Automated Detection of <i>P</i>. <i>falciparum</i> Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

<div><p>Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite <i>Plasmodium falciparum</i> at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and <i>k</i>-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.</p></div

Distribution of RBC types used for imaging experiments.

<p>Distribution of RBC types used for imaging experiments.</p

WBCs.

<p>WBCs, N = 27 (square tile = 20μm x 20μm).</p

Infection staging.

<p>Table showing performance of multinomial machine learning algorithms: Infection stages.</p

QPS system diagram.

<p>(A) Quantitative phase spectroscopy system: (DG) diffraction grating, (GSM) galvanometric scanning mirror, (LP) linear polarizer, (BS) beam splitter, (RR) retroreflectors, (MO) microscope objective. Path-matched sample (S) and reference (R) beams create off-axis interferograms imaged by a CMOS camera. (B) Interferogram spectral sweep from 475–700nm at 5nm increments in ~6s (C) Interferogram with corresponding fringes created by off-axis angle difference between the sample and reference arms.</p

OPL histograms.

<p>(A-D) OPL histograms of uninfected RBC and RBCs infected by <i>P</i>. <i>falciparum</i> in early trophozoite, late trophozoite, and schizont stages respectively as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163045#pone.0163045.g003" target="_blank">Fig 3</a>.</p

Morphological parameters used to describe RBCs.

<p>Morphological parameters used to describe RBCs.</p

OPL maps.

<p>Uninfected RBC and RBCs infected by <i>P</i>. <i>falciparum</i> in early trophozoite, late trophozoite, and schizont stages represented respectively as: (A-D) OPL maps, (E-F) OPL maps from different viewpoint (scale bars = 5μm).</p

Uninfected vs. Infected RBC.

<p>A) Accuracy of nearest neighbor classification (NNC), logistic regression (LR), and linear discriminant classification (LDC) used to distinguish uninfected RBCs from RBCs infected with <i>P</i>.<i>falciparum</i> parasites in early trophozoite (ET), late trophozoite (LT), and schizont (S) stages. B- D) ROC curves and their corresponding AUC for NNC, LR, and LDC respectively.</p

Bright-field microscopy images.

<p>Bright-field microscopy images: (A) uninfected RBC (B-D) RBCs with malaria parasite in early trophozoite, late trophozoite, and schizont stages respectively (scale bars = 5μm).</p