Background Digital health technologies hold promise to enhance patient-related outcomes, to support health care staff by reducing their workload, and to improve the coordination of care. As key users of digital health technologies, health care workers are crucial to enable a meaningful digital transformation of health care. Digital health literacy and digital skills should become prerequisite competencies for health professionals to facilitate the implementation and leverage the potential of digital technologies to improve health. Objective We aimed to assess European medical students’ perceived knowledge and opinions toward digital health, the status of digital health implementation in medical education, and the students’ most pressing needs. Methods The explanatory design of our mixed methods study was based on an online, anonymous, self-administered survey targeted toward European medical students. A linear regression analysis was used to identify the influence of the year of medical studies on the responses. Additional analysis was performed by grouping the responses by the self-evaluated frequency of eHealth technology use. Written responses to four qualitative questions in the survey were analyzed using an inductive approach. Results The survey received a total of 451 responses from 39 European countries, and there were respondents for every year of medical studies. The majority of respondents saw advantages in the use of digital health. While 40.6% (183/451) felt prepared to work in a digitized health care system, more than half (240/451, 53.2%) evaluated their eHealth skills as poor or very poor. Medical students considered lack of education to be the reason for this, with 84.9% (383/451) agreeing or strongly agreeing that more digital health education should be implemented in the medical curriculum. Students demanded introductory and specific eHealth courses covering data management, ethical aspects, legal frameworks, research and entrepreneurial opportunities, role in public health and health systems, communication skills, and practical training. The emphasis lay on tailoring learning to future job requirements and interprofessional education. Conclusions This study shows a lack of digital health-related formats in medical education and a perceived lack of digital health literacy among European medical students. Our findings indicate a gap between the willingness of medical students to take an active role by becoming key players in the digital transformation of health care and the education that they receive through their faculties.

BACKGROUND Digital health technologies promise to enhance patient-related outcomes, to support the healthcare staff by reducing their workload and improve the coordination of care. As key users of digital health technologies, healthcare workers are crucial to enable a meaningful digital transformation of healthcare. Digital health literacy and digital skills are to become prerequisite competencies for health professionals to facilitate the implementation and leverage the potential of digital technologies to improve health. OBJECTIVE We aimed to assess European medical students’ perceived knowledge and opinions towards digital health, the status of digital health implementation in medical education, and the students’ most pressing needs. METHODS The explanatory design of our mixed-methods study was based on an online, anonymous, self-administered survey targeted towards European medical students. The quantitative analysis was performed using R statistical language; qualitative data was analyzed applying an inductive categorization approach using MaxQDA 2020 software. RESULTS The survey received a total of 451 responses from 39 European countries and all years of medical studies. The majority of respondents saw advantages in the use of digital health. More than half (53%) evaluated their eHealth skills as poor or very poor and 40% felt prepared to work in a digitized healthcare system. Medical students considered the reason for this a lack of education, with 85 % agreeing or strongly agreeing that digital health education should be more implemented in the medical curriculum. Students demanded introductory and specific eHealth courses covering data management, ethical aspects, legal frameworks, research and entrepreneurial opportunities,, its role in public health and health systems, communication skills, and practical training with eHealth technologies. The emphasis lay on tailoring learning to future job requirements and interprofessional education. CONCLUSIONS This study shows a lack of digital health-related formats in medical education and a perceived lack of digital (health) literacy among European medical students. Our findings indicate a gap between the willingness of medical students to take an active role by becoming key players in the digital transformation of healthcare, and the education they receive through their faculties. CLINICALTRIAL

Aims This study sought to examine the feasibility, accuracy and reproducibility of a novel, fully automated 2D transthoracic echocardiography (2D TTE) parasternal long axis (PLAX) view aortic measurements quantification software compared to board‐certified cardiologists in controlled clinical setting. Methods and results Aortic Annulus (AoA), Aortic Sinus (AoS), Sinotubular Junction (STJ) and Proximal Ascending Aorta (AAo) diameter measurements were performed retrospectively on each of 58 subjects in two different ways: twice using a fully automated software (Ligence Heart version 2) and twice manually by three cardiologists (ORG) and one expert cardiologist (EC). Out of 58 studies AoA was measured in 54 (93%), AoS in 55 (95%), STJ in 55 (95%) and AAo in 54 (93%) studies. Automated measurements had a stronger correlation with EC when compared to ORG with the largest correlation difference of .1 for STJ measurements and lowest difference of .01 for AoS measurements. Automated software was in higher agreement with ground truth intervals (ORG measurements mean +‐ SEM) in three out of four measurements. Conclusion Fully automated 2D TTE PLAX view aortic measurements using a novel AI‐based quantification software are feasible and yield results that are in close agreement with what experienced readers measure manually while providing better reproducibility. This approach may prove to have important clinical implications in the automation of the aortic root and ascending aorta assessment to improve workflow efficiency.

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

INTRODUCTION Deep learning (DL) has been of increasing use in the field of echocardiographic cardiology. The importance of segmentation and recognition of different heart chambers was already presented in different studies. However, there are no studies made regarding the functional heart measurements. Even though, functional measurements of right ventricle (RV) remains "dark side of the moon", no doubtfully severity of RV dysfunction influences the worse outcomes. PURPOSE To evaluate DL for recognition of geometrical features of RV and measurement of RV fractional area change (FAC). METHODS A total of 896 end-systolic and end-diastolic frames from 129 patients (with various indications for the study) were used to train and validate the neural networks. Raw pixel data was extracted from EPIQ 7G (Philips) imaging platform. All of the images were from 2D echocardiography apical four chamber views. RV was annotated in each image, with 1716 images used for training and 180 for validation. We used the state of art mask regional convolutional neural network (MR-CNN) and attention U-net convolutional neural network models for the RV segmentation task. Intersection over Union (IoU) was used as the primary metric for model evaluation. IoU measures the number of pixels common between the target and the prediction masks divided by the total number of pixels present across both masks. Additionally FAC was calculated using frames with minimal and maximal segmented area by the network. RESULTS U-Net architecture demonstrated considerably faster training compared to MR-CNN with time per training step of 85 ms and 750 ms for U-Net and MR-CNN, respectively (p < 0.001). MR-CNN and U-Net had an IoU of 0.91 and 0.89 respectively on validation dataset which corresponds to good performance of the model and there was no significant difference between the different neural networks (p = 0.876). Comparing the evaluation of FAC by physician and U-Net the mean squared difference was 12% when using minimum and maximum right ventricle area detected by the network. CONCLUSION With small dataset deep learning give us ability to recognize RV and measure RV FAC in apical four chamber view with high accuracy. This method offers assessment of RV to become daily used in the cardiologist practice, moreover, in the near future automated measurements will allow to reduce the need of observer manual evaluation. Improvements can be made in FAC calculation by also improving techniques for end-systolic and end-diastolic frame detection.