An approach to model Right Iliac Fossa pain using pain-only-parameters for screening acute appendicitis

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.

The Alvarado Score Is the Most Impactful Diagnostic Tool for Appendicitis: A Bibliometric Analysis

INTRODUCTION

The objective of this bibliometric analysis was to investigate the citation pattern of studies that have developed a diagnostic tool to diagnose appendicitis.

METHODS

We investigated characteristics of citations, publication frequency, evolution of citations, and fluctuation of previously highly cited studies. We analyzed which studies had been cited in the method section and identified impactful studies in this research field by a network visualization. We analyzed the differences in citations between diagnostic tools requiring a doctor to be present against the diagnostic tools not requiring doctors to be present, English language studies against non-English studies, and identified diagnostic tools targeting children.

RESULTS

There was an upward trend in publications in this research field, and between 1999-2021 the Alvarado score has been cited the most. In general, there was a high fluctuation, and 40 studies had been cited in the methods sections. There were significant differences in studies regarding diagnostic tools written in English compared to non-English studies, with more citations in the English-language studies. Furthermore, 22 studies had children as the target population.

CONCLUSIONS

The Alvarado score was the highest cited study since 1999, with 1086 citations, making it the most impactful study in this research field of diagnostic tools to diagnose appendicitis. Due to the diversity of target populations and settings for which diagnostic tools are developed, there is a need to expand research on diagnostic tools for appendicitis.

Many diagnostic tools for appendicitis: a scoping review

BACKGROUND

We aimed to provide an overview of all diagnostic tools developed to diagnose appendicitis with their reported accuracy and to further characterize these including their need for diagnostic equipment.

METHODS

This scoping review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews and a protocol was registered at Open Science Framework. We performed a systematic literature search in PubMed, Embase, China National Knowledge Infrastructure, Índice Bibliográfico Espanhol de Ciências da Saúde, and Latin American and Caribbean Health Sciences Literature. We included original articles of all languages with the purpose to derive an accessible diagnostic tool. We extracted data regarding study- and diagnostic tool characteristics, and the accuracy of each diagnostic tool.

RESULTS

The search led to 6419 records, where 74 studies were included, yielding 82 diagnostic tools reported in seven different languages. Among these tools, 35% included patient characteristics, 85% symptoms, 93% physical examinations, 37% vital signs, 78% laboratory values, and 16% imaging. Among the diagnostic tools, 35% relied on a medical doctor/surgeon with access to a laboratory, and six diagnostic tools did not require a bedside medical doctor/surgeon. The median positive predictive value, negative predictive value, sensitivity, and specificity across diagnostic tools were 91%, 94%, 89%, and 86%, respectively.

CONCLUSIONS

We identified 82 diagnostic tools that most frequently were based on symptoms and physical examinations. Most diagnostic tools relied on a medical doctor/surgeon with access to laboratory values. The accuracy was high across the diagnostic tools.