A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children

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.

Choice of Machine Learning Models Is Important to Predict Post-Operative Infections in Surgical Patients

Background: Surgical quality datasets are critical to decision-making tools including surgical infection (SI). Machine learning models (MLMs), a branch of artificial intelligence (AI), are increasingly being ingrained within surgical decision-making algorithms. However, given the unique and distinct functioning of individual models, not all models may be suitable for acutely ill surgical patients. Patients and Methods: This is a 5-year retrospective review of National Surgical Quality Improvement Program (NSQIP) patients who underwent an operation. The data were reviewed for demographics, medical comorbidities, rates, and sites of infection. To generate the MLMs, data were imported into Python, and four common MLMs, extreme gradient boosting, K-nearest neighbor (KNN), random forest, and logistic regression, as well as two novel models (flexible discriminant analysis and generalized additive model) and ensemble modeling, were generated to predict post-operative SIs. Outputs included area under the receiver-operating characteristic curve (AUC ROC) including recall curves. Results: Overall, 624,625 urgent and emergent NSQIP patients were included. The overall infection rate was 8.6%. Patients who sustained a post-operative infection were older, more likely geriatric, male, diabetic, had chronic obstructive pulmonary disease, were smokers, and were less likely White race. With respect to MLMs, all four MLMs had reasonable accuracy. However, a hierarchy of MLMs was noted with predictive abilities (XGB AUC = 0.85 and logistic regression = 0.82), wherein KNN has the lowest performance (AUC = 0.62). With respect to the ability to detect an infection, precision recall of XGB performed well (AUC = 0.73), whereas KNN performed poorly (AUC = 0.16). Conclusions: MLMs are not created nor function similarly. We identified differences with MLMs to predict post-operative infections in surgical patients. Before MLMs are incorporated into surgical decision making, it is critical that surgeons are at the fore of understanding the role and functioning of MLMs.

Development and validation of machine learning-based prediction model for central venous access device-related thrombosis in children

BACKGROUND

Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of central venous access device (CVAD)-related thrombosis (CRT) plays a critical role in delivering timely preventive interventions and reducing the incidence of CRT. Approaches for identifying the risk of CRT in children have not been well-researched.

OBJECTIVE

To identify the critical risk factors for CRT in children and to construct machine learning-based prediction models tailored to this group, providing a theoretical basis and technical support for the prediction and prevention of CRT in these patients.

STUDY DESIGN

Retrospective data of pediatric patients receiving CVAD catheterization from January 1, 2018 to June 31, 2023 in Tongji Hospital were collected and divided into a training set and an internal validation set in a ratio of 7:3. Relevant data from July 1, 2023 to July 1, 2024 were prospectively collected for external validation of the model. LASSO regression was applied to determine CRT independent risk factors. Subsequently, four prediction models were constructed using logistic regression (LR), random forest, artificial neural network, and eXtreme Gradient Boosting.

RESULTS

A total of 1445 children were included in this study and the overall incidence of CRT was 17.4 %. The LASSO regression screened out 11 critical variables, including history of thrombosis, leukemia, number of catheters, history of catheterization, chemotherapy, parenteral nutrition, mechanical prophylaxis, dialysis, hypertonic liquid, anticoagulants, and post-catheterization D-dimer. The LR model outperformed the other models in both internal and external validation and was considered the best model for this study, which was transformed into a nomogram.

CONCLUSIONS

This study identified 11 independent risk factors for CRT in children. The prediction model developed using LR algorithm demonstrated excellent clinical applicability and may provide valuable support for early prediction of CRT.

Machine-learning based prediction of appendicitis for patients presenting with acute abdominal pain at the emergency department

BACKGROUND

Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed. However, diagnostic errors and delays remain common. Although various machine learning (ML) models have been proposed to enhance appendicitis detection, none have been seamlessly integrated into the ED workflows for AAP or are specifically designed to diagnose appendicitis as early as possible within the clinical decision-making process. To mimic daily clinical practice, this proof-of-concept study aims to develop ML models that support decision-making using comprehensive clinical data up to key decision points in the ED workflow to detect appendicitis in patients presenting with AAP.

