AI in Healthcare August 28, 2021
Researchers have achieved accuracies of 99.4% and 94.3% in two algorithmic methods for monitoring, diagnosing or ruling out Parkinson’s disease going only by individuals’ spoken words.
The team built a set of 126 voice markers (i.e., “features”) touching everything from tone, pitch and loudness to enunciation, pace and pause ratio.
Additionally, they had their best model analyze 25 isolated Spanish-language words pronounced by each study subject (50 Parkinson’s patients and 50 healthy controls).
The study’s lead author is Federica Amato, a PhD candidate in computer engineering at the Polytechnic University of Turin in Italy. Senior author is electronic engineer and computer scientist Juan Rafael Orozco-Arroyave, PhD, of the University of Antioquia in Medellín, Colombia.
In a study posted in...
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