The relationship between single nucleotide polymorphisms (SNPs) and phenotypes is noisy and cryptic due to the abundance of genetic factors and the influence of environmental factors on complex traits, which makes the idea of applying artificial neural networks (ANNs) as universal approximates of complex functions promising. In this study, we compared different ANN architectures and input parameters to predict the adult length of Pacific lampreys, which is the primary indicator of their total migratory distance. Feedforward and simple recurrent network architectures with a different range of input parameters and different sizes of hidden layers were compared. Results indicate that the highest performing ANN had an accuracy of 67.5% in discriminating between long and short specimens. Sensitivity and specificity were 62.16% and 70.73%, respectively. Our results imply that feedforward ANN architecture with a single hidden neuron is enough to solve the problem of specimen classification. Nonetheless, while ANNs are useful at approximating functions with unknown relationships in the case of SNP data, additional work needs to be performed to ensure that the chosen SNP markers are related to functional regions related to the examined trait, as the use of non-specific markers will result in the introduction of noise into the dataset.

This paper presents a system for classification of asthma based on artificial neural network. A total of 1800 Medical Reports were used for neural network training. The system was subsequently tested through the use of 1250 Medical Reports established by physicians from hospital Sarajevo. Out of the aforementioned Medical Reports, 728 were diagnoses of asthma, while 522 were healthy subjects. Out of the 728 asthmatics, 97.11% were correctly classified, and the healthy subjects were classified with an accuracy of 98.85%. Sensitivity and specificity were assessed, as well, which were 97.11% and 98.85%, respectively. Our system for classification of asthma is based on a combination of spirometry (SPIR) and Impulse Oscillometry System (IOS) test results, whose measurement results were inputs to artificial neural network. Artificial neural network is implemented to obtain both static and dynamic assessment of the patient's respiratory system.

Medical devices with measuring functions play a crucial role in diagnostics and treatment of patients is health care establishments. Both the testing and manufacturing guidelines laid out in the medical devices directive MDD 93/42, as well as the recommendations for electrical medical equipment in IEC 60601 and IEC 62353 are largely not implemented. The majority of the guidelines that are being followed are those relating to the manufacturing and selling (as well as obtaining of the CE mark), while the precision and accuracy of medical devices in the subsequent period of use are not sufficiently monitored. The manufacturers, distributors and servicing companies do provide yearly service to those contracted establishments. However, while the aforementioned service includes part replacement, it does not include measurements of output parameters which are of vital interest to the patients. Part of the reason for the lack of focus on exit parameters by the servicing companies is that they do not possess the necessary equipment needed to perform said tasks, thereby compromising the entire process of preventative maintenance. Due to the aforementioned, in order to regulate this domain, state agencies and the affiliated measurement laboratories ought to perform independent inspection (verification) of medical devices with measurement functions according to ISO 17020 standards. This would effectively abolish the lobby and monopoly that manufacturing companies hold over medical device repair and thereby not only lower the cost due to unnecessary servicing, but it would ensure an increase in medical device reliability and safety and ultimately patient diagnosis and treatment.

This paper presents the results of a study developing expert system to support stress recognition based on Artificial Neural Network (ANN). Developed ANN is trained using data from Physionet database and collected data from other researchers. The implemented system for stress recognition uses drivers ECG signal, Galvanic Skin Response and Respiration Rate as parameters. Developed neural network was validated with 77 samples. Samples are obtained from subjects using Pasco sensors in 7D cinemas. Out of 77 samples, in 71% of subjects higher level of stress is recognized, while 29% of subjects are classified as subjects with normal vital functions. An accuracy of 99% and specificity of 98% is obtained.

This paper presents the results of a study developing artificial neural network system (ANN) for classification of Alzheimer's disease (AD) and healthy patients. The classification is done using biomarkers, from cerebrospinal fluid: albumin ratio (CSF/Serum and/or Plasma), Aβ40 (CSF), Aβ42 (CSF), tau-total (CSF) and tau-phospho (CSF). Neural network input parameters are datasets from Alzbiomarkers database. Independent t-test is used to calculate statistical difference between input parameters. Developed neural network was validated with 80 subjects from Alzbiomarkers database. Out of 45 AD subjects, 43 were correctly classified as AD patients, obtaining a sensitivity of 95.5%, and out of 35 healthy subjects 32 were correctly classified obtaining specificity of 91.43%.

This paper presents implemented artificial neural network (ANN) for diagnosing pulmonary tuberculosis progression and dynamics. Tuberculosis is an infectious disease caused in most cases by microorganism, called Mycobacterium tuberculosis. Tuberculosis is a huge problem in most low-income countries, and also in the Balkan region. The design of the artificial neural network is based on two strains of tuberculosis bacteria and multiple strains of tuberculosis. Training data sets contain 1000 reports for this artificial neural, 800 of them are used for estimation and 200 for validation. The ANN system is validated on 1400 patients from the Clinical Centre University of Sarajevo in the two years period. Out of 1315 patients, 99.24% are correctly classified as tuberculosis related patients. System was 100% successful on 85 patients were diagnosed with normal lung function. Sensitivity of 99.24% and specificity of 100% in tuberculosis classification are obtained. Our artificial neural network is a promising method for predicting diagnosis and possible treatment routine for tuberculosis disease.

