Development of an Intelligent System for the determination of rupture-related characteristics in intracranial aneurysms detected by Computed Tomography Angiography

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Purpose: Purpose of this study is the development and evaluation of an intelligent decision support system that will accurately predict the risk of rupture in incidentally detected intracranial aneurysms. Patients-Methods: 100 patients with intracranial aneurysms (both ruptured and unruptured) detected by computed tomography angiography (CTA) and confirmed by invasive cerebral angiography (DSA) or clipping surgery were included. The intelligent decision support systems were developed based on measurements related to the geometry of the 100 aneurysms and their morphological characteristics, using conventional machine learning methods. Results: The intelligent systems based on WEKAs Neural Network, WEKAs J48 decision tree and in Clips (a free software tool for building expert systems) had overall accuracy 80.6%, 80%, 73% respectively. These accuracy rates resulted from the true-positive classification of almost all ruptured aneurysms, as well as the false-positive classification of the most unruptured. This false-positive classification of unruptured aneurysms correlated with a specific morphologic feature (irregular shape). Conclusion: The overall accuracy of the systems, deemed adequate. Their disability to correctly classify unruptured aneurysms reduces the overall success rates of all systems, however, this discrepancy associated to unruptured aneurysms of irregular shape, could well mean that such aneurysms are in higher risk of rupture and needs immediate treatment.

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In this paper, we present the application of a Machine Learning (ML) approach that generates predictions to support healthcare professionals to identify the outcome of patients through optimization of treatment strategies. Based on Decision Tree algorithms, our approach has been trained and tested by analyzing the severity and the outcomes of 346 COVID-19 patients, treated through the first two pandemics “waves” in a tertiary center in Western Greece. Its’ performance was achieved, analyzing entry features, as demographic characteristics, comorbidity details, imaging analysis, blood values, and essential hospitalization details, like patient transfers to Intensive Care Unit (ICU), medications, and manifestation responses at each treatment stage. Furthermore, it has provided a total high prediction performance (97%) and translated the ML analysis to clinical managing decisions and suggestions for healthcare institution performance and other epidemiological or postmortem approaches. Consequently, healthcare decisions could be more accurately figured and predicted, towards better management of the fast-growing patient subpopulations, giving more time for the effective pharmaceutical or vaccine armamentarium that the medical, scientific community will produce.KeywordsCOVID-19Clinical decision makingMachine learningPatient management
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