Department of Pediatric Surgery, Charité -Universitätsmedizin Berlin, Augustenburger Platz, Berlin, Germany.
OakLabs GmbH, Hennigsdorf, Germany.
PLoS One. 2019 Sep 25;14(9):e0222030. doi: 10.1371/journal.pone.0222030. eCollection 2019.
Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.
急性阑尾炎是儿童和青少年急诊手术的主要原因之一。阑尾切除术仍然是首选治疗方法，但对于非复杂性炎症，越来越多的保守策略正在被研究。由于临床表现通常不典型，急性阑尾炎的诊断仍然具有挑战性。炎症血液标志物和超声等影像学方法受到限制，因为它们必须由专家进行解释，并且仍然不能提供足够的诊断确定性。本研究提出了一种使用针对疑似阑尾炎患者常规且无偏获得的数值/参数自动诊断阑尾炎以及区分复杂和非复杂性炎症的方法。我们分析了来自德国柏林一家医院基于人群的儿童和青少年（0-17 岁）的全血细胞计数、C 反应蛋白（CRP）和超声检查的阑尾直径。共有 590 名患者（组织病理学上有 473 名阑尾炎患者和 117 名组织病理学阴性发现患者）被回顾性地用来自机器学习（ML）和人工智能（AI）的现代算法进行分析。信息参数（生物标志物特征）的发现和分类模型的训练是在最多 35%的患者中完成的。其余至少 65%的患者用于验证。在临床相关截止点，生物标志物特征诊断阑尾炎的准确率为 90%（93%的敏感性，67%的特异性），而正确识别复杂炎症的准确率为 51%（95%的敏感性，33%的特异性）在验证数据上。这样的测试将能够防止三分之二没有阑尾炎的患者进行无用的手术，以及三分之一患有非复杂性阑尾炎的患者。该方法有可能改变目前的阑尾炎治疗方法，并展示了 AI 和 ML 算法的能力，即使基于常规诊断参数，也可以显著提高诊断能力。
