TY - JOUR A1 - Pfeil, Juliane A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Schulze, Katja T1 - Examination of blood samples using deep learning and mobile microscopy JF - BMC Bioinformatics N2 - Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15802 SN - 1471-2105 VL - 23 PB - BioMed Central ER - TY - JOUR A1 - Pfeil, Juliane A1 - Siptroth, Julienne A1 - Pospisil, Heike A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Moskalenko, Olga A1 - Yateem, Murad A1 - Nechyporenko, Alina T1 - Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition JF - Big Data and Cognitive Computing N2 - Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera. KW - human intestinal microbiome KW - next-generation sequencing KW - type 2 diabetes KW - deep learning KW - image classification Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17184 SN - 2504-2289 VL - 7 IS - 1 PB - MDPI ER -