Training device expands access to high-performance diagnostic testing

by PNAS Nexus

edited by Gaby Clark, reviewed by Andrew Zinin

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The rise of automation and AI has raised fears about job loss, but smart tools can also train workers rather than replace them. Meanwhile, a chronic shortage of trained personnel limits the reach of health care in both developing and developed countries.

Minkyo Lee, Rustem F. Ismagilov and colleagues developed and described a device that trains and assists laboratory-inexperienced personnel in performing laboratory workflows. Specifically, the device monitors a user's progress through the steps of sample pooling for nucleic acid amplification tests (NAATs), which can detect a wide range of infectious pathogens. Their work is published in PNAS Nexus.

Sample pooling, in which equal volumes from multiple specimens are combined and tested together, can reduce per-sample costs and increase testing throughput while preserving clinical performance. However, pooling can be error-prone, as workers must transfer clinical specimens correctly, avoid cross-contamination and maintain accurate documentation.

The authors aimed to make pooling more practical in small- and medium-scale health settings, such as mobile or decentralized testing sites, where trained personnel or expensive automated liquid handlers may be unavailable. The device offers step-by-step guidance and tracks user progress, precisely weighing each sample tube and providing clear, real-time feedback if too much or too little sample is added.

The device helps users fix correctable mistakes, but if a detected mistake is uncorrectable, it guides the user to terminate the process. To support broad accessibility, the entire apparatus is made from low-cost, off-the-shelf electronic components, 3D-printed modules and open-source systems, for a total cost of about $600 each.

The authors tested the device with 48 participants, 37 of whom had little or no prior laboratory experience. Compared with paper instructions, the device helped users pool mock clinical samples with high accuracy, reduced uncorrected handling errors, improved volume-transfer skills and produced high-quality pools.

According to the authors, such tools could make pooled NAATs logistically and financially possible for large-scale surveillance programs, including programs that must estimate low disease prevalence and inform decisions about when mass drug administration can be stopped.

Publication details

A train-and-assist device that upskills novices to strengthen the workforce and expand diagnostic access, PNAS Nexus (2026). doi.org/10.1093/pnasnexus/pgag211

Journal information: PNAS Nexus

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Laboratory medicineInfectious diseases Provided by PNAS Nexus Who's behind this story?

Gaby Clark

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