Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach

Applications of Artificial Intelligence in Acute Abdominal Imaging

Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.

A Novel Deep Learning Approach for the Automatic Diagnosis of Acute Appendicitis

Background: Acute appendicitis (AA) is a major cause of acute abdominal pain requiring surgical intervention. Approximately 20% of AA cases are diagnosed neither early nor accurately, leading to an increased risk of appendiceal perforation and postoperative sequelae. AA can be identified with good accuracy using computed tomography (CT). However, some studies have found that a false-negative AA diagnosis made using CT can cause surgical therapy to be delayed. Deep learning experiments are aimed at minimizing false-negative diagnoses. However, the success rates reported in these studies are far from 100%. In addition, the methods used to divide patients into groups do not adequately reflect situations in which accurate radiological diagnosis is difficult. Therefore, in this study, we propose a novel deep-learning approach for the automatic diagnosis of AA using CT based on establishing a new strategy for classification according to the difficulties encountered in radiological diagnosis. Methods: A total of 266 patients with a pathological diagnosis of AA who underwent appendectomy were divided into two groups based on CT images and radiology reports. A deep learning analysis was performed on the CT images and clinical and laboratory parameters that contributed to the diagnosis of both the patient and age- and sex-adjusted control groups. Results: The deep learning diagnosis success rate was 96% for the group with advanced radiological findings and 83.3% for the group with radiologically suspicious findings that could be considered normal. Conclusions: Using deep learning, successful results can be achieved in cases in which the appendix diameter has not increased significantly and there is no significant edema effect.

The AI Future of Emergency Medicine

In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.

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.

Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis

PURPOSE

Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors.

METHODS

To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method.

RESULTS

Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster.

CONCLUSION

AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection.

LEVELS OF EVIDENCE

Level III.

[Predictive appendicitis scale for children under 4 years of age: Is it possible to apply artificial intelligence?]

Acute appendicitis in the pediatric population is a pathology of heterogeneous presentation that is currently diagnosed using various criteria or predictive scales, which have proven not to be sufficiently accurate to be standardized, however, methods have been created to establish a more accurate diagnosis, an aspect that has been provided by artificial intelligence, which through different algorithms has the ability to show the patient's condition and the most appropriate intervention for this, thus reducing the rate of unnecessary interventions and therefore possible related complications.

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.

Artificial intelligence in rectal cancer: What is the future?

Colorectal cancer (CRC) is the third most prevalent cancer in both men and women, and it is the second leading cause of cancer-related deaths globally. Around 60%-70% of CRC patients are diagnosed at advanced stages, with nearly 20% having liver metastases. It is noteworthy that the 5-year survival rates decline significantly from 80%-90% for localized disease to a mere 10%-15% for patients with metastasis at the time of diagnosis. Early diagnosis, appropriate therapeutic strategy, accurate assessment of treatment response, and prognostication is essential for better outcome. There has been significant technological development in the last couple of decades to improve the outcome of rectal cancer including Artificial intelligence (AI). AI is a broad term used to describe the study of machines that mimic human intelligence, such as perceiving the environment, drawing logical conclusions from observations, and performing complex tasks. At present AI has demonstrated a promising role in early diagnosis, prognosis, and treatment outcomes for patients with rectal cancer, a limited role in surgical decision making, and had a bright future.

The Utility of Artificial Intelligence in the Diagnosis and Management of Pancreatic Cancer

Artificial intelligence (AI) has made significant advancements in the medical domain in recent years. AI, an expansive field comprising Machine Learning (ML) and, within it, Deep Learning (DL), seeks to emulate the intricate operations of the human brain. It examines vast amounts of data and plays a crucial role in decision-making, overcoming limitations related to human evaluation. DL utilizes complex algorithms to analyze data. ML and DL are subsets of AI that utilize hard statistical techniques that help machines consistently improve at tasks with experience. Pancreatic cancer is more common in developed countries and is one of the leading causes of cancer-related mortality worldwide. Managing pancreatic cancer remains a challenge despite significant advancements in diagnosis and treatment. AI has secured an almost ubiquitous presence in the field of oncological workup and management, especially in gastroenterology malignancies. AI is particularly useful for various investigations of pancreatic carcinoma because it has specific radiological features that enable diagnostic procedures without the requirement of a histological study. However, interpreting and evaluating resulting images is not always simple since images vary as the disease progresses. Secondly, a number of factors may impact prognosis and response to the treatment process. Currently, AI models have been created for diagnosing, grading, staging, and predicting prognosis and treatment response. This review presents the most up-to-date knowledge on the use of AI in the diagnosis and treatment of pancreatic carcinoma.

