A multiple instance learning approach for detecting COVID-19 in peripheral blood smears.
PLOS Digit Health
; 1(8): e0000078, 2022 Aug.
Article
in English
| MEDLINE | ID: covidwho-2255555
ABSTRACT
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:
Available
Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
PLOS Digit Health
Year:
2022
Document Type:
Article
Affiliation country:
Journal.pdig.0000078
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