LivGuard, a deep neural network for cirrhosis detection in liver ultrasound (USD)

Introduction: Ultrasound (US) is widely used for diagnosing liver disease, particularly cirrhosis, with key signs including liver shape irregularity and echostructure. The classification of liver parenchyma as smooth or coarse, indicative of chronic liver disease, is subjective and dependent on operator experience. To address this, we introduce LivGuard, a deep learning binary classifier designed to…

BIOMAKERS and TOPAZIUM Unveil Groundbreaking AI Collaboration to Enhance NSCLC Immunotherapy

San Francisco, CA – July 17th, 2024 – BIOMAKERS, a Precision Medicine & Biotech Company that supports data-driven Drug Development globally, and TOPAZIUM, an AI-driven healthtech company specializing in drug discovery and human wellbeing, have announced the results of their innovative collaborative project. This partnership showcases the power of AI systems in leveraging real-world DNA…

“We live longer, but we want to live better”

On June 11, Topazium presented at Madrid Innovation Lab (MIL; https://milmadrid.es/) its project HealthGuard, winner of the challenge “Mechanisms for Predicting and Controlling Chronic Diseases” at the 2023 AI and Advanced Technologies Awards. After a few words of welcome, Patricia Sarasola, Head of the Innovation Department of the Madrid City Council, emphasized the accessibility of…

SkinGuard receives the “Best Healthcare Solution Award” at the IOT solutions World Congress

SkinGuard is a computer-vision smartphone-based application utilising cutting-edge machine learning technologies to identify malignant skin lesions with an accuracy above 96%. It was fully developed and implemented by Topazium and it is intended for easy detection of skin cancer like melanoma to help practitioners (especially GPs) identifying the disease in its early stage. SkinGuardTM focuses…

Identification of genes related to PD-L1 expression in non-small cell lung cancer (NSCLC) tumor samples by GFPrint, a machine learning framework (MLF)

Background: The advent of drugs targeting the PD-1/PD-L1 axis has revolutionized NSCLC treatment. Although promising, individual responses to these therapies can vary among individuals, and research is ongoing to better understand the mechanisms involved in PD1/PD-L1 expression to optimize treatment protocols, and identify which patients are most likely to benefit from these therapies. In this…

Development and validation of MOSPROC, an easily applicable predictive score of mortality for patients with prostate cancer

Background: Predicting life expectancy in prostate cancer patients is particularly difficult as different clinical factors significantly influence clinical outcome. Here we have developed MOSPROC, a simplified mortality predictive score to identify prostate cancer patients at high risk of mortality. Methods: The training set (TRS) (n=2,035) included metastatic Castration Resistant PC (mCRPC) patients from 4 randomized…

Ethics or Atics? The role of artificial intelligence in medicine

In an era of technological uncertainty, artificial intelligence (AI) emerges as a revolutionary tool capable of transforming multiple sectors, including medicine. However, it is crucial to understand its role as a complement rather than a substitute for human intelligence and quality, while maintaining the legacy of the Hippocratic method. The era of Artificial Intelligence (AI)…

GFPrint and PredLung nominated finalists in the AstraZeneca’s BeLung Innovation challenge

Two machine learning frameworks developed in Topazium, GFPrint™ and PredLung™, have been used to extract important information from cancer patients, in particular from lung cancer patients, that may serve to design better therapies, generate virtual biomarker signatures and predict patient outcomes. Three case studies are presented, focused on i) the discovery of potential novel targets…