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: 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: 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: 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: 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 r ² 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.