Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone.

Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research.

Finding temporal gene expression patterns for translational research.

Translational research of time-series of gene-expression microarray datasets makes use on gene expression profiles that have been obtained at different points in time. Our web-based multi-user program helps a researcher find temporal patterns like peaks in large pre-selected microarray data sets that include data from different but related studies in publicly available databases. If all studies use the same platform, data can be combined for a meta-analysis type approach. For combination of data from different platforms we allow only Affymetrix GeneChips, for which a method for pooling of information exists. To search for time patterns, the data are transformed into an abstract layer that is independent from the particular selection of time point in the individual studies.