R&D Projects and Clinical Studies
Project No.: INNEST/2021/348
Project funded by the Agència Valenciana de la Innovació (AVI)
Game-changing value proposition for early diagnosis and effective intervention of sarcopenia syndrome: the progressive loss of skeletal muscle that comes with aging
iSARC-Genetics project born with the mission of providing a novel and cost-efficient scientific and technological solution in the field of health, and with a value proposition for the prevention and control of sarcopenia, through the development of decision support system for the diagnosis and monitoring of Sarcopenia, based on a new wearable imaging device (wearable ultrasound), integrated into an innovative cloud computing system grounded on Artificial Intelligence (AI) that will allow early detection of this prevalent muscle disease, and the remote monitoring of patients in their personalized treatment and care
Project No.: 2021/C005/00150950
Project funded by Ministerio de Asuntos Economicos y Transformación Digital (RED.ES)
2D and 3D Convolutional Neural Networks to train Deep Learning Models for the advance analysis of ultrasound images
The technological solution defined within the framework of the iRIA project aims to investigate an innovative computational system based on Deep Learning Models for the advanced analysis of US images, with the aim of improving and optimizing the automation of relevant image features, using Deep Neural Networks (DNNs). It is expected to achieve the development of an advanced next-generation US image analysis system, exploiting the intrinsic complexities of anatomical structures and tissues to drive effective patient stratification and rapid diagnosis, surpassing the current state of the art previously exposed and analyzed
SERGAS Reg. Code: 2022/326
iSARC™ Research Project. Early diagnosis of sarcopenia
through Ecographic images processed and analyzed by the PIIXMED™ system (Cloud Web Application Medical Device as a Computer Program)
iSARC Study Hypothesis: That a tool such as PIIXMEDTM could simplify the diagnosis of sarcopenia, reliably confirming muscle quantity and quality, helping the medical professional to diagnose by means of sarcopenia indices and that it is easy to manage in Primary Care and in hospitals
Objectives: The aim of the iSARC™ research study is to analyze the variation of ultrasound parameters of muscle architecture with age in a healthy population. To identify "ranges of normality" in ultrasound muscle architecture parameters by age and sex groups. Identify patients with sarcopenia with ultrasound study. Determine cut-off points to establish sarcopenia indices. To assess the PIIXMED™ system as a valid, consistent, and reproducible diagnostic tool for the finding of sarcopenia
Project No.: SNEO-20211084
Project funded by Centro para el Desarrollo Tecnológico y la Innovación (CDTI)
Intelligent biomedical imaging system for remote patient monitoring and quantitative image biomarker analysis of physiological parameters
Dawako Medtech through the WUS System project is developing an innovative patented multi-modal Wearable Ultrasound Apparatus system for the acquisition of ultrasound images, bioelectrical signals (EMG, ECG) and spectrometry (NIRS) in motion, in patch format: WUS System, which together with the PIIXMED platform, as an intelligent system, in a Cloud environment, based on Artificial Intelligence (AI) and Machine Learning algorithms, allows the analysis of quantitative biomarkers of ultrasound images and remote monitoring of patients, for personalized healthcare and the physiological performance of users and/or patients
Project No.: CPP2022-009718
Project funded by Ministerio de Ciencia e Innovación (MICINN)
Development of a Sarcopenia prediction model by Integrating Genome Analysis, Microbiome, and other Biochemical Biomarkers
SARCOTECH project get focus on development of a Sarcopenia prediction model by Integrating Genome Analysis, Microbiome, and other Biochemical Biomarkers. The proposal involves the combination of omics, Bioinformatics, and Information and Communication Technologies to develop a rapid and low-cost solution for the massive screening of sarcopenia. The novelty of the proposal consists in combining multiple risk factors on sarcopenia, such as genetics, microbiota, clinical biomarkers, and lifestyle parameters in a machine learning algorithm capable of predicting the onset and severity of this geriatric syndrome
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