Abstract Pediatric and neonate incubators today are more sophisticated than the first prototypes but basic functions remained the same. They provide controlled environmental conditions needed to treat prematurely born infants who are not able to endure all the conditions outside the womb or infants born with certain diseases or health conditions.

Abstract Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare. To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance. The results of this study demonstrate that clinical engineering and health technology management benefit from application of machine learning in terms of cost optimization and medical device management. Automated systems, based on machine learning algorithms, can predict defibrillator performance with high accuracy. Systems based on Random Forest classifier with Genetic Algorithm feature selection yielded highest accuracy among other machine learning systems. Adoption of such systems will help in overcoming challenges of adapting maintenance and medical device supervision mechanism protocols to rapid technological development of these devices. Due to increased complexity of healthcare institution environment and increased technological complexity of medical devices, performing maintenance strategies in traditional manner is causing a lot of difficulties.

Aim To develop an online biofilm calculation tool (Biofilm Classifier), which calculates the optical density cut off value and accordingly determines the biofilm forming categories for the tested isolates by standardized formulas, as well as to compare the results obtained by Biofilm Classifier to manual calculations and the use of predefined values. Methods The biofilm forming capacity of tested strains was evaluated using tissue culture plate method in 96 well plates, and optical density (OD) value of the formed biofilm was measured on an ELISA Microplate reader at 595 nm on a total of 551 bacterial isolates from clinical specimen. Results Comparative analysis indicated that the manual calculation was 100% in accordance with results obtained by the designed software as opposed to the results obtained by use of predefined values for biofilm categorization. When using predefined values compared to manual biofilm categorization for the determination of biofilm positive and biofilm negative strains the specificity was 100%, sensitivity 97.81%, positive predictive value 100%, negative predictive value 96.04% and accuracy 98.57%. Conclusion Considering obtained results, the use of the designed online calculator would simplify the interpretation of biofilm forming capacity of bacteria using tissue culture plate method.

Aim To develop an online biofilm calculation tool (Biofilm Classifier), which calculates the optical density cut off value and accordingly determines the biofilm forming categories for the tested isolates by standardized formulas, as well as to compare the results obtained by Biofilm Classifier to manual calculations and the use of predefined values. Methods The biofilm forming capacity of tested strains was evaluated using tissue culture plate method in 96 well plates, and optical density (OD) value of the formed biofilm was measured on an ELISA Microplate reader at 595 nm on a total of 551 bacterial isolates from clinical specimen. Results Comparative analysis indicated that the manual calculation was 100% in accordance with results obtained by the designed software as opposed to the results obtained by use of predefined values for biofilm categorization. When using predefined values compared to manual biofilm categorization for the determination of biofilm positive and biofilm negative strains the specificity was 100%, sensitivity 97.81%, positive predictive value 100%, negative predictive value 96.04% and accuracy 98.57%. Conclusion Considering obtained results, the use of the designed online calculator would simplify the interpretation of biofilm forming capacity of bacteria using tissue culture plate method.