in delayed treatments or unnecessary surgical interventions, highlighting the need for more robust diagnostic tools. In this study, we explore the potential of machine learning (ML) algorithms to improve the diagnosis, management, and prediction of appendicitis severity in pediatric patients. Using a dataset of pediatric patients with suspected appendicitis, we developed and compared several ML models, including logistic regression (LR), random forests (RFs), gradient boosting machines (GBMs), and Multilayer Perceptrons (MLPs). These models were trained using clinical, laboratory, and imaging data to predict three key outcomes: diagnosis accuracy, management strategy, and the likelihood of negative appendectomies. Our results demonstrate that the RF model achieved the highest overall performance with an Area Under the Receiver Operating Characteristic curve (AUC-ROC) score of 0.94 for diagnosing appendicitis, 0.92 for determining the appropriate management strategy, and 0.70 for predicting appendicitis severity. Furthermore, by employing advanced feature selection techniques, the models were able to reduce the number of unnecessary surgical interventions by up to 17%, highlighting their potential for clinical application. The findings of this study suggest that ML models can significantly enhance diagnostic accuracy and provide valuable insights for managing pediatric appendicitis, potentially reducing unnecessary surgeries and improving patient outcomes. Full article

Background/Objectives: Acute appendicitis (AA) is one of the most common causes of emergency department visits due to acute abdominal pain, with a lifetime risk of 7–8%. Managing AA presents significant challenges, particularly among vulnerable patient groups, due to its association with substantial morbidity and mortality. Methods: The EUPEMEN (European PErioperative MEdical Networking) project aims to optimize perioperative care for AA by developing multidisciplinary guidelines that integrate theoretical knowledge and clinical expertise from five European countries. This study presents the key elements of the EUPEMEN protocol, which focuses on reducing surgical stress, optimizing perioperative care, and enhancing postoperative recovery. Results: Through this standardized approach, the protocol aims to lower postoperative morbidity and mortality, shorten hospital stays, and improve overall patient outcomes. The recommendations are tailored to address the variability in clinical practice across Europe and are designed to be widely implementable in diverse healthcare settings. Conclusions: The conclusions drawn from this study highlight the potential for the EUPEMEN protocol to significantly improve perioperative care standards for AA, demonstrating its value as a practical, adaptable tool for clinicians. Full article

Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. Methods: In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. Results: We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. Conclusions: This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency. Full article

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. Full article

Background: The role of Enterobius vermicularis infestation in the context of appendicitis is largely overlooked, but Enterobius vermicularis is considered an unexpected and significant appendicectomy finding. The aim of this study was to investigate the frequency of Enterobius vermicularis findings in appendectomies and to evaluate the clinical and histopathologic features of patients with Enterobius vermicularis-associated acute appendicitis and those with appendiceal Enterobius vermicularis infestation. Methods: The medical records of all children who underwent an appendectomy in two large pediatric centers in Croatia between 1 January 2009 and 1 January 2024 were retrospectively reviewed. Of 6359 appendectomies, 61 (0.96%) children were diagnosed with Enterobius vermicularis on histopathology and included in further analysis. The groups were compared with regard to demographic characteristics, laboratory values, clinical features and histopathological findings. Results: The incidence of enterobiasis fluctuated slightly in the individual study years, but was constant overall. The median age of all patients was 11 years (IQR 8.5, 13), with females predominating (60.7%). Acute appendicitis was observed in 34% of the appendiceal species. The patients with Enterobius vermicularis infestation, without appendicitis, were younger (9 years (IQR 8, 13) vs. 12 years (IQR 10, 15); p = 0.020), had longer duration of symptoms (36 h (IQR, 12, 48) vs. 24 h (IQR, 12, 36); p = 0.034), lower body temperature (37 °C (IQR 36.8, 37.4) vs. 37.6 °C (IQR, 37, 38.6) p = 0.012), lower Appendicitis Inflammation Response (AIR) score (3 (IQR 2, 5) vs. 7 (IQR 5, 9.5) p < 0.001), lower incidence of rebound tenderness (57.1% vs. 20%; p = 0.003) and less frequent vomiting (12.5% vs. 47.6%; p = 0.004) compared to the patients with Enterobius vermicularis-associated acute appendicitis. Acute inflammatory markers in the laboratory showed significantly higher values in the group of patients with acute appendicitis: C-reactive protein (p = 0.009), White blood cells (p = 0.001) and neutrophils (p < 0.001). Eosinophilia was not found in any of the groups, although eosinophil counts were significantly higher in children who had Enterobius vermicularis infestation than in those with Enterobius vermicularis-related appendicitis (2.5% (IQR 0.9, 4.3) vs. 1.8% (IQR 0.7, 2.1); p = 0.040). Conclusions: Pediatric surgeons should consider Enterobius vermicularis infestation as a differential diagnosis when removing a vermiform appendix. Younger age, longer duration of symptoms, lower body temperature, lower AIR score, lower diameter of the appendix and normal laboratory inflammatory markers could predict Enterobius vermicularis infection in children presenting with right iliac fossa pain and avoid unnecessary appendectomy. Full article

