Near-infrared spectroscopy and machine learning algorithms for rapid and non-invasive detection of Trichuris

BackgroundTrichuris trichiura (whipworm) is one of the most prevalent soil transmitted helminths (STH) affecting 604-795 million people worldwide. Diagnostic tools that are affordable and rapid are required for detecting STH. Here, we assessed the performance of the near-infrared spectroscopy (NIRS) technique coupled with machine learning algorithms to detect Trichuris muris in faecal, blood, serum samples and non-invasively through the skin of mice.MethodologyWe orally infected 10 mice with 30 T. muris eggs (low dose group), 10 mice with 200 eggs (high dose group) and 10 mice were used as the control group. Using the NIRS technique, we scanned faecal, serum, whole blood samples and mice non-invasively through their skin over a period of 6 weeks post infection. Using artificial neural networks (ANN) and spectra of faecal, serum, blood and non-invasive scans from one experiment, we developed 4 algorithms to differentiate infected from uninfected mice. These models were validated on mice from the second independent experiment.Principal findingsNIRS and ANN differentiated mice into the three groups as early as 2 weeks post infection regardless of the sample used. These results correlated with those from concomitant serological and parasitological investigations.SignificanceTo our knowledge, this is the first study to demonstrate the potential of NIRS as a diagnostic tool for human STH infections. The technique could be further developed for large scale surveillance of soil transmitted helminths in human populations. The existing diagnostic tools for STH infections can be time consuming, expensive, less sensitive and some require trained personnel. These techniques are therefore not feasible for large scale programmatic surveillance. Novel surveillance tools that can be easily scaled up to guide mass drug administration (MDA) for communities at risk remain a priority. This study investigated the role of NIRS as a potential large scale surveillance tool for soil transmitted helminths using Trichiuris muris experimental mouse model. The technique is environmentally friendly because it neither requires reagents nor sample processing procedures to operate and it only takes 5-10 seconds to analyse a sample allowing thousands of samples to be assessed in a day by unskilled personnel. Our results demonstrate for the first time that infrared light coupled with machine learning has the potential to be developed into a large-scale surveillance tool for STH infections to assess impact of interventions and guide future elimination efforts.