人工神经网络：诊断急性阑尾炎的有用辅助手段。
Artificial neural networks: useful aid in diagnosing acute appendicitis.
作者信息
Prabhudesai S G, Gould S, Rekhraj S, Tekkis P P, Glazer G, Ziprin P
机构信息
Department of Biosurgery and Surgical Technology, Faculty of Medicine, Imperial College London, St. Mary's Hospital Campus, Room 1029, 10th floor QEQM Building, Praed Street, London, W2 1NY, UK.
出版信息
World J Surg. 2008 Feb;32(2):305-9; discussion 310-1. doi: 10.1007/s00268-007-9298-6.
BACKGROUND
[corrected] The purpose of the study was to assess the role of artificial neural networks (ANNs) in the diagnosis of appendicitis in patients presenting with acute right iliac fossa (RIF) pain and comparing its performance with the assessment made by experienced clinicians and the Alvarado score.
METHODS
After training and testing an ANN, data from 60 patients presenting with suspected appendicitis over a 6-month period to a teaching hospital was collected prospectively. Accuracy of diagnosing appendicitis by the clinician, the Alvarado score, and the ANN was compared.
RESULTS
The sensitivity, specificity, and positive and negative predictive values of the ANN were 100%, 97.2%, 96.0%, and 100% respectively. The ability of the ANN to exclude accurately the diagnosis of appendicitis in patients without true appendicitis was statistically significant compared to the clinical performance (p=0.031) and Alvarado score of >or=6 (p=0.004) and nearly significant compared to the Alvarado score of >or=7 (p=0.063).
CONCLUSIONS
ANNs can be an effective tool for accurately diagnosing appendicitis and may reduce unnecessary appendectomies.
背景
[已校正] 本研究的目的是评估人工神经网络（ANN）在诊断急性右下腹（RIF）疼痛患者阑尾炎中的作用，并将其性能与经验丰富的临床医生的评估以及阿尔瓦拉多评分进行比较。
方法
在对一个人工神经网络进行训练和测试后，前瞻性收集了一家教学医院6个月内60例疑似阑尾炎患者的数据。比较了临床医生、阿尔瓦拉多评分和人工神经网络诊断阑尾炎的准确性。
结果
人工神经网络的敏感性、特异性、阳性和阴性预测值分别为100%、97.2%、96.0%和100%。与临床诊断表现（p = 0.031）以及阿尔瓦拉多评分≥6（p = 0.004）相比，人工神经网络准确排除无真正阑尾炎患者阑尾炎诊断的能力具有统计学意义，与阿尔瓦拉多评分≥7（p = 0.063）相比接近具有统计学意义。
结论
人工神经网络可以成为准确诊断阑尾炎的有效工具，并可能减少不必要的阑尾切除术。
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