The Impact of Health Education on the Quality of Life of Patients Hospitalized in Forensic Psychiatry Wards.

PURPOSE: An original health education program, developed for a group of patients of forensic psychiatry wards, was the basis for conducting a study on the impact of educational influences on the quality of life of patients long-term isolated from their natural environment. The main aim of the study was to answer the question: Does health education affect the quality of life of patients in forensic psychiatry wards and is educational activity effective? METHODS: The study was conducted at the State Hospital for Mental and Nervous Diseases in Rybnik, Poland, in the forensic psychiatry wards, and lasted from December 2019 to May 2020. During the study, patients gained knowledge in the field of broadly understood health education. The study group consisted of 67 men, aged 22-73, diagnosed with schizophrenia. The method of double measurements (before and after the health education cycle) was applied, using the WHOQOL-BREF scale of quality of life and the first author's questionnaire of patients' knowledge, from the educational program used. RESULTS: Health education does not significantly affect the overall quality of life of patients staying in forensic psychiatry wards, but it does affect their somatic condition. The proprietary health education program is effective because the patients' knowledge has significantly improved. CONCLUSIONS: The quality of life of interned patients with schizophrenia is not significantly related to educational activities, however, psychiatric rehabilitation through educational activities effectively increases the level of patients' knowledge.

Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle.

We consider parameter inference in cardio-mechanic models of the left ventricle, in particular the one based on the Holtzapfel-Ogden (HO) constitutive law, using clinical in vivo data. The equations underlying these models do not admit closed form solutions and hence need to be solved numerically. These numerical procedures are computationally expensive making computational run times associated with numerical optimisation or sampling excessive for the uptake of the models in the clinical practice. To address this issue, we adopt the framework of Bayesian optimisation (BO), which is an efficient statistical technique of global optimisation. BO seeks the optimum of an unknown black-box function by sequentially training a statistical surrogate-model and using it to select the next query point by leveraging the associated exploration-exploitation trade-off. To guarantee that the estimates based on the in vivo data are realistic also for high-pressures, unobservable in vivo, we include a penalty term based on a previously published empirical law developed using ex vivo data. Two case studies based on real data demonstrate that the proposed BO procedure outperforms the state-of-the-art inference algorithm for the HO constitutive law.

Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics.

Combining biomechanical modelling of left ventricular (LV) function and dysfunction with cardiac magnetic resonance (CMR) imaging has the potential to improve the prognosis of patient-specific cardiovascular disease risks. Biomechanical studies of LV function in three dimensions usually rely on a computerized representation of the LV geometry based on finite element discretization, which is essential for numerically simulating in vivo cardiac dynamics. Detailed knowledge of the LV geometry is also relevant for various other clinical applications, such as assessing the LV cavity volume and wall thickness. Accurately and automatically reconstructing personalized LV geometries from conventional CMR images with minimal manual intervention is still a challenging task, which is a pre-requisite for any subsequent automated biomechanical analysis. We propose a deep learning-based automatic pipeline for predicting the three-dimensional LV geometry directly from routinely-available CMR cine images, without the need to manually annotate the ventricular wall. Our framework takes advantage of a low-dimensional representation of the high-dimensional LV geometry based on principal component analysis. We analyze how the inference of myocardial passive stiffness is affected by using our automatically generated LV geometries instead of manually generated ones. These insights will inform the development of statistical emulators of LV dynamics to avoid computationally expensive biomechanical simulations. Our proposed framework enables accurate LV geometry reconstruction, outperforming previous approaches by delivering a reconstruction error 50% lower than reported in the literature. We further demonstrate that for a nonlinear cardiac mechanics model, using our reconstructed LV geometries instead of manually extracted ones only moderately affects the inference of passive myocardial stiffness described by an anisotropic hyperelastic constitutive law. The developed methodological framework has the potential to make an important step towards personalized medicine by eliminating the need for time consuming and costly manual operations. In addition, our method automatically maps the CMR scan into a low-dimensional representation of the LV geometry, which constitutes an important stepping stone towards the development of an LV geometry-heterogeneous emulator.