A multiple instance learning approach for detecting COVID-19 in peripheral blood smears

Journal Article

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. We integrated image and diagnostic information from across 236 patients to demonstrate not only that there is a significant link between blood and a patient’s COVID-19 infection status, but also that novel machine learning approaches offer a powerful and scalable means to analyze peripheral blood smears. Our results both backup and enhance hematological findings relating blood cell morphology to COVID-19, and offer a high diagnostic efficacy; with a 79% accuracy and a ROC-AUC of 0.90.

Full Text

Duke Authors

Cited Authors

  • Cooke, CL; Kim, K; Xu, S; Chaware, A; Yao, X; Yang, X; Neff, J; Pittman, P; McCall, C; Glass, C; Jiang, XS; Horstmeyer, R

Cited Editors

  • Yoon, D

Published Date

  • August 19, 2022

Published In

Volume / Issue

  • 1 / 8

Start / End Page

  • e0000078 - e0000078

Published By

Electronic International Standard Serial Number (EISSN)

  • 2767-3170

Digital Object Identifier (DOI)

  • 10.1371/journal.pdig.0000078


  • en