METHODS

Data from the Dutch triage system at the ED, vital signs, complete medical history and physical examination findings and routine laboratory test results were retrospectively extracted from 350 AAP patients presenting to the ED of a Dutch teaching hospital from 2016 to 2023. Two eXtreme Gradient Boosting ML models were developed to differentiate cases with appendicitis from other AAP causes: one model used all data up to and including physical examination, and the other was extended with routine laboratory test results. The performance of both models was evaluated on a validation set (n = 68) and compared to the Alvarado scoring system as well as three ED physicians in a reader study.

RESULTS

The ML models achieved AUROCs of 0.919 without laboratory test results and 0.923 with the addition of laboratory test results. The Alvarado scoring system attained an AUROC of 0.824. ED physicians achieved AUROCs of 0.894, 0.826, and 0.791 without laboratory test results, increasing to AUROCs of 0.923, 0.892, and 0.859 with laboratory test results.

CONCLUSIONS

Both ML models demonstrated comparable high accuracy in predicting appendicitis in patients with AAP, outperforming the Alvarado scoring system. The ML models matched or surpassed ED physician performance in detecting appendicitis, with the largest potential performance gain observed in absence of laboratory test results. Integration could assist ED physicians in early and accurate diagnosis of appendicitis.

Update on the Diagnosis and Treatment of Acute Appendicitis

Acute appendicitis (AA) is one of the most common surgical emergencies in adults and children [...].

Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach

The diagnosis of abdominal pain in emergency departments is challenging, and appendicitis is a common concern. Atypical symptoms often delay diagnosis. Although the Alvarado score aids in decision-making, its low specificity can lead to unnecessary surgeries. By leveraging machine learning, we aim to enhance diagnostic accuracy by predicting appendicitis and distinguishing it from other causes of abdominal pain in the emergency department. Data were collected from 534 patients who presented with acute abdominal pain. Patient characteristics, laboratory results, and causes of pain were recorded. Machine learning algorithms (support vector classifier, random forest classifier, gradient boosting classifier, and Gaussian naive Bayes) were used to predict the cause of pain. Model calibration was assessed using the Brier score. The mean age was 46.89 (20.3) years, with an almost equal sex distribution (49% male, 51% female). Cholecystitis was the most prevalent outcome (37.07%), followed by appendicitis (25.84%). The Gaussian naive Bayes model exhibited superior performance in terms of accuracy (95.03% 95% CI 90.44-97.83%), sensitivity (87.18% 95% CI 72.57-95.70%), and specificity (97.54% 95% CI 92.98-99.49%), while the random forest model showed a sensitivity of 79.49%, specificity of 96.72%, and accuracy of 92.55%. The gradient boosting algorithm achieved a sensitivity, specificity, and accuracy of 89.74%, 95.90%, and 94.41%, respectively. The support vector classifier demonstrated a sensitivity of 89.74%, specificity of 92.62%, and accuracy of 91.93%. The use of modern machine learning methods aids in the accurate diagnosis of appendicitis.

An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients

Appendicitis, an infection and inflammation of the appendix is a prevalent condition in children that requires immediate treatment. Rupture of the appendix may lead to several complications, such as peritonitis and sepsis. Appendicitis is medically diagnosed using urine, blood, and imaging tests. In recent times, Artificial Intelligence and machine learning have been a boon for medicine. Hence, several supervised learning techniques have been utilized in this research to diagnose appendicitis in pediatric patients. Six heterogeneous searching techniques have been used to perform hyperparameter tuning and optimize predictions. These are Bayesian Optimization, Hybrid Bat Algorithm, Hybrid Self-adaptive Bat Algorithm, Firefly Algorithm, Grid Search, and Randomized Search. Further, nine classification metrics were utilized in this study. The Hybrid Bat Algorithm technique performed the best among the above algorithms, with an accuracy of 94% for the customized APPSTACK model. Five explainable artificial intelligence techniques have been tested to interpret the results made by the classifiers. According to the explainers, length of stay, means vermiform appendix detected on ultrasonography, white blood cells, and appendix diameter were the most crucial markers in detecting appendicitis. The proposed system can be used in hospitals for an early/quick diagnosis and to validate the results obtained by other diagnostic modalities.

