Supper happy to share that our paper, "Prediction of low birthweight from fetal ultrasound and clinical characteristics: a comparative study between a low-middle-income and a high-income country," has been accepted for publication in #BMJGlobalHealth!
https://t.co/y79qBCiVR3
Last weeks for applying to the open Data Scientist position in our research group at UPF.
https://t.co/SBmoZsyKeP
A great opportunity for motivated MSc students to contribute to a project for advancing fetal medicine in Low-Middle-Income Countries for Life-saving Impact!
📽️ El documental que muchos aficionados al fútbol y valencianistas estaban esperando 🚨
🦇 Un repaso a una mentira que se prolonga desde 2014 en la que el @valenciacf y su gente son los mayores perjudicados
📺 Peter Lim: destruyendo un sentimiento
🔗 Ya disponible en YouTube: https://t.co/4AkS37e612
Collaborative work to bridge inequity in peri natal outcomes. Use of AI in fetal biometry and dopplers with high quality images for machine learning from LMIC
Sergio Sánchez Martínez, Josa Prats iValero, @a_m_aguado @devyanichowdhu1@HoodbhoyZahra@DrBabarHasan@DrShaziaMohsin
Within the La Caixa Foundation Inphinit Doctoral programme, cofunded by the EU Horizon 2020 Marie Skłodowska-Curie framework (), we have a fully funded PhD position available in Barcelona on 'Developing and investigating computing,…https://t.co/9vPS86ACXK https://t.co/QGiRB1BM6E
¡He empezado en un nuevo puesto de Postdoctoral Researcher en Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)! https://t.co/hZLKOSNWxu
Gran artículo de mi buena amiga Rocio. ¿Qué es nuestra identidad digital, y cómo nos hace vulnerables? Currently reading: 'Identidad 2.0' on https://t.co/7lwqcGaTwU
At the same time, the paper aims at informing cardiologists about which ML tools could target their problems and what are their current limitations.
🥳🎆
Check our article "Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging" published in Frontiers in Cardiovascular Medicine.
#medicine#machinelearning#decisionmaking#cardiologist
https://t.co/4Df72q72Ci
This paper addresses potential questions arising from data scientists, industrial partners and funding institutions, helping them understand clinical decision-making in cardiology and identify potential niches for their solutions to be helpful.