Yanhua Zheng, Ruilin Ren, Teng Zuo, Xuan Chen, Hanxuan Li, Cheng Xie, Meiling Weng, Chunxiao He, Min Xu, Lili Wang, Nainong Li, Xiaofan Li
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引用次数: 0
摘要
背景:尽管造血干细胞移植(HSCT)后患者的早期影像学改变,但CMV肺炎的诊断仍存在挑战:本研究旨在采用一种深度学习模型,将CMV肺炎与COVID-19肺炎、社区获得性肺炎以及造血干细胞移植术后正常肺部区分开来:首先,用 Kaggle 的 COVID 多类数据集(数据集 A)中的 COVID-19 肺炎、社区获得性肺炎和正常肺 CT 图像预训练 6 个神经网络模型,然后将数据集 A 与本中心的 CMV 肺炎图像合并,形成数据集 B:2018年1月至2022年12月期间,共发现34例HSCT后CMV肺炎病例。数据集 A 包含来自 Kaggle 的每个分组的 1681 张图像。结合数据集 A,数据集 B 最初由 98 张 CMV 肺炎和正常肺部的图像组成。最佳模型(Xception)的准确率为 0.9034。在数据集 B 的测试集中,精确度、召回率和 F1 分数都达到了 0.9091,AUC 为 0.9668:该框架展示了深度学习模型利用少量 CT 图像区分罕见肺炎类型的能力,有助于早期检测 HSCT 后 CMV 肺炎。
Prediction of early-phase cytomegalovirus pneumonia in post-stem cell transplantation using a deep learning model.
Background: Diagnostic challenges exist for CMV pneumonia in post-hematopoietic stem cell transplantation (post-HSCT) patients, despite early-phase radiographic changes.
Objective: The study aims to employ a deep learning model distinguishing CMV pneumonia from COVID-19 pneumonia, community-acquired pneumonia, and normal lungs post-HSCT.
Methods: Initially, 6 neural network models were pre-trained with COVID-19 pneumonia, community-acquired pneumonia, and normal lung CT images from Kaggle's COVID multiclass dataset (Dataset A), then Dataset A was combined with the CMV pneumonia images from our center, forming Dataset B. We use a few-shot transfer learning strategy to fine-tune the pre-trained models and evaluate model performance in Dataset B.
Results: 34 cases of CMV pneumonia were found between January 2018 and December 2022 post-HSCT. Dataset A contained 1681 images of each subgroup from Kaggle. Combined with Dataset A, Dataset B was initially formed by 98 images of CMV pneumonia and normal lung. The optimal model (Xception) achieved an accuracy of 0.9034. Precision, recall, and F1-score all reached 0.9091, with an AUC of 0.9668 in the test set of Dataset B.
Conclusions: This framework demonstrates the deep learning model's ability to distinguish rare pneumonia types utilizing a small volume of CT images, facilitating early detection of CMV pneumonia post-HSCT.
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Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered:
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