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.

Eosinophil Granule Proteins Involvement in Acute Appendicitis-An Allergic Disease?

Several pieces of evidence point to an allergic component as a trigger of acute appendicitis. As the Th2 immune response is characterized by eosinophil mobilization to the target organ and release of their cationic granule proteins, it is reasonable to investigate if the degranulation of eosinophils could be associated with the local injury. The primary aim of this study is to evaluate the participation of eosinophils granules proteins in acute appendicitis, both at local and systemic levels and the secondary aim is to evaluate the diagnostic accuracy of eosinophils granules proteins for the detection of acute appendicitis, as well as for distinguishing between complicated and uncomplicated acute appendicitis. Eosinophil-derived neurotoxin (EDN), eosinophil cationic protein (ECP) and eosinophil peroxidase (EP) are the most well-known eosinophil granule proteins. From August 2021 to April 2022, we present a prospective single-center study to evaluate the EDN, ECP, and EP concentrations simultaneously in appendicular lavage fluid (ALF) and the serum of 22 patients with acute phlegmonous appendicitis (APA), 24 with acute gangrenous appendicitis (AGA), and 14 normal controls. Concerning EDN, no differences were found between groups. ECP concentrations in ALF and serum were significantly higher in the histologically confirmed acute appendicitis compared to the control groups (p < 0.0001 and p < 0.0001, respectively). In ALF, no differences were found between ECP levels in APA: 38.85 ng/mL (IQR 26.50-51.77) and AGA 51.55 ng/mL (IQR 39.55-70.09) groups (p = 0.176). In the serum, no difference was found between ECP levels at APA: 39 ng/mL (IQR 21.30-56.90) and AGA: 51.30 ng/mL (IQR 20.25-62.59) (p = 0.100). For EP, the concentrations in ALF (p < 0.001) and serum (p < 0.001) were both higher in acute appendicitis compared to the control. In ALF, no difference was found between APA: 240.28 ng/mL (IQR 191.2-341.3) and AGA: 302.5 (IQR 227.7-535.85) (p = 0.236). In the serum, no differences were found between APA: 158.4 ng/mL (IQR 111.09-222.1) and AGA: 235.27 (IQR 192.33-262.51) (p = 0.179). Globally, the ALF concentrations were higher than serum concentrations, reflecting an intense inflammatory local reaction in AA. The optimal ECP cut-off for discriminating between acute appendicitis and the controls was >11.41 ng/mL, with a sensitivity of 93.5%, but with a specificity for identifying appendicitis of 21.4%, good discriminative power (AUC = 0.880). For EP, the optimal cut-off was >93.20 ng/mL, with a sensitivity of 87%, but with a specificity of 14.3% (AUC = 0.901), excellent discriminative power. For the diagnosis of perforated AA, the discriminative power of ECP and EP serum concentrations are weak (AUC = 0.562 and AUC = 0.664, respectively). Concerning the presence of peritonitis, the discriminative power of ECP and EP serum concentrations is acceptable, respectively: AUC = 0.724 and AUC = 0.735. Serum levels of EDN (p = 0.119), ECP (p = 0.586) and EP (p = 0.08) in complicated appendicitis were similar to uncomplicated appendicitis. Serum concentrations of ECP and EP can be added to decision-making AA diagnosis. A Th2-type immune response is present in AA. These data bring forward the role of an allergic reaction in the pathogenesis of acute appendicitis.

