Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic quantification of disease infection. In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI models.

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A modernized device intended for diagnosing the temperature dependent activity of cells is described. The activity of cells was determined by finding the correlation coefficient of optical signals in speckle patterns recorded at different moments of time. It is shown that, as the temperature increases from 26 to 36°C, variations in the optical paths in cells increase from 16 to 26 nm. This is probably associated with the physiological activity of cells-trapping of large molecules or their conglomerates. In contrast to the pre vious variant of the device design, instead of a lipid membrane, a cell monolayer was used, which was culti vated on a plane-parallel glass plate. In order to reduce variations in the optical path lengths in a nutrient solution, a 1.7 mm thick plane-parallel glass plate was additionally inserted into the cuvette. The modern ization of the device also includes heating of the cuvette with hot air flows.

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