This paper presents a system for classification of asthma based on fuzzy rules. Fuzzy rules are defined according to Global Initiative for Asthma (GINA) guidelines, as well as through consultations with long-term experience of pulmologists. Our fuzzy system for classification of asthma is based on a combination of spirometry (SPIR) and Impulse Oscillometry System (IOS) test results, which are inputs to fuzzy system. Additionally, the use of bronchodilatation and bronhoprovocation enabled a complete patient's dynamic assessment rather than a simple static assessment. The system was retroactively tested with 1250 Medical Reports established by pulmologists, out of which 728 were diagnosed with asthma and 522 were healthy subjects. Sensitivity and specificity were assessed, on this dataset, which were 91.89% and 95.01%, respectively.

In respect with growing population, chronical disease management and aging population, the medicine and health care have drastically changed over the past decades. Engineering became more involved in medicine resulting in development of new discipline, Biomedical Engineering. As new engineering solution for problems in medicine regarding therapy, diagnosis and treatment have emerged, the need for new interdisciplinary educational curriculums evolved. Today, South East Countries are focused on developing new educational curriculums in Biomedical Engineering, following the models established in other European and United States Countries, and recognizing the new discipline - Biomedical Engineering as a professional discipline. International Federation for Medical and Biological Engineering, supported by United Nations (UN), gives support for national societies that are focused on Biomedical Engineering. In this article, overview of development and perspectives of Biomedical Engineering in South East European (SEE) countries is given, with focus on Bosnia and Herzegovina.

This chapter presents a system for classification of chronic obstructive pulmonary disease (COPD) based on fuzzy rules and a trained neural network. Fuzzy rules and neural network parameters are defined according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. The system was subsequently validated in 285 patients by physicians at the Clinical Centre University of Sarajevo. Out of the investigated patients, 99.19% of the 248 with COPD and all of the 37 individuals with normal lung function were classified correctly. Obtained sensitivity (99.3%) and specificity (100%) in COPD were assessed, as well. Implemented neuro-fuzzy system for classification of COPD is based on a combination of spirometry and Impulse Oscillometry System (IOS) test results, which enables more accurate classification of the disease. Additionally, a complete patient’s dynamic assessment can be obtained rather than a mere static assessment through the use of bronchodilatation and bronchoprovocation.

This paper presents software package solution for tracking the status of Medical Devices inspection dates in Inspection Laboratory. Software package is implemented by the Engineers from Medical Device Inspection Laboratory, Verlab, as necessary tool for tracking dates and results of inspection of Medical Devices in legal Metrology from 3 Clinical Center's, 26 Hospitals, 63 Primary Care Units and more than 350 Private Clinics in Bosnia and Herzegovina (BH). This software is offering possibility to check and download certificates, measuring reports, working reports and calculated errors for each Medical Device in each healthcare institution. The software is working online and access is possible through Verlab's web site. Inspection of Medical Devices in BH is defined by the National Metrology Institute of BH based on directive MDD 93/42/EEC, ISO 60601 and more than 20 different manufacturer's user and service manuals. Software is used for more than three months and has 64 active users. Software's data base has 500 Medical Devices registered with all relevant documents. Based on active clients and uploaded Medical Devices inspection reports the software proved as good solution in respect of improving efficiency and as a clear data base of owned Medical Devices for healthcare institutions.

In addition to knowledge and experience of medical doctors, correct diagnosis and appropriate patient treatment largely depend on accuracy and functionality of medical devices. In a large number of serious medical situations proper functionality of medical devices is crucial for patients. Therefore it is necessary to carry out as strict and independent testing of functionalities of medical devices as possible and to obtain the most accurate and reliable diagnosis and patient treatment. This paper presents the results of study conducted by the Institute of Metrology of Bosnia and Herzegovina (IMBIH) that highlight the necessity of introducing metrology into medicine and defining standard regulations for inspections of medical devices. As it has been previously done for other kinds of devices that are under jurisdiction of the Institute of Metrology of BH, this research provides a foundation for the introduction of medical devices into the legal metrology system with precisely defined units of measurement, their ranges and errors. The study was based upon data collected through three clinical centers, 25 hospitals, 63 health centers and 320 private health institutions in BH over the course of one year. As a result of this study, the medical devices that have been introduced into the legal metrology system in BH include ECG devices, defibrillators, patient monitors, respirators, anesthesia machines, dialysis machines, pediatric and neonatal incubators, therapeutic ultrasounds, infusion pumps and perfusors. Furthermore, standard inspection regulations for the aforementioned medical devices are also defined. Additionally, a national laboratory for the inspection of medical devices was established and it currently operates under the ISO 17020 standard. With the introduction of medical devices into the legal metrology system and with the establishment of a fully operational national laboratory for inspection of medical devices, we expect that the reliability of medical devices in diagnosis and patient care will increase and that the costs of the health care system in BH will be reduced.