Artificial Intelligence in the Diagnosis and Management of Appendicitis in Pediatric Departments: A Systematic Review

INTRODUCTION

 Artificial intelligence (AI) is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review.

METHODS

 PubMed, Embase, and Web of Science were searched using the following keywords: "pediatric," "artificial intelligence," "standard practices," and "appendicitis," up to September 2023. The risk of bias was assessed using PROBAST.

RESULTS

 A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or area under the curve >0.9. All studies were rated as a "high risk" concerning their overall risk of bias.

CONCLUSION

 We analyzed the current status of AI in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.

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.

The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review

BACKGROUND

Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery.

METHODS

Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2.

RESULTS

Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%.

CONCLUSIONS

While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows.

LEVEL OF EVIDENCE

2A.

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review

Background: Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations. Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. Methods: We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query ("Appendectomy" OR "Appendicitis") AND ("Machine Learning" OR "Artificial Intelligence") was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. Results: The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. Conclusions: The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications. However, further studies are warranted to assess the feasibility and advisability of implementing machine learning-based tools in clinical practice.

Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis

Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.

Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis

The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent clinical decision support system for the timely and accurate diagnosis of acute appendicitis. The number of participants which is equal to 181 was chosen as the sample size for developing and evaluating neural networks. The information was gathered from the medical files of patients who underwent appendicectomies at Shahid Modarres Hospital as well as from the findings of their appendix samples' pathological tests. The diagnostic outcomes were then ascertained by the development and comparison of a Multilayer Perceptron network (MLP) and a Support Vector Machine (SVM) system in the MATLAB environment. The SVM algorithm functioned as the central processing unit in the Clinical Decision Support System (CDSS) that was built. The intelligent appendicitis diagnostic system was subsequently developed utilizing the Java programming language. Technical evaluation and system usability testing were both done as part of the software evaluation process. Comparing the output of the optimized artificial neural network of the SVM with the pathology result showed that the network's sensitivity, specificity, and accuracy were 91.7%, 96.2%, and 95%, respectively, in diagnosing acute appendicitis. Based on the existing standards and the opinions of general surgeons, and also comparing the results with the diagnostic accuracy of general surgeons, findings indicated the proper functioning of the network for the diagnosis of acute appendicitis. The use of this system in medical centers is useful for purposes such as timely diagnosis and prevention of negative appendectomy, reducing patient hospital stays and treatment costs, and improving the patient referral system.

A different way to diagnosis acute appendicitis: machine learning

<b><br>Indroduction:</b> Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.</br> <b><br>Aim:</b> Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method.</br> <b><br>Materials and methods:</b> Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed. Patients with right lower quadrant pain were selected. A total of 189 positive and 156 negative appendectomies were found. Gender and hemogram were used as features. Machine learning algorithms and data analysis were made in Python (3.7) programming language.</br> <b><br>Results:</b> Negative appendectomies were found in 62% (n = 97) of the women and in 38% (n = 59) of the men. Positive appendectomies were present in 38% (n = 72) of the women and 62% (n = 117) of the men. The accuracy in the test data was 82.7% in logistic regression, 68.9% in support vector machines, 78.1% in k-nearest neighbors, and 83.9% in neural networks. The accuracy in the voting classifier created with logistic regression, k-nearest neighbor, support vector machines, and artificial neural networks was 86.2%. In the voting classifier, the sensitivity was 83.7% and the specificity was 88.6%.</br> <b><br>Conclusions:</b> The results of our study show that machine learning is an effective method for diagnosing acute appendicitis. This study presents a practical, easy, fast, and inexpensive method to predict the diagnosis of acute appendicitis.</br>.