Application of Artificial Neural Network Models to Differentiate Between Complicated and Uncomplicated Acute Appendicitis

Preoperative prediction of complicated appendicitis is challenging, and many clinical tools are developed to predict complicated appendicitis. This study evaluated whether a supervised learning method can recognize complicated appendicitis in emergency department (ED). Consecutive patients with acute appendicitis presenting to the ED were enrolled and included into training and testing datasets at a ratio of 70:30. The multilayer perceptron artificial neural network (ANN) models were trained to perform binary outcome classification between uncomplicated and complicated acute appendicitis. Measures of sensitivity, specificity, positive and negative likelihood ratio (LR + and LR-), and a c statistic of a receiver of operating characteristic curve were used to evaluate an ANN model. The simplest ANN model by Bröker et al. including the C-reactive protein (CRP) and symptom duration as variables achieved a c statistic value of 0.894. The ANN models developed by Avanesov et al. including symptom duration, appendiceal diameter, periappendiceal fluid, extraluminal air, and abscess as variables attained a high diagnostic performance (a c statistic value of 0.949) and good efficiency (sensitivity of 78.6%, specificity of 94.5%, LR + of 14.29, LR- of 0.23 in the testing dataset); and our own model by H.A. Lin et al. including the CRP level, neutrophil-to-lymphocyte ratio, fat-stranding sign, appendicolith, and ascites exhibited high accuracy (c statistic of 0.950) and outstanding efficiency (sensitivity of 85.7%, specificity of 91.7%, LR + of 10.36, LR- of 0.16 in the testing dataset). The ANN models developed by Avanesov et al. and H.A. Lin et al. developed model exhibited a high diagnostic performance.

Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence

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.

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

Background

Complicated appendicitis, a potentially life-threatening condition, is common. However, the diagnosis of this condition is mainly based on physician's experiences and advanced diagnostic equipment. This study built and validated machine learning models to facilitate the detection of complicated appendicitis.

Methods

A retrospective cohort study was conducted based on medical charts of all patients undergoing a laparoscopic appendectomy at a city hospital during 2016-2020. The synthetic minority over-sampling technique (SMOTE) was used to adjust for the imbalance. Multiple classification approaches were used to train and validate models including support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), artificial neural network (ANN), and gradient boosting (GB).

Results

Among 1,950 patients included in the data analysis, there were 483 patients identified as having complicated appendicitis (24.8%). Based on data without SMOTE adjustment for imbalance, the accuracy levels and AUCs were high in all models using different parameters, ranging from 0.687 to 0.815. After adjusting for imbalance data using SMOTE, AUC and accuracy levels in the models using imbalance adjusted data were higher. Of these, the GB had all AUC and accuracy values of approximately 0.8 or more in both adjusted and unadjusted data.

Conclusions

Machine learning approaches including SVM, DT, logistic, KNN, ANN, and GB have a high level of validity in classifying patients with complicated appendicitis and patients without complicated appendicitis. Among these, GB had the highest level of validity and should be used or further validated. Our study indicates the beneficial potentials of machine learning techniques in a clinical setting in general and in the diagnosis of complicated appendicitis in particular.

IL-5 Serum and Appendicular Lavage Fluid Concentrations Correlate with Eosinophilic Infiltration in the Appendicular Wall Supporting a Role for a Hypersensitivity Type I Reaction in Acute Appendicitis

Appendicitis is the most common abdominal surgical emergency, but its aetiology is not fully understood. We and others have proposed that allergic responses play significant roles in its pathophysiology. Eosinophils and Interleukin (IL)-5 are involved in a hypersensitivity type I reaction. Eosinophil infiltration is common in the allergic target organ and is dependent on IL-5. In the presence of an allergic component, it is expected that the eosinophil count and IL-5 local and systemic concentrations become elevated. To address this hypothesis, we designed a prospective study that included 65 patients with acute appendicitis (grouped as acute phlegmonous or gangrenous according to the histological definition) and 18 patients with the clinical diagnosis of acute appendicitis, but with normal histological findings (control group) were enrolled. Eosinophil blood counts and appendicular wall eosinophil infiltration were determined. IL-5 levels in blood and appendicular lavage fluid were evaluated. Appendicular lavage fluid was collected by a new methodology developed and standardized by our group. Appendicular wall eosinophil infiltration was higher in acute phlegmonous appendicitis than in gangrenous appendicitis (p = 0.000). IL-5 blood levels were similar in both pathologic and control groups (p > 0.05). In the appendicular lavage fluid, the higher levels of IL-5 were observed in the phlegmonous appendicitis group (p = 0.056). We found a positive correlation between the appendicular wall eosinophilic infiltration and the IL-5 concentrations, in both the blood and the appendicular lavage fluid, supporting the IL-5 reliance in eosinophil local infiltration. We observed the highest presence of eosinophils at phlegmonous appendicitis walls. In conclusion, the present data are compatible with a hypersensitivity type I allergic reaction in the target organ, the appendix, during the phlegmonous phase of appendicitis.

Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma

Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.

Pediatric Buried Bumper Syndrome: Diagnostic Validity of Transabdominal Ultrasound and Artificial Intelligence

PURPOSE

 Buried bumper syndrome (BBS) is a severe complication of percutaneous endoscopic gastrostomy (PEG) resulting from overgrowth of gastric mucosa and penetration of the inner holding plate into the gastric wall. The aim of this study was to evaluate the diagnostic value of transabdominal ultrasound (US) in comparison to an artificial intelligence (AI) model for the diagnosis of BBS in children.

MATERIALS AND METHODS

 In this monocentric retrospective study, pediatric US data concerning BBS from a ten-year period (2009-2019) were analyzed. US findings were compared to a clinical multiparameter-based AI model and reference standard endoscopy. Clinical risk factors for the occurrence of pediatric BBS were determined.

RESULTS

 In n = 121 independent examinations of n = 82 patients, the placement of the inner holding plate of the PEG was assessed by US. In n = 18 cases BBS was confirmed. Recall and precision rates were 100 % for US and 88 % for the AI-based assessment. Risk factors for the occurrence of BBS were mobilization problems of the PEG (r_s = 0.66, p < 0.001), secretion/exudation (r_s = 0.29, p = 0.002), time between 1^st PEG placement and US (r_s = 0.38, p < 0.001), and elevated leukocyte count (r_s = 0.24, p = 0.016).

CONCLUSION

 Transabdominal US enables correct, rapid, and noninvasive diagnosis of BBS in pediatric patients. Preceding AI models could aid during diagnostic workup. To avoid unnecessary invasive procedures, US could be considered as a primary diagnostic procedure in suspected BBS. .

Development and Validation of a Differential Diagnosis Model for Acute Appendicitis and Henoch-Schonlein Purpura in Children

Objective: To study and develop a predictive model for the differential diagnosis of acute appendicitis (AA) and Henoch-Schonlein purpura (HSP) in children and to validate the model internally and externally. Methods: The complete data of AA and HSP cases were retrospectively analyzed and divided into internal and external verification groups. SPSS software was used for single-factor analysis and screening of independent variables, and R software was used for the development and verification of the diagnostic model. Lasso regression analysis was used to screen predictors and Lasso-logistic regression model was constructed, and K-fold cross-validation was used for the internal verification. In addition, nonfever patients were selected for model development and validation in the same way. Receiver operating characteristic (ROC) curves and calibration curves were drawn, respectively, to evaluate the 2 models. Results: Internal development and validation of the model showed that fever, neutrophil ratio (NEUT%), albumin (ALB), direct bilirubin (DBIL), C-reactive protein (CRP), and K were predictive factors for the diagnosis of HSP. The model was presented in the form of a nomogram, and the area under ROC curve of the development group and verification group was 0.9462 (95% confidence interval [CI] = 0.9402-0.9522) and 0.8931 (95% CI = 0.8724-0.9139), respectively. In the model of patients without fever, NEUT%, platelets (PLT), ALB, DBIL, alkaline phosphatase (ALP), CRP, and K were predictive factors for the diagnosis of HSP, and the area under ROC curve of the development group and verification group was 0.9186 (95% CI = 0.908-0.9293) and 0.8591 (95% CI = 0.8284-0.8897), respectively. Conclusion: In this study, 2 diagnostic models were constructed for fever or not, both of which had good discrimination and calibration, and were helpful to distinguish AA and HSP in children.

