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. 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. 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. 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.

中文翻译：

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人工智能与急性阑尾炎：诊断和预后模型的系统回顾


与传统方法相比，评估人工智能 (AI) 模型在诊断和预测成人患者急性阑尾炎 (AA) 方面的功效。 AA 是急诊就诊和腹部手术的常见原因。通常通过临床评估、实验室测试和影像学研究来诊断。然而，传统的诊断方法可能既耗时又不准确。机器学习模型在提高诊断准确性和预测结果方面显示出了希望。根据 PRISMA 指南进行了系统评价，检索了 PubMed、Embase、Scopus 和 Web of Science 数据库。使用预测模型偏倚风险评估工具评估研究的偏倚风险。提取的数据点包括模型类型、输入特征、验证策略和关键性能指标。总共分析了 29 项研究，其中 21 项关注诊断，7 项关注预后，1 项关注两者。人工神经网络（ANN）是最常用的诊断算法。人工神经网络和逻辑回归也被广泛用于对 AA 类型进行分类。 ANN 在大多数情况下表现出高性能，准确率通常超过 80%，AUC 值峰值为 0.985。该模型在预测脓毒症风险和 ICU 入院等术后结果方面也显示出良好的结果。大多数研究都发现了偏倚风险，其中选择偏倚和缺乏内部验证是最常见的问题。人工智能算法在诊断和预测 AA 方面展现出巨大的前景，在速度和准确性方面往往超越传统方法和临床评分，例如 Alvarado 评分系统。