Artificial intelligence for predicting acute appendicitis: a systematic review

BACKGROUND

Paediatric appendicitis may be challenging to diagnose, and outcomes difficult to predict. While diagnostic and prognostic scores exist, artificial intelligence (AI) may be able to assist with these tasks.

METHOD

A systematic review was conducted aiming to evaluate the currently available evidence regarding the use of AI in the diagnosis and prognostication of paediatric appendicitis. In accordance with the PRISMA guidelines, the databases PubMed, EMBASE, and Cochrane Library were searched. This review was prospectively registered on PROSPERO.

RESULTS

Ten studies met inclusion criteria. All studies described the derivation and validation of AI models, and none described evaluation of the implementation of these models. Commonly used input parameters included varying combinations of demographic, clinical, laboratory, and imaging characteristics. While multiple studies used histopathological examination as the ground truth for a diagnosis of appendicitis, less robust techniques, such as the use of ICD10 codes, were also employed. Commonly used algorithms have included random forest models and artificial neural networks. High levels of model performance have been described for diagnosis of appendicitis and, to a lesser extent, subtypes of appendicitis (such as complicated versus uncomplicated appendicitis). Most studies did not provide all measures of model performance required to assess clinical usability.

CONCLUSIONS

The available evidence suggests the creation of prediction models for diagnosis and classification of appendicitis using AI techniques, is being increasingly explored. However, further implementation studies are required to demonstrate benefit in system or patient-centred outcomes with model deployment and to progress these models to the stage of clinical usability.

Use of machine learning in pediatric surgical clinical prediction tools: A systematic review

PURPOSE

Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artificial intelligence have resulted in a proliferation of CPTs created using machine learning (ML)-yet the clinical applicability of ML-based CPTs and their validation in clinical settings remain unclear. This systematic review aims to compare the validity and clinical efficacy of ML-based to traditional CPTs in pediatric surgery.

METHODS

Nine databases were searched from 2000 until July 9, 2021 to retrieve articles reporting on CPTs and ML for pediatric surgical conditions. PRISMA standards were followed, and screening was performed by two independent reviewers in Rayyan, with a third reviewer resolving conflicts. Risk of bias was assessed using the PROBAST.

RESULTS

Out of 8300 studies, 48 met the inclusion criteria. The most represented surgical specialties were pediatric general (14), neurosurgery (13) and cardiac surgery (12). Prognostic (26) CPTs were the most represented type of surgical pediatric CPTs followed by diagnostic (10), interventional (9), and risk stratifying (2). One study included a CPT for diagnostic, interventional and prognostic purposes. 81% of studies compared their CPT to ML-based CPTs, statistical CPTs, or the unaided clinician, but lacked external validation and/or evidence of clinical implementation.

CONCLUSIONS

While most studies claim significant potential improvements by incorporating ML-based CPTs in pediatric surgical decision-making, both external validation and clinical application remains limited. Further studies must focus on validating existing instruments or developing validated tools, and incorporating them in the clinical workflow.

TYPE OF STUDY

Systematic Review LEVEL OF EVIDENCE: Level III.

The role of IL-6, thiol-disulfide homeostasis, and inflammatory biomarkers in the prediction of acute appendicitis in children: a controlled study

PURPOSE

This study aimed to examine the diagnostic value of IL-6, thiol-disulfide homeostasis, complete blood count and inflammatory biomarkers in the prediction of acute appendicitis in children.

