COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy

• Published in: Optics express Vol. 30; no. 2; p. 1723
• Main Authors: O’Connor, Timothy, Santaniello, Sabato, Javidi, Bahram
• Format: Journal Article
• Language: English
• Published: United States 17.01.2022
• Subjects: Case-Control Studies; COVID-19 - blood; COVID-19 - diagnostic imaging; Equipment Design; Erythrocytes - classification; Holography - instrumentation; Holography - methods; Humans; Intravital Microscopy - instrumentation; Intravital Microscopy - methods; Preliminary Data; ROC Curve; Sensitivity and Specificity
• ISSN: 1094-4087; 1094-4087
• DOI: 10.1364/OE.442321

We present an automated method for COVID-19 screening based on reconstructed phase profiles of red blood cells (RBCs) and a highly comparative time-series analysis (HCTSA). Video digital holographic data ­was obtained using a compact, field-portable shearing microscope to capture the temporal fluctuations and spatio-temporal dynamics of live RBCs. After numerical reconstruction of the digital holographic data, the optical volume is calculated at each timeframe of the reconstructed data to produce a time-series signal for each cell in our dataset. Over 6000 features are extracted on the time-varying optical volume sequences using the HCTSA to quantify the spatio-temporal behavior of the RBCs, then a linear support vector machine is used for classification of individual RBCs. Human subjects are then classified for COVID-19 based on the consensus of their cells’ classifications. The proposed method is tested on a dataset of 1472 RBCs from 24 human subjects (10 COVID-19 positive, 14 healthy) collected at UConn Health Center. Following a cross-validation procedure, our system achieves 82.13% accuracy, with 92.72% sensitivity, and 73.21% specificity (area under the receiver operating characteristic curve: 0.8357). Furthermore, the proposed system resulted in 21 out of 24 human subjects correctly labeled. To the best of our knowledge this is the first report of a highly comparative time-series analysis using digital holographic microscopy data.