Machine Learning and IoT for Predicting the Productivity of MRI Equipment

The rapid evolution and widespread accessibility of non-invasive medical imaging technologies, exemplified by Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT), are fundamentally reshaping medical decisionmaking paradigms. These sophisticated imaging modalities, capable of extracting high-definition medical images, have emerged as integral components of modern healthcare, facilitating precise diagnostics and treatment planning. The escalating adoption of such technologies, however, has accentuated the need for a nuanced understanding and optimization of the performance and productivity of both medical equipment and the teams operating them, mainly due to the high costs and risks caused by their misuse. This work proposes using univariate analytical models to estimate the number of exams performed per day with machine learning algorithms. For such, different energy-related sensors monitoring 25 magnetic resonance equipment from three different brands were considered. The results of the research reveal a compelling validation of the proposed approach. A notably high Pearson correlation coefficient is observed between the predictions generated by the evaluated models and the real measurements obtained through the Radiology Information System (RIS). This robust correlation emphasizes the accuracy and reliability of the estimation models, validating their potential applicability in real-world healthcare scenarios. Furthermore, the study unveils an intriguing trend that distinguishes the performance of electric current sensors. Thirteen out of the 25 evaluated MRI machines demonstrate superior results when equipped with electric current sensors compared to other sensor types. This nuanced insight not only substantiates the critical role of energy-related sensors in predicting equipment performance but also underscores the importance of tailoring monitoring strategies to the unique characteristics of each machine.