Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer

TL;DR

This paper introduces a portable, cost-effective imaging flow cytometer that uses deep learning and lensfree holographic imaging to detect Giardia lamblia cysts in water samples in real-time without labels, suitable for resource-limited settings.

Contribution

It presents a novel, compact, and label-free imaging flow cytometer integrated with deep learning for real-time detection of waterborne parasites.

Findings

Detects Giardia cysts at <10 per 50 mL in real-time

Operates at 100 mL/h volumetric throughput

Portable device suitable for field use

Abstract

We report a field-portable and cost-effective imaging flow cytometer that uses deep learning to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL/h. This flow cytometer uses lensfree color holographic imaging to capture and reconstruct phase and intensity images of microscopic objects in a continuously flowing sample, and automatically identifies Giardia Lamblia cysts in real-time without the use of any labels or fluorophores. The imaging flow cytometer is housed in an environmentally-sealed enclosure with dimensions of 19 cm x 19 cm x 16 cm and weighs 1.6 kg. We demonstrate that this portable imaging flow cytometer coupled to a laptop computer can detect and quantify, in real-time, low levels of Giardia contamination (e.g., <10 cysts per 50 mL) in both freshwater and seawater samples. The field-portable and label-free nature of this method has the potential to allow rapid and automated screening of drinking water supplies in resource limited settings in order to detect waterborne parasites and monitor the integrity of the filters used for water treatment.