This paper presents the results of development of Artificial Neural Networks (ANNs) for prediction of medical device performance based on conformity assessment data. Conformity assessment data of medical devices was obtained from periodical inspections conducted by ISO 17020 accredited laboratory during 2015–2019 period. For the development of ANNs, 1738 samples of conformity assessment of infusion and perfusor pumps was used. Out of the overall number of samples, 1391 (80%) of them were used during system development and 346 (20%) samples were used for subsequent validation of system performance. During system development, the impact on overall system accuracy of different number of neurons in hidden layer and the activation functions was tested. Also, two neural network architectures were tested: feedforward and feedback. The results show that feedforward neural network architecture with 10 neurons in single hidden layer has the best performance. The overall accuracy of that neural network is 98.06% for performance prediction of perfusor pumps and 98.83% for performance prediction of infusion pumps. The recurrent neural network resulted in accuracy of 98.41% for both infusion pumps and perfusor pumps. The results show that conformity assessment data obtained through yearly inspections of medical devices can successfully be used for prediction of performance of single medical device. This is very important in increasing the safety and accuracy of diagnosis and treatment of patients.

Glucose is a main source of energy in human body and its regulation is controlled by a biological mechanism with organ/cell interactions that are related to glucose-insulin dynamics. This paper presents the model of physiological behaviors of glucose-insulin regulatory mechanism. This model allows investigation of blood glucose dynamics dependency on food intake. The model presented in this paper discusses several parameters within this complex system.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by major social, communication and behavioural challenges. The cause of ASD is still unclear and it is assumed that environmental, genetic and epigenetic factors influence the risk of ASD occurrence. MicroRNAs (miRNAs) are short 21–25 nucleotide long RNA molecules which post-transcriptionally regulate gene expression. MiRNAs play an important role in central nervous system development; therefore, dysregulation of miRNAs is connected to changes in behaviour and cognition observed in many disorders including ASD. Based on previously published work, on diagnosing ASD using miRNAs, we hypothesized that miRNAs can be used as biomarkers in children with suspected developmental disorders (DD) including ASD within Bosnian-Herzegovinian (B&H) population. 14 selected miRNAs were tested on saliva of children with suspected developmental disorders including ASD. The method of choice was qRT-PCR as a relatively cheap method available in most diagnostic laboratories in low to mid-income countries (LMIC). Out of 14 analysed miRNAs, 6 were differentially expressed between typically developing children and children with some type of developmental disorder including autism spectrum disorder. Using the most optimal logistic regression, we were able to distinguish between ASD and typically developing (TD) children. We have found 5 miRNAs as potential biomarkers. From those, 3 were differentially expressed within the ASD cohort. All 5 miRNAs had shown good chi-square statistics within the logistic regression performed on all 14 analysed miRNAs. The accuracy of 5-miRNAs model training set was 90.2%, while the validation set had a 90% accuracy. This study has shown that miRNAs may be considered as biomarkers for ASD detection and may be used to identify children with ASD along with standard developmental screening tests. By combining these methods we may be able to reach a reliable and accessible diagnostic model for children with ASD in LMIC such as B&H.

Due to the development of information communication technologies (ICT), the number of medical devices (MDs) with telemetric possibilities is rising, so the concept of homecare is gaining importance. Also, new generation medical devices are equipped with artificial intelligence that is able to perform real-time analysis of measurement result and provide diagnosis prediction. This is the Industry 4.0 happening now. However, there is still traditional approach in management of medical devices. As medical devices have been sophisticated, management systems should improve so they can encompass all the important aspects regarding safety of patients and quality of care. This chapter presents how the technology of Industry 4.0 can be used to improve medical device maintenance systems by application of artificial intelligence (AI). Clinical engineering and health technology management departments benefit from such systems in terms of increase of safety and quality of patient diagnosis and treatments, and cost optimization in medical device management.