Exercise-based cardiac rehabilitation (ExCR) programs are essential for patients diagnosed with cardiac diseases. Studies have shown that they aid in the rehabilitation process and may even facilitate a return to previous cardiorespiratory fitness. Also, patients who enroll and follow such programs have shown a lower rate of complications and mortality in the long run. The results vary depending on the type of program followed and the degree of debilitation the disease has caused. Therefore, in order to obtain optimal results, it is ideal to tailor each ExCR program to the individual profile of each patient. At the moment, the two most studied and employed training types are medium-intensity continuous training (MICT) and high-intensity interval training (HIIT). For most of the time, MICT was the first-choice program for patients with cardiovascular disease. In recent years, however, more and more studies have pointed towards the benefits of HIIT and how it better aids patients in recovering their cardiovascular fitness. Generally, MICT is more suited for patients with a severe degradation in functional capacity and who require a higher degree of safety (e.g., elderly, with a high number of comorbidities). On the other hand, while HIIT is more demanding, it appears to offer better outcomes. Therefore, this review aimed to summarize information from different publications on both types of training regimens in ExCR and assess their utility in current clinical practice.

Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.

(1) Background: The aortic arch (AA) branching model is challenging, considering the multiple anatomical variations documented in existing research. The bovine aortic arch (BAA) is the most prevalent anatomical variation among these. This variant of AA branching has long been considered a nonsymptomatic malformation, having been discovered incidentally during imaging investigations for other causes. However, more recent studies have demonstrated that BAA shows a frequent association with coarctation of the aorta (CoA), thoracic aortic disease (TAD), and stroke. At the same time, given the current context of increasing activity in the fields of interventional and surgical procedures in the aorta and its branches, it is very important to know the medical–surgical implications of this anatomical variant. (2) Methods: We conducted a comprehensive review using PubMed and Embase, focusing specifically on randomized trials and cohort analyses that examined the medical–surgical implications of BAA. We assessed information related to studied groups, medical procedures, and study outcomes. Initially, we identified 8454 studies, and after rigorous evaluation, we narrowed down our review to 25 articles. (3) Discussions: The intervention consisted of assessing the risks associated with BAA through different imaging investigation methods such as computer tomographic angiography (CTA), magnetic resonance imaging (MRI), or ultrasonography (US). The following results were evaluated: the prevalence of the BAA, the importance of imaging investigations in establishing the diagnosis and the therapeutic management and monitoring the evolution of patients with the BAA, the association of the BAA with CoA, TAD, and stroke, and the potential risks of interventional treatment in patients with the BAA. (4) Conclusions: The prevalence of the BAA differs both between different ethnic groups and between genders. Advanced imaging methods such as CTA and 4D flow MRI allow detailed descriptions of supra-aortic vascular anatomy and information about blood flow velocities, direction, and turbulence in the AA. US remains an easy and valuable imaging investigation, with the potential to detect and correctly diagnose the BAA and its hemodynamic implications. Anatomical variations in the AA are associated with increased rates of TAD, CoA, and stroke, necessitating early diagnosis and increased supervision of patients with such incidentally observed abnormalities. In addition, there is a need to further develop and refine the surgical techniques used and personalize them to the individual characteristics of patients with the BAA.

(1) Background: Telerehabilitation is an approach that uses digital technology to provide remote medical recovery services. It can be an option for cardiovascular recovery at home in patients with peripheral arterial disease (PAD) of the lower limbs. (2) Methods: We performed literature research through two databases: PubMed and Embase. We included randomized controlled trials and cohort studies that evaluated the effectiveness of a technology-assisted home exercise intervention compared with conventional rehabilitation or the usual care in patients with PAD. We analyzed population, intervention, and outcome data. (3) Results: We identified 2468 studies. After rigorous screening, we included 25 articles in the review. The following results were evaluated: dissemination and acceptance of digital technologies among these people, functional capacity, exercise intensity, patient motivation, sex-specific response differences in mortality and clinical outcomes, quality of life assessment, and changes in values of inflammatory biomarkers. All of these were correlated with the type of intervention and the dose of the exercise. (4) Conclusions: Home-based exercise therapy supervised with the help of specific devices could be successfully implemented in the therapeutic management of the PAD population. Health specialists should take into account the clinical–paraclinical profile and the emotional status of the patients. Such individualized interventions could bring significant benefits for the people with this disease and for the healthcare system, including increasing exercise adherence, engagement, self-care capacity, life expectancy, and quality of life for these patients, as well as reducing their symptoms, cardiovascular complications, and hospitalizations.

Transcatheter aortic valve implantation (TAVI) has redefined the management of severe aortic stenosis, particularly in surgical high-risk patients. As the number of TAVI procedures increases, there is a growing need for effective post-procedural care. Cardiac rehabilitation (CR) has emerged as a critical component of treatment in these patients. The most recent update of the European recommendations highlights the importance of including post-TAVI patients in CR programs. However, the benefits of CR in this particular patient group still need to be fully understood. The objective of this narrative review is to summarize the safety and benefits of post-TAVI CR by evaluating its impact on functional capacity, frailty, muscular strength, mental health, quality of life, and long-term survival. While emerging evidence supports its safety and effectiveness in the aforementioned outcomes, gaps remain regarding the optimal rehabilitation protocols, including the timing, duration, and intensity of CR as well as its long-term cardiovascular benefits. Further research is needed to develop personalized approaches for different patient groups. This article highlights the current knowledge, identifies critical gaps, and underlines the need for tailored rehabilitation strategies to improve post-TAVI recovery and patient outcomes.

Background/Objectives: Glioblastoma (GB) remains the most prevalent primary malignant brain tumor in adults, characterized by its aggressive nature and poor prognosis. The present study endeavored to contribute to the development of advanced computational tools for neuro-oncology by integrating artificial intelligence (AI)-based segmentation and multi-model machine learning (ML) approaches. Methods: A retrospective analysis was conducted on patients with GB. AI-driven algorithms were utilized to perform volumetric segmentation of GB. These quantitative metrics were subsequently integrated into a multi-model ML framework to analyze correlations with patient survival and evaluate the predictive accuracy of the resulting models. Results: A total of 79 patients were ultimately included in the study after meeting all eligibility criteria. The results showed that larger GB tumors were associated with shorter post-treatment survival. Necrotic patterns within GB tumors impacted patient survival rates and response to therapy. Quantitative volumetric analysis of tumor enhancement, shape features, and morphological metrics were associated with patient outcomes. The Neural Network remained the top ML model performer overall for discrimination, but the Random Forest model also showed strong practical performance. Conclusions: As a summary, our study contributes to the development of advanced computational tools for neuro-oncology by integrating AI-based segmentation and multi-model ML approaches, and the results highlight the importance of imaging biomarkers in understanding GB prognosis.