Author(s): Rodrigues, Pedro Miguel ; Bispo, Bruno ; Freitas, Diamantino Silva ; Marques, João Alexandre Lobo ; Teixeira, João Paulo
Date: 2023
Persistent ID: http://hdl.handle.net/10198/29105
Origin: Biblioteca Digital do IPB
Subject(s): Detection; Diagnosis; Machine learning; Nervous system disease; Technologies
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.