Colorectal cancer: Artificial intelligence and its role in surgical decision making

Despite several advances in the oncological management of colorectal cancer (CRC), there still remains a lacuna in the treatment strategy, which differs from center to center and on the philosophy of the treating clinician that is not without bias. Personalized treatment is essential for the treatment of CRC to achieve better long-term outcomes and to reduce morbidity. Surgery has an important role to play in the treatment. Surgical treatment of CRC is decided based on clinical parameters and investigations and hence likely to have judgmental errors. Artificial intelligence has been reported to be useful in the surveillance, diagnosis, treatment, and follow-up with accuracy in several malignancies. However, it is still evolving and yet to be established in surgical decision making in CRC. It is not only useful preoperatively but also intraoperatively. Artificial intelligence helps to rectify the human surgical decision when clinical data and radiological and laboratory parameters are fed into the computer and may guide correct surgical treatment.

Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review

PURPOSE

An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery.

METHODS

Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed.

RESULTS

Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM.

CONCLUSIONS

A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.

The adaptive (aka “acquired”) immune system

The purpose of the immune system is simple—protect the human organism from foreign (antigenic) invasion and resultant disease. The innate immune response (“our best friend”) does a great job of accomplishing that defense under most circumstances. But sometimes, innate immunity confronts an adversary (a pathogen) that overwhelms it and produces a “dysregulated,” adaptive immune response. The first clinical effect is acute inflammation with an array of familiar signs and symptoms (pain, redness, swelling, sometimes fever). If not reversed within days to weeks, this negative pathological condition progresses from the acute state to its more devastating successor, chronic inflammation (“our worst enemy”), and the progenitor of all human disease. This chapter presents the clinical, histological, and pharmacological stages and basic immunotherapeutic efforts to arrest and reverse the “inflammatory cascade.” Unsuccessful efforts allow the adaptive immune system and chronic inflammation to begin an inexorable, pathological course toward autoimmune disease, cancers, and the ravages of infectious pandemics like COVID19.

A diagnostic testing for people with appendicitis using machine learning techniques

Appendicitis is a common disease that occurs particularly often in childhood and adolescence. The accurate diagnosis of acute appendicitis is the most significant precaution to avoid severe unnecessary surgery. In this paper, the author presents a machine learning (ML) technique to predict appendix illness whether it is acute or subacute, especially between 10 and 30 years and whether it requires an operation or just taking medication for treatment. The dataset has been collected from public hospital-based citizens between 2016 and 2019. The predictive results of the models achieved by different ML techniques (Logistic Regression, Naïve Bayes, Generalized Linear, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Random Forest) are compared. The covered dataset are 625 specimens and the total of the medical records that are applied in this paper include 371 males (60.22%) and 254 females (40.12%). According to the dataset, the records consist of 318 (50.88%) operated and 307 (49.12%) unoperated patients. It is observed that the random forest algorithm obtains the optimal result with an accurately predicted result of 83.75%, precision of 84.11%, sensitivity of 81.08%, and the specificity of 81.01%. Moreover, an estimation method based on ML techniques is improved and enhanced to detect individuals with acute appendicitis.

Increased IgE Deposition in Appendicular Tissue Specimens Is Compatible with a Type I Hypersensitivity Reaction in Acute Appendicitis

Background

IgE mediates type I hypersensitivity reaction and can be found in the mucosa of organs affected by allergy. Acute appendicitis (AA) is a common disease, but its etiology remains poorly understood. Here, we investigated IgE deposition in histological sections of AA samples to test the hypothesis that an allergic reaction may substantially contribute to the pathophysiology of AA.

Materials and Methods

In a retrospective study, we assessed the presence of IgE in appendicular specimens of histologically confirmed appendicitis and in the control group, comprised of negative appendicitis and incidental appendectomies, using a monoclonal antibody against human IgE. Samples from 134 appendectomies were included: 38 phlegmonous and 27 gangrenous appendicitis from the study group and 52 incidental appendectomies and 17 negative appendicitis from the control group. The slides were visualized by light microscopy, and a standard procedure was used to manually count the positive IgE staining cells.

