RESUMO
(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.
RESUMO
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.
