Background: Patient education is crucial in the secondary prevention of cardiovascular disease. Novel technologies such as augmented reality or mixed reality expand the possibilities for providing visual support in this process. Mixed reality creates interactive digital three-dimensional (3D) projections overlaying virtual objects on the real-world environment. While augmented reality only overlays objects, mixed reality not just overlays but anchors virtual objects to the real world. However, research on this technology in the patient domain is scarce. Objective: The aim of this study was to understand how patients perceive information provided after myocardial infarction and examine if mixed reality can be supportive in this process. Methods: In total, 12 patients that experienced myocardial infarction and 6 health care professionals were enrolled in the study. Clinical, demographic, and qualitative data were obtained through semistructured interviews, with a main focus on patient experiences within the hospital and the knowledge they gained about their disease. These data were then used to map a susceptible timeframe to identify how mixed reality can contribute to patient information and education. Results: Knowledge transfer after myocardial infarction was perceived by patients as too extensive, not personal, and inconsistent. Notably, knowledge on anatomy and medication was minimal and was not recognized as crucial by patients, whereas professionals stated the opposite. Patient journey analysis indicated the following four critical phases of knowledge transfer: at hospital discharge, at the first outpatient visit, during rehabilitation, and during all follow-up outpatient visits. Important patient goals were understanding the event in relation to daily life and its implications on resuming daily life. During follow-up, understanding physical limitations and coping with the condition and medication side effects in daily life emerged as the most important patient goals. The professionals’ goals were to improve recovery, enhance medication adherence, and offer coping support. Conclusions: There is a remarkable difference between patients’ and professionals’ goals regarding information and education after myocardial infarction. Mixed reality may be a practical tool to unite perspectives of patients and professionals on the disease in a more even manner, and thus optimize knowledge transfer after myocardial infarction. Improving medication knowledge seems to be a feasible target for mixed reality. However, further research is needed to create durable methods for education on medication through mixed reality interventions.

BACKGROUND Patient education is crucial in the secondary prevention of cardiovascular disease. Novel technologies such as augmented reality or mixed reality expand the possibilities for providing visual support in this process. Mixed reality creates interactive digital three-dimensional (3D) projections overlaying virtual objects on the real-world environment. While augmented reality only overlays objects, mixed reality not just overlays but anchors virtual objects to the real world. However, research on this technology in the patient domain is scarce. OBJECTIVE The aim of this study was to understand how patients perceive information provided after myocardial infarction and examine if mixed reality can be supportive in this process. METHODS In total, 12 patients that experienced myocardial infarction and 6 health care professionals were enrolled in the study. Clinical, demographic, and qualitative data were obtained through semistructured interviews, with a main focus on patient experiences within the hospital and the knowledge they gained about their disease. These data were then used to map a susceptible timeframe to identify how mixed reality can contribute to patient information and education. RESULTS Knowledge transfer after myocardial infarction was perceived by patients as too extensive, not personal, and inconsistent. Notably, knowledge on anatomy and medication was minimal and was not recognized as crucial by patients, whereas professionals stated the opposite. Patient journey analysis indicated the following four critical phases of knowledge transfer: at hospital discharge, at the first outpatient visit, during rehabilitation, and during all follow-up outpatient visits. Important patient goals were understanding the event in relation to daily life and its implications on resuming daily life. During follow-up, understanding physical limitations and coping with the condition and medication side effects in daily life emerged as the most important patient goals. The professionals’ goals were to improve recovery, enhance medication adherence, and offer coping support. CONCLUSIONS There is a remarkable difference between patients’ and professionals’ goals regarding information and education after myocardial infarction. Mixed reality may be a practical tool to unite perspectives of patients and professionals on the disease in a more even manner, and thus optimize knowledge transfer after myocardial infarction. Improving medication knowledge seems to be a feasible target for mixed reality. However, further research is needed to create durable methods for education on medication through mixed reality interventions.

The morphology of blood vessels in retinal fundus images is a key biomarker for diagnosing conditions such as glaucoma, hypertension, and diabetic retinopathy. This study introduces a deep learning-based method for automatic blood vessel segmentation, trained from scratch on 44 clinician-annotated images. The proposed architecture integrates Bidirectional Long Short-Term Memory (Bi-LSTM) layers with dropout to mitigate overfitting. A distinguishing feature of this approach is the column-wise processing, which improves feature extraction and segmentation accuracy. Additionally, a custom data augmentation technique tailored for retinal images is implemented to improve training performance. The results are presented in their raw form—without post-processing—to objectively assess the method’s effectiveness and limitations. Further refinements, including pre- and post-processing and the use of image rotations to combine multiple segmentation outputs, could significantly boost performance. Overall, this work offers a novel and effective approach to the still unresolved task of retinal vessel segmentation, contributing to more reliable automated analysis in ophthalmic diagnostics.