Background: Many scoring systems, algorithms, and guidelines have been developed to aid in the evaluation and diagnosis of acute appendicitis (AA). Many of these algorithms advocate against the routine use of radiological investigations when there is a high clinical suspicion of AA. However, there has been a significant rise in the use of imaging techniques for diagnosing AA in the past two decades. This is a national study aimed at assessing the adherence of residents assigned to the emergency department to the clinical guidelines for diagnosing AA. Methods: We introduced a case study of a male patient with highly suspicious clinical findings of AA to all surgical and emergency medicine residents assigned to the emergency department with the autonomy to make critical decisions to determine the preferred way of diagnosing AA. Results: A total of 62.4% of all relevant residents participated in this survey; 69.6% reported that the Alvarado score was eight or higher, and 82.1% estimated that the next step recommended by most clinical guidelines was appendectomy without further abdominal imaging tests. However, 83.4% chose to perform an imaging test to establish the diagnosis of AA. Conclusions: Our study revealed a notable non-adherence to clinical guidelines in diagnosing AA. Given the significance of these guidelines, we assert that adopting medical recommendations should not solely depend on individual education but should also be incorporated as a departmental policy. Full article

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance. Full article

Background: Acute appendicitis is a frequently encountered surgical emergency. Despite several scoring systems, the possibility of delayed diagnosis persists. In addition, a delayed diagnosis leads to an increased risk of complicated appendicitis. Hence, there is a need to identify biological markers to help clinicians rapidly and accurately diagnose and prognosticate acute appendicitis with a high sensitivity and specificity. Although several markers have been evaluated, the pressing concern is still the low specificity of these markers. One such marker is serum ischemia-modified albumin (IMA), which can be a novel biomarker for accurately diagnosing and prognosticating acute appendicitis. Methods: The authors conducted a systematic search of the PubMed, EMBASE, Web of Science, and Scopus databases through February 2023 as per the PRISMA guidelines. The difference in the levels of IMA between patients with acute appendicitis vs. healthy controls, and the difference in the levels of IMA between patients with complicated vs. non-complicated acute appendicitis were taken as the outcome measures. Statistical analysis was performed using a random effects model and mean difference (MD) was calculated. The methodological quality of the studies was assessed by utilizing the Newcastle–Ottawa scale. Results: A total of six prospective comparative studies were included in the meta-analysis. The analysis revealed that the mean level of serum IMA was significantly raised in the acute appendicitis group (MD 0.21, 95% CI 0.05 to 0.37, p = 0.01). Similarly, the mean serum IMA levels were also raised in the complicated appendicitis group compared to the non-complicated appendicitis group (MD 0.05, 95% CI 0.01 to 0.10, p = 0.02). Three of the studies included were, however, of poor methodological quality. Conclusions: Serum IMA is a viable potential marker for diagnosing and prognosticating acute appendicitis. However, due to the limited methodological quality of available studies, further prospectively designed and adequately powered studies are needed. Full article

Background: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). Method: A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. Results: The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6–90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6–94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. Conclusion: For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp. Full article

