Focused review on artificial intelligence for disease detection in infants

Autor: Katrin D. Bartl-Pokorny, Claudia Zitta, Markus Beirit, Gunter Vogrinec, Björn W. Schuller, Florian B. Pokorny

Publikováno v: Frontiers in Digital Health, Vol 6 (2024)

Over the last years, studies using artificial intelligence (AI) for the detection and prediction of diseases have increased and also concentrated more and more on vulnerable groups of individuals, such as infants. The release of ChatGPT demonstrated

Externí odkaz: https://doaj.org/article/9d5b137a12d047f28f774e04d582afcb

HEAR4Health: a blueprint for making computer audition a staple of modern healthcare

Autor: Andreas Triantafyllopoulos, Alexander Kathan, Alice Baird, Lukas Christ, Alexander Gebhard, Maurice Gerczuk, Vincent Karas, Tobias Hübner, Xin Jing, Shuo Liu, Adria Mallol-Ragolta, Manuel Milling, Sandra Ottl, Anastasia Semertzidou, Srividya Tirunellai Rajamani, Tianhao Yan, Zijiang Yang, Judith Dineley, Shahin Amiriparian, Katrin D. Bartl-Pokorny, Anton Batliner, Florian B. Pokorny, Björn W. Schuller

Publikováno v: Frontiers in Digital Health, Vol 5 (2023)

Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. Thi

Externí odkaz: https://doaj.org/article/37ec478a52a04d60a259dc435e111374

Novel AI driven approach to classify infant motor functions

Autor: Simon Reich, Dajie Zhang, Tomas Kulvicius, Sven Bölte, Karin Nielsen-Saines, Florian B. Pokorny, Robert Peharz, Luise Poustka, Florentin Wörgötter, Christa Einspieler, Peter B. Marschik

Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)

Abstract The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessm

Externí odkaz: https://doaj.org/article/5e84508d3ab84cf5b9b5854daeaa0dd4

Triplet Loss-Based Models for COVID-19 Detection from Vocal Sounds

Autor: Adria, Mallol-Ragolta, Florian B, Pokorny, Katrin D, Bartl-Pokorny, Anastasia, Semertzidou, Bjorn W, Schuller

Publikováno v: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

This work focuses on the automatic detection of COVID-19 from the analysis of vocal sounds, including sustained vowels, coughs, and speech while reading a short text. Specifically, we use the Mel-spectrogram representations of these acoustic signals

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::894b0d0c6f83765b040ff39e1d2ec2a4
https://doi.org/10.1109/embc48229.2022.9871125

Fitbeat:COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

Autor: Gloria Dalla Costa, Judith Dineley, Per Soelberg Sørensen, Raquel Bailon, Matthew Hotopf, Srinivasan Vairavan, Mathias Buron, Giancarlo Comi, Björn Schuller, Carlos Nos, Shuo Liu, Florian B. Pokorny, Ana Isabel Guerrero, Vaibhav A. Narayan, Shaoxiong Sun, Amos Folarin, Yatharth Ranjan, Letizia Leocani, Estela Laporta Puyal, Spyridon Kontaxis, Nicholas Cummins, Callum Stewart, Patrick Locatelli, Jing Han, Richard Dobson, Zulqarnain Rashid, Melinda Magyari, Pauline Conde, Ana Zabalza

Publikováno v: Liu, S, Han, J, Puyal, E L, Kontaxis, S, Sun, S, Locatelli, P, Dineley, J, Pokorny, F B, Dalla Costa, G, Leocani, L, Guerrero, A I, Nos, C, Zabalza, A, Sorensen, P S, Buron, M, Magyari, M, Ranjan, Y, Rashid, Z, Conde, P, Stewart, C, Folarin, A A, Dobson, R J B, Bailon, R, Vairavan, S, Cummins, N, Narayan, V A, Hotopf, M, Comi, G, Schuller, B & RADAR-CNS Consortium 2022, ' Fitbeat : COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder ', Pattern Recognition, vol. 123, 108403 . https://doi.org/10.1016/j.patcog.2021.108403
Pattern Recognition

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with mu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b8dd863a0309e61c297f3dbf7b94a6f
https://curis.ku.dk/portal/da/publications/fitbeat(e7102f68-cfff-4fe4-b8bf-03199a1d93ae).html