Abstract：
High-resolution CT (HRCT) imaging features of idiopathic interstitial pneumonia (IIP) patients are related to glucocorticoid sensitivity. This study aimed to develop an artificial intelligence model to assess glucocorticoid efficacy according to the HRCT imaging features of IIP. The medical records and chest HRCT images of 150 patients with IIP were analyzed retrospectively. The U-net framework was used to create a model for recognizing different imaging features, including ground glass opacities, reticulations, honeycombing, and consolidations. Then, the area ratio of those imaging features was calculated automatically. Forty-five patients were treated with glucocorticoids, and according to the drug efficacy, they were divided into a glucocorticoid-sensitive group and a glucocorticoid-insensitive group. Models assessing the correlation between imaging features and glucocorticoid sensitivity were established using the k-nearest neighbor (KNN) algorithm. The total accuracy (ACC) and mean intersection over union (mIoU) of the U-net model were 0.9755 and 0.4296, respectively. Out of the 45 patients treated with glucocorticoids, 34 and 11 were placed in the glucocorticoid-sensitive and glucocorticoid-insensitive groups, respectively. The KNN-based model had an accuracy of 0.82. An artificial intelligence model was successfully developed for recognizing different imaging features of IIP and a preliminary model for assessing the correlation between imaging features and glucocorticoid sensitivity in IIP patients was established.
摘要：
特发性间质性肺炎（IIP）患者的高分辨率CT（HRCT）影像学特征与糖皮质激素敏感性有关。本研究旨在根据IIP的HRCT影像特征，建立一种评估糖皮质激素疗效的人工智能模型。回顾性分析150例IIP患者的病历和胸部HRCT图像。U-net框架用于创建识别不同成像特征的模型，包括磨砂玻璃不透明度，网状,蜂窝,和合并。然后，这些成像特征的面积比是自动计算的.45例患者接受糖皮质激素治疗,根据药物疗效，分为糖皮质激素敏感组和糖皮质激素不敏感组.使用k最近邻（KNN）算法建立评估成像特征与糖皮质激素敏感性之间相关性的模型。U-net模型的总精度（ACC）和联合平均交集（mIoU）分别为0.9755和0.4296。在45例接受糖皮质激素治疗的患者中，34和11分别被置于糖皮质激素敏感和糖皮质激素不敏感组，分别。基于KNN的模型具有0.82的准确度。成功开发了用于识别IIP不同成像特征的人工智能模型，并初步建立了评估IIP患者成像特征与糖皮质激素敏感性之间相关性的模型。