Background and Objectives: To assess the current practice pattern in the management of pediatric acute appendicitis in Romania. Materials and Methods: A questionnaire was emailed to all the members of the Romanian Society of Pediatric Surgery between June–July 2022. Results: 118 answers were received, 79.7% responses being from permanent staff members. In the diagnosis of appendicitis, complete blood count, C-reactive protein and abdominal ultrasound are the most commonly used diagnostic tools, while appendicitis scores are not widely used (25% of surgeons). In the case of simple appendicitis, 49.2% of surgeons prefer the conservative approach—oral/intravenous antibiotics. Those who choose the operative approach begin preoperative antibiotics in 56.7% of patients. In case of a stable patient, only 16.7% of surgeons will operate during the night. Laparoscopic approach is chosen by 51.7% of surgeons. In the case of a complicated appendicitis, 92.4% of surgeons will perform the appendectomy, prescribing preoperative antibiotics in 94% of the cases and continuing the therapy postoperatively in 98.2%. Laparoscopic approach is used by 28.8% of surgeons in case of complicated appendicitis. In presence of appendicular mass, 80% prefer a conservative approach with a delayed appendectomy within 6 months. Appendicular abscesses are managed operatively in 82.2% of the cases. The appendix is sent for histological analysis by 95.8% of surgeons. If the peritoneal cavity is contaminated, 95% of the respondents will take a sample for microbiological analysis, 71% will always place a drainage and 44% will always irrigate (71.9%-saline). Conclusions: Clearly, there seems to be a lack of consensus regarding several aspects of the management of acute appendicitis in children. In addition, minimally invasive surgery is not as widely used as reported, despite literature support. Full article

Acute appendicitis is one of the most common causes of abdominal pain in the emergency department and the most common surgical emergency reason for children younger than 15 years of age, which could be enormously dangerous when ruptured. The choice of radiological approach is very important for the diagnosis. In this way, unnecessary surgery is avoided. The aim of this study was to examine the validity of the American College of Radiology appropriateness criteria for radiological imaging in diagnosing acute appendicitis with multivariate decision criteria. In our study, pediatric patients who presented to the emergency department with abdominal pain were grouped according to the Appendicitis Inflammatory Response (AIR) score and the choice of radiological examinations was evaluated with fuzzy-based Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) and with the fuzzy-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model for the validation of the results. As a result of this study, non-contrast computed tomography (CT) was recommended as the first choice for patients with low AIR score $({\mathrm{where} \mathsf{\Phi }}^{\mathrm{net}}=0.0733$) and with high AIR scores (${\mathrm{where} \mathsf{\Phi }}^{\mathrm{net}}=0.0702$) while ultrasound (US) examination was ranked third in patients with high scores. While computed tomography is at the forefront with many criteria used in the study, it is still a remarkable practice that US examination is in the first place in daily routine. Even though there are studies showing the strengths of these tools, this study is unique in that it provides analytical ranking results for this complex decision-making issue and shows the strengths and weaknesses of each alternative for different scenarios, even considering vague information for the acute appendicitis diagnosis in children for different scenarios. Full article

Background: Acute appendicitis in the pediatric population remains a diagnostic challenge for clinicians. Despite many biochemical markers, imaging modalities and scoring systems, initial misdiagnosis and complication rates are high in children. This suggests the need for investigations directed towards new diagnostic tools to aid in the diagnosis. Recent studies have shown a correlation between serum sodium levels and complicated appendicitis. Although the exact reasons for hyponatremia in patients with complicated appendicitis are not known, there is persuasive data to support the role of pro-inflammatory cytokines such as IL-6 in the non-osmotic release of antidiuretic hormone. This meta-analysis aims to investigate all available data on hyponatremia as a diagnostic marker of complicated appendicitis in the pediatric population. Methods: The literature search was conducted by two independent investigators according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The scientific databases (PubMed, EMBASE, Web of Science, and Scopus) were systematically searched for relevant studies using the keywords (hyponatremia) AND (appendicitis) AND (children). The methodological quality was assessed using a validated scale, and RevMan 5.4 software was utilized for pooled analysis. Results: Seven studies were included in the final meta-analysis, five of which were retrospective. A total of 1615 and 2808 cases were distributed into two groups: group A with complicated appendicitis and group B with uncomplicated acute appendicitis, respectively. The studies compared serum sodium levels of patients among the groups. Pooling the data demonstrated significantly lower serum sodium levels in children with complicated appendicitis vs. the non-complicated appendicitis (WMD: −3.29, 95% CI = −4.52 to −2.07, p < 0.00001). The estimated heterogeneity among the included studies was substantial and statistically significant (I² = 98%, p < 0.00001). Conclusion: The results of the present meta-analysis indicate that hyponatremia has potential to be utilized as a biochemical marker in the diagnosis of complicated appendicitis in the pediatric population. However, well designed prospective diagnostic efficiency studies are essential to consolidate the association between hyponatremia and complicated acute appendicitis. Full article