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Authors
Teixeira, João Paulo
Advisor(s)
Abstract(s)
The nervous system is essential for physical and mental health but is complex and
delicate. As it can unfortunately be affected by several progressive diseases, an early
diagnosis is often critical for effective treatment (Xu et al., 2022). The diagnosis of nervous
system diseases traditionally relies on a combination of clinical examination, imaging and
signals tests, and laboratory tests (Siuly and Zhang, 2016). However, these methods can be
time-consuming, expensive, and not always accurate (Milligan, 2019).
In an era marked by unprecedented technological advances in machine learning (ML),
a computational tool that allows the identification of patterns in data that would be difficult
or even impossible for humans, its application to assist in medical diagnosis emerges as a
beacon of hope in the complex panorama of nervous system diseases. The Research Topic
Advances in machine learning approaches and technologies for supporting nervous system
disease diagnosis aims to shed light on the transformative role that ML-based approaches
and technologies are playing in reshaping the way an ensemble of nervous system disorders
are understood, diagnosed, and treated.
Description
Keywords
Detection Diagnosis Machine learning Nervous system disease Technologies
Citation
Rodrigues, Pedro Miguel; Bispo, Bruno; Freitas, Diamantino Silva; Marques, João Alexandre Lobo; Teixeira, João Paulo (2023). Editorial: Advances in machine learning approaches and technologies for supporting nervous system disease diagnosis. Frontiers in Human Neuroscience. ISSN 1662-5161. 17, p. 1-3
Publisher
Frontiers