BackgroundDialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment.ObjectivePrediction of dialysis machine performance status and errors using regression models.MethodThe methodology includes seven steps: data collection, processing, model selection, training, evaluation, fine-tuning, and prediction. After preprocessing 1034 measurements, twelve machine learning models were trained to predict dialysis machine performance, and temperature and conductivity error values.ResultsEach model was trained 100 times on different splits of the dataset (80% training, 10% testing, 10% evaluation). Logistic regression achieved the highest accuracy in predicting dialysis machine performance. For temperature predictions, Lasso regression had the lowest MSE on training data (0.0058), while Linear regression showed the highest R² (0.59). For conductivity predictions, Lasso regression provided the lowest MSE (0.134), with Decision tree achieving the highest R² (0.2036). SVM attained the lowest MSE on testing dataset, with 0.0055 for temperature and 0.1369 for conductivity.ConclusionThe results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automated systems for advanced management of dialysis machines.

BACKGROUND Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. As infant incubators are used as a form of fundamental healthcare support for the most sensitive population, prematurely born infants, special care mus be taken to ensure their proper functioning. This is done through a standardized process of post-market surveillance. OBJECTIVE To address the issue of faulty infant incubators being undetected and used between yearly post-market surveillance, an automated system based on machine learning was developed for prediction of infant incubator performance status. METHODS In total, 1997 samples were collected during the inspection process of infant incubator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) for the development of the automated system. RESULTS The aforementioned algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The 0.93 AUC of Naïve Bayes indicates that it is overall stronger in predictive capabilities than decision tree and random forest which displayed superior accuracy in comparison to Naïve Bayes. CONCLUSION The results of this study demonstrate that machine learning algorithms can be effectively used to predict infant incubator performance status on the basis of measurements taken during post-market surveillance. Adoption of these automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of infant incubators that are already being used in healthcare institutions.

BACKGROUND This paper describes pharmacoeconomic analysis of ethanol and benzalkonium chloride disinfectants used in dental institutions to prevent infections. Pathogens can be transmitted to patients via air, items, contact or vectors. The aim of this study is to compare the efficiency and cost-effectiveness of both disinfectants. METHODS For pharmacoeconomic assessment, cost minimization analysis, cost benefit analysis (CBA), cost effectiveness analysis and cost utility analysis were performed. The cost of disinfectants used in hand disinfection of dental professionals is estimated to be 50 times higher when using ethanol. Compared monthly costs for disinfectants in surface disinfection are 18 times higher when using ethanol. RESULTS Results of CBA imply 12 hours as annual time needed for performance of benzalkonium chloride disinfection, and 720 hours for ethanol. Reduction of pathogens on the examined surface after application of benzalkonium chloride was 99-99% for all tested pathogens. The application of the amount of benzalkonium chloride analogous to the cost of ethanol in dental facilities could eliminate the chance of nosocomial infections. CONCLUSIONS The cost-effectiveness of benzalkonium chloride leads to more agile recovery of the patient. Performed assessments lead to the conclusion that benzalkonium chloride is more efficient in dental facilities than ethanol. Utilization of benzalkonium chloride improves quality of life, significantly decreasing time spent for application and frequent reapplications of the disinfectant.

BACKGROUND After 25 years of implementing the Medical Devices Directive (MDD), in 2017, the new Medical Devices Regulation (MDR) came into force, establishing stricter requirements for post-market surveillance of the safety and performance of medical devices (MD). For electrocardiogram (ECG) devices, which are crucial for monitoring cardiac activities, these requirements are essential to ensure the reliability and accuracy of diagnosing cardiac conditions and timely treatment. OBJECTIVE This study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices. Specifically, the research focuses on classifying the success or failure of ECG device operations based on performance and safety parameters. The ultimate goal is to improve the management strategies of ECG devices in healthcare institutions, ensuring optimal functionality and increasing the reliability of diagnostic procedures. METHOD During the inspection process of ECG devices conducted by an accredited laboratory in accordance with ISO 17020 standard in numerous healthcare institutions in Bosnia and Herzegovina, a total of 5577 samples were collected. Various machine learning algorithms, including Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM), were employed for result comparison and selection of the most accurate algorithm. RESULTS All algorithms demonstrated good performance, but the Random Forest (RF) algorithm stood out, achieving 100% accuracy in predicting the success/unsuccess status of the device. While the results of this research are specific to the collected data from EKG devices, the developed algorithms can be applied to other similar datasets, offering opportunities for broader use in the medical environment. CONCLUSION Implementing machine learning algorithms for automated systems in healthcare institutions can significantly enhance the quality of patient diagnosis and treatment. Additionally, these systems can optimize costs associated with managing medical devices. Improved post-market surveillance using ML can address challenges related to ensuring device reliability and safety.