METHODS

The study was designed as a prospective and controlled study in children-the study was conducted at a tertiary referential university hospital between May 2020 and April 2021. Patients were divided between study groups and one control group (CG): 1: confirmed acute appendicitis group (AAP); 2: perforated appendicitis group (PAP); and 3: non-specified abdominal pain (NAP). The age and gender of the patients were determined. The following listed laboratory parameters were compared between groups: TOS: total oxidative status, TAS: total antioxidant status, OSI: oxidative stress index, TT: total thiol, NT (µmol/L): native thiol, DIS: disulfide, IL-6: interleukin 6, TNF-a: tumor necrosis factor-alpha, WBC: white blood cell, NEU: neutrophil, NEU%: neutrophil percentage, LY: lymphocyte, LY%: lymphocyte percentage, PLT: platelet, MPV: mean platelet volume NLR: neutrophil lymphocyte ratio, CRP: C-reactive protein, LCR: lymphocyte CRP ratio, and serum lactate.

RESULTS

The TOS level of the PAP group was found to be significantly higher than that in the AAP, NAP and control groups (p = 0.006, < 0.001 and p < 0.001). TAS, TT, and NT levels in the PAP group were significantly lower than those in the AAP, NAP and control groups. OSI was significantly higher in the PAP group than in the other groups. The TT and NT levels of the NAP group were both similar to those of the control group. Serum DIS level was similar between the AAP and PAP groups, AAP and NAP groups, and NAP and control groups. Serum IL-6 and TNF-α levels were found to be significantly higher in the PAP group compared to those in all groups. The WBC, NEU, and NEU% values were found to be significantly higher in the PAP group than those in the NAP and control groups, while LY and LY% values were found to be significantly lower. PAP and AAP groups were found to be similar in terms of WBC, NEU, LYM, NEU%, and LYM% values. PLT and MPV values and serum lactate values did not show a significant difference between the groups. NLR was similar in the AAP and PAP groups. A significant increase in CRP versus a decrease in LCR was detected in the PAP group compared to that in the AAP group. Multivariate analysis demonstrated that only IL-6 has significant estimated accuracy rates as 80% for the control group, 78.8% for AAP, 96.9% for PAP, and 81.6% for NAP.

CONCLUSION

Rather than AAP, PAP caused significantly higher oxidative stress (increased TOS and OSI), and lower antioxidation capacity (decreased TT and NT). IL-6 levels can provide a significant stratification. Nevertheless, simply detecting WBC or CRP is not enough to distinguish the specific pathology in acute appendicitis and related conditions.

Applicability of American College of Radiology Appropriateness Criteria Decision-Making Model for Acute Appendicitis Diagnosis in Children

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 (where Φnet=0.0733) and with high AIR scores (where Φnet=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.

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.

Management of abdominal emergencies in adults using telemedicine and artificial intelligence

The terms "telemedicine" and "artificial intelligence" (AI) are used today throughout all fields of medicine, with varying degrees of relevance. If telemedicine corresponds to practices currently being developed to supply a high quality response to medical provider shortages in the general provision of healthcare and to specific regional challenges. Through the possibilities of "scalability" and the "augmented physician" that it has helped to create, AI may also constitute a revolution in our practices. In the management of surgical emergencies, abdominal pain is one of the most frequent complaints of patients who present for emergency consultation, and up to 20% of patients prove to have an organic lesion that will require surgical management. In view of the very large number of patients concerned, the variety of clinical presentations, the potential seriousness of the etiological pathology that sometimes involves a life-threatening prognosis, healthcare workers responsible for these patients have logically been led to regularly rely on imaging examinations, which remain the critical key to subsequent management. Therefore, it is not surprising that articles have been published in recent years concerning the potential contributions of telemedicine (and teleradiology) to the diagnostic management of these patients, and also concerning the contribution of AI (albeit still in its infancy) to aid in diagnosis and treatment, including surgery. This review article presents the existing data and proposes a collaborative vision of an optimized patient pathway, giving medical meaning to the use of these tools.

Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time.

Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis

Background: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children. Materials and Methods: Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables. Results: A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis. Discussion: This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool. Conclusion: ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.