Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition
Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition
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DOI：
10.1371/journal.pone.0206410
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发表时间：
2018-12-05
期刊：
影响因子：
3.7
通讯作者：
Oberhelman, Richard
中科院分区：
文献类型：
--
作者：
Correa, Malena;Zimic, Mirko;Oberhelman, Richard
肺炎是儿童死亡的主要原因之一，但如果及时诊断，通常可以通过抗生素治疗治愈。在许多发展中地区，由于医疗资源短缺，肺炎的诊断仍然是一个挑战。肺部超声已被证明是一个有用的工具，以检测肺实变作为肺炎的证据。然而，通过超声诊断肺炎具有局限性：它依赖于操作者，并且需要由训练有素的人员进行和解释。模式识别和图像分析是一种潜在的工具，可以自动诊断肺炎实变，而无需专家分析。本文提出了一种使用肺部超声成像和模式识别对肺炎进行自动分类的方法。这里提出的方法是基于对来自超声数字图像的矩形段（这里称为“特征向量”）中存在的亮度分布图案的分析。在第一步中，我们识别并消除了肺部超声帧中的皮肤和皮下组织（脂肪和肌肉），并使用标准神经网络使用人工智能方法分析“特征向量”。我们分析了来自秘鲁利马一家医院人群的21名5岁以下儿童（15名儿童经临床检查和X线确诊为肺炎，6名儿童无肺部疾病）的60个肺部超声帧。使用Ultrasonix超声设备获得肺部超声图像。使用标准神经网络分析了总共1450个阳性（肺炎）和1605个阴性（正常肺）向量，并用于创建一种区分肺浸润与健康肺的算法。使用该算法训练神经网络，它能够正确识别肺炎浸润，灵敏度为90.9%，特异性为100%。这种方法可用于开发操作员独立的计算机算法，用于使用超声诊断幼儿肺炎。
Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultra-sound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.