Introduction
Neurological disorders are a major challenge in diagnosis and
treatment, but with the advent of the CTCAIT system, the way we treat these
diseases may change. In today's article, we will learn how the CTCAIT system
works to detect neurological diseases.
An AI system that opens new horizons in the early diagnosis of neurological disorders
CTCAIT Artificial Intelligence System for Neurodegenerative Disease Detection
A research team
from China has developed an artificial intelligence system called CTCAIT to
detect early symptoms of neurodegenerative diseases, through careful analysis of voice patterns, and from detected
neurodegenerative diseases such as Wilson's disease, Parkinson's disease and
Huntington's disease.
The study on
the testing of the model and its mechanism of work was published in the journalNeurocomputing. The CTCAIT deep artificial intelligence system
achieved a detection accuracy of 92.06% on a Chinese dataset and 87.73% on an
external dataset in English, which
indicates the strength of the system and the possibility of using it to analyze
multilingual audio data.
How CTCAIT works?
The system analyzes precise
phonological characteristics and uses attention mechanisms to detect changes
and disturbances in sound over time. This makes it very accurate, unlike
traditional methods that analyze speech by excessive reliance on manual
characteristics, poor ability to model changes in sound over time, and the
ability to provide clear and limited explanations.
First, the
CTCAIT deep artificial intelligence system detects neurological disorders through an advanced voice model to extract
precise features of speech such as rhythm, speed, precise frequencies, changes
in tone and vibrations, and then uses the Inception Time network to capture changes in
voice through multi-headed attention mechanisms so that the system can capture
distinctive pathological features in patient speech.
In conclusion, with an
accuracy of more than 90%, the CTCAIT system is a promising tool in the field
of medical diagnosis, and can contribute to improving patient outcomes and
saving lives.
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