Technologies for developing decision support systems for the diagnosis of blood disorders using convolutional neural networks

In this study, we analyzed technologies for obtaining, processing, segmentation, and transmitting of microphotographs for subsequent recognition. We selected the most promising machine learning algorithms optimal for the processing of medical images, investigated the technologies of analyzing medical texts, studied the aspects of using the Watson neural network for analyzing the semantics of medical images, as well as the aspect of using the unified medical language UMLS for the needs of syndromic diagnostics for the evaluation of medical texts from medical histories in natural language. We also developed an interface for receiving, processing, segmenting, and transmitting microphotographs to artificial neural networks and an interface for the primary accepting and processing of microphotographs based on the OMERO medical image processing platform. To send data online, a demo script for jupiter was prepared. An interface for transmitting medical texts to the medical text semantics recognition system was also developed. The IBM Watson Annotator for Clinical Data was used to perform preliminary analysis of medical texts. We created a database of medical images of the bone marrow for neural network training. We made 3,500 color microphotographs (600×400 pixels) of bone marrow smears at a resolution of ×600 (light microscopy; hematoxylin and eosin staining). We performed marking of 11 types of bone marrow cells. We created a database of medical texts (167 patients, 40,000 words) to prepare a neural network. The database was stripped of all personal identifiers.