Results

IgE staining was present in the cells of all but 5 appendicular specimens. We found a significantly increased number of IgE-positive cells in phlegmonous AA (median = 28) when compared to incidental appendectomy (median = 17) (p = 0.005; p < 0.0001 when adjusted for age and gender). No difference was found for gangrenous appendicitis. Discussion. The presence of IgE supports the contribution of an allergic reaction for the pathophysiology of phlegmonous appendicitis. The reduced number of IgE staining cells in gangrenous appendicitis can be due to tissue destruction, or, as been claimed by others, gangrenous appendicitis is a distinct entity, with different etiology.

Conclusion

In this study, phlegmonous appendicitis had the highest number of IgE-positive appendicular cells. These findings suggest that an allergic reaction can contribute to the pathophysiology of AA, opening a novel possibility for preventive measures in a disease that typically requires surgery.

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation

Purpose

Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation.

Materials and methods

The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation.

Results

The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided.

Conclusions

Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly.

WSES project on decision support systems based on artificial neural networks in emergency surgery

The article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.

Artificial intelligence in gastrointestinal diseases

Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.

The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study

Background

Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA). Methods of artificial intelligence (AI) have successfully been applied to routine laboratory and sonographic parameters for differentiation of the inflammatory manifestations. In this study we aimed to apply AI methods to gene expression data to provide evidence for feasibility.

Methods

Modern algorithms from AI were applied to 56.666 gene expression data sets from 13 patients with PA and 16 with GA aged 7–17 years by using resampling methods (bootstrap). Performance with respect to sensitivities and specificities where investigated with receiver operating characteristic (ROC) analysis.

Results

Within the experimental setting a best performing discriminatory biomarker signature consisting of a set of 4 genes could be defined: ERGIC and golgi 3, regulator of G-protein signaling 2, Rho GTPase activating protein 33, and Golgi Reassembly Stacking Protein 2. ROC analysis showed a mean area under the curve of 84%.

Conclusions

Gene expression based application of AI methods is feasible and represents a promising approach for future discriminatory diagnostics in children with acute appendicitis.

CT based Acute Appendicitis Severity Index for acute appendicitis and validate its effectiveness in predicting complicated appendicitis

AIM

To propose a CT-based scoring system called Acute Appendicitis Severity Index (AASI) for diagnosis of acute appendicitis and validates its effectiveness in predicting complicated appendicitis.

SUBJECTS AND METHODS

Retrospective analyses of CT images of 120 adult patients with pathologically proven uncomplicated (n = 64) and complicated (n = 56) acute appendicitis were performed. All patients had undergone a CT scan of the abdomen and pelvis using 320 multi-detectors computed tomography with Adaptive Iterative Dose Reduction 3D (AIDR 3D). CT image parameters were identified and used to develop a CT-based scoring system (AASI) to predict the severity of acute appendicitis and its outcome. All image analysis was performed by 2 radiologists and the total score was assigned to each patient based on the proposed CT scoring system. Validation of the effectiveness of the proposed scoring system (AASI) was done using statistical models.

RESULTS

The mean and standard deviation of AASI was found to be significantly higher (P value = 0.001) in the complicated appendicitis group (observer 1 = 10.2 ± 1.6 and observer 2 = 9.63 ± 2.3) as compared to that in uncomplicated acute appendicitis group (observer 1 = 7.09 ± 2.2 and observer 2 = 6.38 ± 1.9). There was an excellent interobserver agreement of the Acute Appendicitis Severity Index for both the uncomplicated and complicated appendicitis groups (K = 0.89, 95% CI = 0.87-0.92, P = 0.001). The cutoff value for AASI used to predict complicated appendicitis was taken as 9.5 and 8.5. This resulted in an AUC of 0.877 and 0.848, accuracy of 83% and 81%, the sensitivity of 75% and 80%, the specificity of 90% and 81%, the positive predictive value of 87% and 78%, and a negative predictive value of 81% and 83% by both reviewers respectively.

CONCLUSION

The proposed CT-based AASI is a reliable parameter for the prediction of complicated appendicitis.

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.

Use of gene expression profiling to identify candidate genes for pretherapeutic patient classification in acute appendicitis

Background

Phlegmonous and gangrenous appendicitis represent independent pathophysiological entities with different clinical courses ranging from spontaneous resolution to septic disease. However, reliable predictive methods for these clinical phenotypes have not yet been established. In an attempt to provide pathophysiological insights into the matter, a genomewide gene expression analysis was undertaken in patients with acute appendicitis.

