论文标题
使用光声成像自动对儿童的神经肌肉疾病进行自动分类
Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging
论文作者
论文摘要
神经肌肉疾病（NMDS）给医疗保健系统和社会带来重大负担。它们可能导致严重的进行性肌肉无力，肌肉变性，染色，畸形和进行性残疾。在这项研究中评估的NMD经常在幼儿时期表现出来。作为疾病的亚型，例如Duchenne肌肉运动障碍（DMD）和脊柱肌肉萎缩（SMA）很难在开始时进行区分，并且迅速，快速，可靠的鉴别诊断至关重要。光声和超声成像显示出可视化和量化不同疾病程度的巨大潜力。自动分类此类图像数据可以进一步改善标准诊断程序。我们比较了基于VGG16的深度学习的2级和3级分类器，以将健康与患病的肌肉组织区分开。这项工作显示了3级问题高于0.86的高精度的有希望的结果，可以用作NMD的早期诊断和治疗监测的未来方法的概念证明。
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability. The NMDs evaluated in this study often manifest in early childhood. As subtypes of disease, e.g. Duchenne Muscular Dystropy (DMD) and Spinal Muscular Atrophy (SMA), are difficult to differentiate at the beginning and worsen quickly, fast and reliable differential diagnosis is crucial. Photoacoustic and ultrasound imaging has shown great potential to visualize and quantify the extent of different diseases. The addition of automatic classification of such image data could further improve standard diagnostic procedures. We compare deep learning-based 2-class and 3-class classifiers based on VGG16 for differentiating healthy from diseased muscular tissue. This work shows promising results with high accuracies above 0.86 for the 3-class problem and can be used as a proof of concept for future approaches for earlier diagnosis and therapeutic monitoring of NMDs.
