Open AccessMobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning
Author(s) -
Ahmed Shokr,
Luis Gustavo Carvalho Pacheco,
Prudhvi Thirumalaraju,
Manoj Kumar Kanakasabapathy,
Jahnavi Gandhi,
Deeksha Kartik,
Filipe S R Silva,
Eda Erdogmus,
Hemanth Kandula,
Shenglin Luo,
Xu G. Yu,
Raymond T. Chung,
Jonathan Z. Li,
Daniel R. Kuritzkes,
Hadi Shafiee
Publication year - 2020
Publication title -
acs nano
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.554
H-Index - 382
eISSN - 1936-086X
pISSN - 1936-0851
DOI - 10.1021/acsnano.0c06807
Subject(s) - computer science , artificial intelligence , generalizability theory , mhealth , deep learning , machine learning , software deployment , medicine , statistics , mathematics , psychiatry , psychological intervention , operating system
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 s martphone-based p athogen de tection r esource m ultiplier using a dversarial n etworks (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.