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

Abstract

Background To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. Main body A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics. Results In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues. Conclusion AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.

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

“…The use of AI in the diagnosis of acute appendicitis is still emerging [ 40 – 43 ]. A small number of studies have been published so far.…”

“…A small number of studies have been published so far. The achievements of various research proposed in recent years in the field of ML and sophisticated human anatomy designed DL have been remarkable [ 43 – 45 ]. The aim of several studies was primarily the diagnosis of acute appendicitis, but also the differentiation between complicated and uncomplicated forms [ 40 – 42 , 46 – 48 ].…”

“…In the last few years, DL, a subset of AI based on ANN, has been increasingly used, with excellent results. ANN have proven to be particularly effective for the diagnosis of acute appendicitis [ 43 , 52 ]. Prabhudesai et al [ 58 ] showed that the use of ANN was better to exclude false appendicitis compared to the clinic and Alvarado score, significantly.…”