Background With the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being. Objective This study aims to explore machine learning techniques to enhance predictive maintenance for mechanical ventilators. Method: The dataset used for this study contains information about 1350 entries of mechanical ventilators, made by 15 different manufacturers and available in 30 distinct models. Different machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, K-nearest Neighbors, Support Vector Machines, Naive Bayes, and XG Boost are developed and tested in terms of their performance in predicting mechanical ventilator failures. Results The ensemble methods, particularly Random Forest and XGBoost, have proven to be more adept at handling the complexities of the dataset. The Decision Tree and Random Forest models both showed remarkable accuracies of approximately 0.993, while K-Nearest Neighbors (KNN) performed exceptionally with near perfect accuracy. Conclusion Adoption of automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of MDs that are already being used in healthcare institutions. Implementing machine learning-based predictive maintenance can significantly enhance the reliability of mechanical ventilators in healthcare settings.

Background Analysis of data from incident registries such as MAUDE has identified the need to improve surveillance and maintenance strategies for infusion pumps to enhance patient and healthcare staff safety. Objective The ultimate goal is to enhance infusion pump management strategies in healthcare facilities, thus transforming the current reactive approach to infusion pump management into a proactive and predictive one. Method: This study utilized real data collected from 2015 to 2021 through the inspection of infusion pumps in Bosnia and Herzegovina. Inspections were conducted by the national laboratory in accordance with the Legal Metrology Framework, accredited to ISO 17020 standard. Out of 988 samples, 790 were used for model training, while 198 samples were set aside for validation (20% of the dataset). Various machine learning algorithms for binary classification of samples (pass/fail status) were considered, including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machine. These algorithms were chosen for their ability to handle large datasets and potential for high prediction accuracy. Results Through detailed analysis of the achieved results, it was found that all applied machine learning methods yielded satisfactory results, with accuracy ranging from 0.98% to 1.0%, precision from 0.99% to 1%, sensitivity from 0.98% to 1.0%, and specificity from 0.87% to 1.0%. However, Decision Tree and Random Forest methods proved to be the best, both due to their maximum achieved values of accuracy, precision, sensitivity, and specificity, and due to result interpretability. Conclusion It has been established that machine learning methods are capable of identifying potential issues before they become critical, thus playing a crucial role in predicting the performance of infusion pumps, potentially enhancing the safety, reliability, and efficiency of healthcare delivery. Further research is needed to explore the potential application of machine learning algorithms in various healthcare domains and to address practical issues related to the implementation of these algorithms in real clinical settings.

Background Healthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients. Objective One way to address these issues is through the use of artificial intelligence for the detection of medical device failure. This goal of this study was to develop automated systems utilising machine learning algorithms to predict patient monitor performance and potential failures based on data collected during regular safety and performance inspections. Methods The system developed in this study utilised machine learning techniques as its core. Throughout the study four algorithms were utilised. These algorithms include Decision Tree, Random Forest, Linear Regression and Support Vector Machines. Results Final results showed that Random Forest algorithms had the best performance on various metrics among the four developed models. It achieved accuracy of 94% and precision and recall of 70% and 93% respectively. Conclusion This study shows that use of systems like the one developed in this study have the potential to improve management and maintenance of medical devices.

Background Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. After the MD directive (MDD) had been in force for 25 years, in 2017 the new MD Regulation (MDR) was introduced. One of the more stringent requirement is a need for better control of MD safety and performance post-market surveillance mechanisms. Objective To address this, we have developed an automated system for management of MDs, based on their safety and performance measurement parameters, that use machine learning algorithm as a core of its functioning. Methods In total, 1997 samples were collected during the inspection process of defibrillator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. This paper presents solution developed for defibrillators, but proposed system is scalable to any other type of MDs, both diagnostic and therapeutic. Results Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR). In addition, random forest regressor and XG Boost algorithms were tested for their predictive capabilities in the field of defibrillator output error prediction. These algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The highest accuracy achieved on this dataset was 94.8% using the Naive Bayes algorithm. The XGBoost Regressor with its r2 of 0.99 emerged as a powerful tool, showcasing exceptional predictive accuracy and the ability to capture a substantial portion of the dataset's variability. Conclusion The results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automatization of defibrillator maintenance scheduling in terms of increased safety and treatment of patients, on one side, and cost optimization in MD management departments, on the other side.

Neurological impairment disorders in fetuses, such as cerebral palsy, epilepsy, and autism spectrum disorder, can arise from numerous factors impacting the development of the fetal nervous system. Although diagnosing these disorders early is difficult, it is essential for prompt intervention. Recent progress in deep learning and ultrasound technology offers the potential to create a tool for early detection. Development of the TRUEAID system is based on combining the meticulously tuned Kurjak Antenatal Neurodevelopmental Test (KANET) with a sophisticated convolutional neural network for construction of an AI empowered ultrasound module capable of automated diagnostic decision support in the field of fetal neurodevelopmental risk assessment. The model's performance was evaluated using accuracy metrics, precision, sensitivity, specificity, F1 score, and Mathesson Correlation Coefficient (MCC). The custom CNN architecture achieved an overall accuracy of 93.83%. This pilot study lays the foundation for AI-based fetal neurobehavioral assessment, providing a promising tool for the early detection of fetal neurological impairment disorders. The research holds implications for improving outcomes for affected children and making advanced diagnostic capabilities accessible in diverse healthcare settings.