Caliman Sturdza Olga Adriana, Filip Florin, Terteliu Baitan Monica, Dimian Mihai
Faculty of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, 720229 Suceava, Romania.
Emergency Clinical Hospital Suceava, 720237 Suceava, Romania.
Diagnostics (Basel). 2025 Jul 21;15(14):1830. doi: 10.3390/diagnostics15141830.
The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies.
新冠病毒病（COVID-19）的迅速传播增加了对快速诊断工具的需求，这促使科学家们对使用胸部成像（如胸部X线（CXR）和计算机断层扫描（CT））的深度学习（DL）应用进行广泛研究。本综述考察了DL架构，特别是卷积神经网络（CNN）和新兴的视觉Transformer（ViT）在识别与COVID-19相关的肺部异常方面的发展和性能。单独的残差网络（ResNet）架构与CNN模型一起，通过迁移协议展现出强大的诊断性能；然而，ViT具有更好的性能，可读性提高且数据需求减少。多模态诊断系统现在除了成像之外，还纳入了使用肺部超声、临床数据和咳嗽声音评估的替代方法。在数据、特征和决策层面运行的信息融合技术提高了诊断性能。然而，COVID-19检测的进展受到一些持续问题的阻碍，这些问题源于受限且不统一的数据集，以及图像标准的领域差异，还有诊断过度拟合和泛化能力差的问题。COVID-19诊断的最新进展包括构建庞大的多噪声信息集，同时创建面向临床过程的人工智能算法，并实施分布式学习协议以确保信息安全和系统稳定性。虽然基于深度学习的COVID-19检测系统在临床应用中显示出强大潜力，但更广泛的验证、监管批准以及持续调整对于其成功部署和制定未来大流行应对策略仍然至关重要。
