Colin L. Cooke, Kanghyun Kim, Shiqi Xu, Amey Chaware, Xing Yao, Xi Yang, Jadee Neff, Patricia Pittman, Chad McCall, Carolyn Glass, Xiaoyin Sara Jiang, Roarke Horstmeyer
A wide variety of diseases are commonly diagnosed via the visual examination of cell morphology within a peripheral blood smear. For certain diseases, such as COVID-19, morphological impact across the multitude of blood cell types is still poorly understood. In this paper, we present a multiple instance learning-based approach to aggregate high-resolution morphological information across many blood cells and cell types to automatically diagnose disease at a per-patient level.
The analysis of blood cell morphology plays a critical role in hematology to diagnose and understand various diseases. A key tool for blood cell morphology assessment is the light microscope, which is often applied to examine peripheral blood smears (PBS). In a typical procedure, a physician will visually examine white and red blood cells within a PBS on a glass slide at high microscope magnification (usually 100×).
We collected digital anonymized PBS images from patients at the Duke Medical Center (IRB Protocol 00105472). We preserved patient anonymity by only collecting PBS image data and COVID-19 infection status within the standard group of tested patients.
Data preprocessing and augmentation
We applied the same kind of data preprocessing and augmentations to all images used within this study. To prepare the data for processing by our neural networks we cropped and normalized the images.
Machine learning system
Our machine learning system is a novel hybrid of two complementary multiple instance learning (MIL) approaches (shown in Fig 2). In the first branch, we used DenseNet-121 , a convolutional neural network (CNN), to process each image from a patient PBS image set independently
Inflammatory Protein Biomarkers
The Systems Approach to Biomarker Research in Cardiovascular Disease initiative measured 85 plasma proteins in the participants of the FHS Offspring cohorts. From these, we chose 11 protein biomarkers related to innate or adaptive immune cells or known to be associated with inflammatory responses for our investigation, as mounting evidence demonstrates a role for immune cells and inflammation in the disease pathogenesis of AD dementia and cognitive disorders.
In this paper, we present a MIL-based method to accurately diagnose the COVID-19 disease at a per-patient level from high-resolution morphological information across many blood cells and cell types.
Citation: Cooke CL, Kim K, Xu S, Chaware A, Yao X, Yang X, et al. (2022) A multiple instance learning approach for detecting COVID-19 in peripheral blood smears. PLOS Digit Health 1(8): e0000078. https://doi.org/10.1371/journal.pdig.0000078
Editor: Dukyong Yoon, Yonsei University College of Medicine, REPUBLIC OF KOREA
Received: January 24, 2022; Accepted: June 21, 2022; Published: August 19, 2022.
Copyright: © 2022 Cooke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Due to the conditions and agreements under which this data was collected, no data will be made publicly available, but can be requested at https://irb.duhs.duke.edu/ under IRB Pro00105472-KSP-2.0 - Cell morphology of COVID-19-positive blood smear images. The code used within this work has been made publicly available at: https://github.com/clvcooke/covid-blood.
Funding: This study was funded by a Duke-Coulter Translational Partnership, a fellowship from the Natural Sciences and Engineering Research Council (NSERC) of Canada, and funding from a 3M Nontenured Faculty Award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: RH is the scientific director of Ramona Optics Inc., and RH and AC are co-founders of Airilabs LLC. Both companies are developing novel hardware for microscope imaging.