Digital therapeutics (DTx) is a section of digital health defined by the DTx Alliance as "delivering evidence-based therapeutic interventions to patients that are driven by software to prevent, manage, or treat a medical disorder or disease. They are used independently or in concert with medications, devices, or other therapies to optimize patient care and health outcomes". Chronic disabling diseases could greatly benefit from DTx. In this narrative review, we provide an overview of DTx in the care of patients with neurological dysfunctions.
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| http://dx.doi.org/10.1007/s00415-021-10608-4 | DOI Listing |
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European IPF/ILD Registry and Biobank (eurIPFreg/bank, eurILDreg/bank), Giessen, Germany.
Background And Aims: Predicting progression and prognosis in Interstitial Lung Diseases (ILD), especially Idiopathic Pulmonary Fibrosis (IPF) and Progressive Pulmonary Fibrosis (PPF), remains a challenge. Integrating patient-centered measurements is essential for earlier and safer detection of disease progression. Home monitoring through e-health technologies, such as spirometry and oximetry connected to smartphone applications, holds promise for early detection of ILD progression or acute exacerbations, enabling timely therapeutic interventions.
View Article and Find Full Text PDFChronic wounds, due to their high prevalence, are a serious global health concern. Effective therapeutic strategies can significantly accelerate healing, thereby reducing the risk of complications and alleviating the economic burden on healthcare systems. Although numerous experimental studies have investigated wound healing, most rely on qualitative observations or quantitative direct measurements.
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Alpert Medical School of Brown University, Department of Medicine, Division of Cardiology, Rhode Island Hospital.
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View Article and Find Full Text PDF[Acute airway obstruction from a retropharyngeal hemorrhage].
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View Article and Find Full Text PDFArtificial intelligence-enhanced magnetic resonance imaging-based pre-operative staging in patients with endometrial cancer.
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Institute of Image-Guided Surgery, IHU Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.
Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.
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