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Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning
ACS Nano ( IF 17.1 ) Pub Date : 2020-11-23 , DOI: 10.1021/acsnano.0c06807 Ahmed Shokr 1 , Luis G C Pacheco 1, 2 , Prudhvi Thirumalaraju 1 , Manoj Kumar Kanakasabapathy 1 , Jahnavi Gandhi 1 , Deeksha Kartik 1 , Filipe S R Silva 1, 2 , Eda Erdogmus 1 , Hemanth Kandula 1 , Shenglin Luo 1 , Xu G Yu 3, 4, 5 , Raymond T Chung 5, 6 , Jonathan Z Li 4, 5 , Daniel R Kuritzkes 4, 5 , Hadi Shafiee 1, 5
ACS Nano ( IF 17.1 ) Pub Date : 2020-11-23 , DOI: 10.1021/acsnano.0c06807 Ahmed Shokr 1 , Luis G C Pacheco 1, 2 , Prudhvi Thirumalaraju 1 , Manoj Kumar Kanakasabapathy 1 , Jahnavi Gandhi 1 , Deeksha Kartik 1 , Filipe S R Silva 1, 2 , Eda Erdogmus 1 , Hemanth Kandula 1 , Shenglin Luo 1 , Xu G Yu 3, 4, 5 , Raymond T Chung 5, 6 , Jonathan Z Li 4, 5 , Daniel R Kuritzkes 4, 5 , Hadi Shafiee 1, 5
Affiliation
Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.
中文翻译:
通过自适应对抗学习启用移动健康 (mHealth) 病毒诊断
基于深度学习 (DL) 的图像处理有可能彻底改变智能手机在传染病移动健康 (mHealth) 诊断中的使用。然而,手机图像数据采集的高度可变性以及传统 DL 模型训练对大量专家注释图像的普遍需求可能会妨碍基于智能手机的诊断的普遍性。在这里,我们使用带有条件的对抗性神经网络开发了一个易于重新配置的病毒诊断平台,该平台利用智能手机拍摄的微流控芯片照片数据集,按需快速生成不同目标病原体的图像分类器。对抗性学习还用于通过样式生成对抗网络 (StyleGAN) 生成 16,000 个逼真的合成微芯片图像来增强这个真实图像数据集。s基于智能手机的病原体检测资源倍增器,使用对抗网络( SPyDERMAN ),准确检测临床样本中不同的完整病毒,并通过与 CRISPR 诊断集成检测病毒核酸。我们使用 179 个患者样本评估了该系统在检测五种不同病毒靶标方面的性能。通过快速重新配置以检测鼻拭子样本中的 SARS-CoV-2 抗原,证实了该系统的普遍性(n= 62) 具有 100% 的准确度。总体而言,SPyDERMAN 系统可以通过提供基于智能手机的诊断平台来促进流行病防备策略,该平台可以在工作几天内适应给定的新兴病毒剂。
更新日期:2021-01-26
中文翻译:
通过自适应对抗学习启用移动健康 (mHealth) 病毒诊断
基于深度学习 (DL) 的图像处理有可能彻底改变智能手机在传染病移动健康 (mHealth) 诊断中的使用。然而,手机图像数据采集的高度可变性以及传统 DL 模型训练对大量专家注释图像的普遍需求可能会妨碍基于智能手机的诊断的普遍性。在这里,我们使用带有条件的对抗性神经网络开发了一个易于重新配置的病毒诊断平台,该平台利用智能手机拍摄的微流控芯片照片数据集,按需快速生成不同目标病原体的图像分类器。对抗性学习还用于通过样式生成对抗网络 (StyleGAN) 生成 16,000 个逼真的合成微芯片图像来增强这个真实图像数据集。s基于智能手机的病原体检测资源倍增器,使用对抗网络( SPyDERMAN ),准确检测临床样本中不同的完整病毒,并通过与 CRISPR 诊断集成检测病毒核酸。我们使用 179 个患者样本评估了该系统在检测五种不同病毒靶标方面的性能。通过快速重新配置以检测鼻拭子样本中的 SARS-CoV-2 抗原,证实了该系统的普遍性(n= 62) 具有 100% 的准确度。总体而言,SPyDERMAN 系统可以通过提供基于智能手机的诊断平台来促进流行病防备策略,该平台可以在工作几天内适应给定的新兴病毒剂。