Methods

Peripheral blood mononuclear cells were isolated and, after histological confirmation of PA or GA, analysed for genomewide gene expression profiling using RNA microarray technology and subsequent pathway analysis.

Results

Samples from 29 patients aged 7–17 years were included. Genomewide gene expression analysis was performed on 13 samples of phlegmonous and 16 of gangrenous appendicitis. From a total of 56 666 genes, 3594 were significantly differently expressed. Distinct interaction between T and B cells in the phlegmonous appendicitis group was suggested by overexpression of T cell receptor α and β subunits, CD2, CD3, MHC II, CD40L, and the B cell markers CD72 and CD79, indicating an antiviral mechanism. In the gangrenous appendicitis group, expression of genes delineating antibacterial mechanisms was found.

Conclusion

These results provide evidence for different and independent gene expression in phlegmonous and gangrenous appendicitis in general, but also suggest distinct immunological patterns for the respective entities. In particular, the findings are compatible with previous evidence of spontaneous resolution in phlegmonous and progressive disease in gangrenous appendicitis.

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.

The modified endoscopic retrograde appendicitis therapy versus antibiotic therapy alone for acute uncomplicated appendicitis in children

BACKGROUND

Endoscopic retrograde appendicitis therapy (ERAT) is an emerging endoscopic treatment modality for acute uncomplicated appendicitis (AUA) supported by several case series. However, to date, systematic studies have not been conducted in children and the prospective comparative data are lacking. Moreover, due to a concern for future malignancy risk in children from ionizing radiation, we used contrast-enhanced ultrasound (CEUS) instead of endoscopic retrograde appendiceal radiography (ERAR). Therefore, we conducted a prospective, randomized control clinical trial to compare the modified ERAT (mERAT) to antibiotic therapy in children with AUA. The aim of this study was to evaluate the safety and feasibility and of mERAT in the treatment of hospitalized children with AUA.

METHODS

Children with AUA, confirmed by ultrasonography and or abdominal computed tomography, were consecutively enrolled from October 2018 to February, 2020. They were randomly assigned to receive mERAT or routine antibiotic treatment. Patients were followed until May, 2020. Th primary outcome variable was the duration of relief of the abdominal pain after treatment. We collected patient's demographics, ultrasonic imaging findings, colonoscopy findings, and treatment outcomes of the mERAT and adverse even associated with mERAT.

RESULTS

A total of 83 children were enrolled. 36 were randomized to mERAT and 47 to antibiotics treatment. All children in the mERAT group had endoscopic confirmed acute uncomplicated appendicitis, and there were no significant complications. However, 9 of patients in antibiotic group were poor responsive to treatment and switched to mERAT. The overall success rate of treatment with mERAT (100%) was significantly higher than that of antibiotics (80.9%) (P = 0.004). The median time to discharge was significantly shorter in mERAT group than in antibiotics treatment group [6.0 ± 1.76 days] (P = 0.004).

CONCLUSIONS

mERAT provide a new alternative therapeutic option for childhood with AUA, especially for families who are reluctant to undergo an appendectomy.

The Borderline-Size Appendix: Grayscale, Color Doppler, and Spectral Doppler Findings That Improve Specificity for the Sonographic Diagnosis of Acute Appendicitis

Diagnostic criteria for acute appendicitis using graded compression sonography have been well established based on the maximum outer diameter (MOD) of the appendix, with MOD values of <6 mm nearly always indicating normal appendices and MOD values of >8 mm nearly always indicating appendicitis. However, the "borderline-size" appendix, meaning one whose MOD lies between these ranges (ie, an appendix with MOD of 6-8 mm), presents a diagnostic dilemma because appendices in this size range are neither clearly normal nor abnormal when diagnosis is based on the MOD alone; accordingly, such borderline MOD values are diagnostically equivocal, and sonographic diagnosis must rely on sonographic findings other than the MOD. The goal of this review was to examine the additional sonographic findings that can add specificity and help enable an accurate diagnosis to be made in patients with borderline-size appendices.