Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications

Abstract

Acute appendicitis (AAp) remains one of the most common abdominal emergencies, requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries. Conventional diagnostic methods, including medical history, clinical assessment, biochemical markers, and imaging techniques, often present limitations in sensitivity and specificity, especially in atypical cases. In recent years, artificial intelligence (AI) has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning (ML) and deep learning (DL) models. This review evaluates the current applications of AI in both adult and pediatric AAp, focusing on clinical data-based models, radiological imaging analysis, and AI-assisted clinical decision support systems. ML models such as random forest, support vector machines, logistic regression, and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems, achieving sensitivity and specificity rates exceeding 90% in multiple studies. Additionally, DL techniques, particularly convolutional neural networks, have been shown to outperform radiologists in interpreting ultrasound and computed tomography images, enhancing diagnostic confidence. This review synthesized findings from 65 studies, demonstrating that AI models integrating multimodal data including clinical, laboratory, and imaging parameters further improved diagnostic precision. Moreover, explainable AI approaches, such as SHapley Additive exPlanations and local interpretable model-agnostic explanations, have facilitated model transparency, fostering clinician trust in AI-driven decision-making. This review highlights the advancements in AI for AAp diagnosis, emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases. While preliminary results are promising, further prospective, multicenter studies are required for large-scale clinical implementation, given that a great proportion of current evidence derives from retrospective designs, and existing prospective cohorts exhibit limited sample sizes or protocol variability. Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.

Keywords: Acute appendicitis; Complicated appendicitis; Artificial intelligence; Machine learning; Deep learning; Decision support systems; Explainable artificial intelligence; Predictive modeling; Diagnosis

Core Tip: This comprehensive review explores the emerging role of artificial intelligence (AI), including machine learning and deep learning techniques, in diagnosing acute appendicitis (AAp). Despite advancements in imaging and clinical scoring, diagnosing AAp remains challenging, particularly in atypical cases. AI models such as random forests, support vector machines, and convolutional neural networks have demonstrated promising results in enhancing diagnostic accuracy and decision-making. In addition to aiding in the differential diagnosis of AAp from other causes of acute abdominal pain, AI approaches have also been applied to distinguish between complicated and uncomplicated appendicitis, thereby supporting risk stratification and guiding management strategies. The review discusses current evidence, potential benefits, and limitations of integrating AI-based decision support into clinical practice. These insights may pave the way for more precise, timely, and individualized management of AAp.