School of Engineering, Universidad de La Sabana, Chía, Colombia.
School of Medicine, Universidad de La Sabana, Chía, Colombia.
Int J Chron Obstruct Pulmon Dis. 2024 Jun 12;19:1333-1343. doi: 10.2147/COPD.S456390. eCollection 2024.
Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD).
This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD.
A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value.
Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.
人工智能新技术在多维模式识别方面表现出色，这使其在慢性阻塞性肺疾病（COPD）的诊断中具有一定的应用价值。
这是一项在门诊医疗中进行肺量计检查的患者的观察性分析性单中心研究。使用二次多项式对呼气峰流速至用力肺活量的部分进行建模，获得的系数用于训练和测试神经网络，以实现对 COPD 患者的分类任务。
共纳入 695 例患者记录进行分析。COPD 组患者明显比非 COPD 组患者年龄更大。对支气管扩张剂预前（Pre BD）和支气管扩张剂后（Post BD）的肺量计曲线进行二次多项式建模，并使用获得的系数来为三个神经网络（Pre BD、Post BD 和所有系数）提供输入。表现最佳的神经网络是使用支气管扩张剂后系数的神经网络，该网络具有 3 个神经元的输入层，具有 Sigmoid 激活函数的 3 个隐藏层和具有 Softmax 激活函数的 2 个神经元的输出层。该系统在使用专家判断作为参考测试时的准确率为 92.9%，敏感度为 88.2%，特异性为 94.3%。它还显示出优于当前金标准的性能，尤其是在特异性和阴性预测值方面。
由二次和三次多项式获得的系数来为人工神经网络提供输入具有模仿临床诊断过程的潜力，可以成为初级保健中帮助早期诊断 COPD 的重要辅助手段。
