Parkinson’s disease diagnostics using ai and natural language knowledge transfer
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AGH University of Science and Technology, Adama Mickiewicza 30, 30-059 Kraków, Poland
Jagiellonian University, Collegium Medicum, Jakubowskiego 2, 30-688, Kraków, Poland
These authors had equal contribution to this work
Submission date: 2023-10-04
Final revision date: 2023-12-02
Acceptance date: 2023-12-13
Online publication date: 2023-12-21
Publication date: 2023-12-21
Corresponding author
Maciej Kłaczyński   

AGH University of Science and Technology, Adama Mickiewicza 30, 30-059 Kraków, Poland
Diagnostyka 2024;25(1):2024103
With global life expectancy rising every year, ageing-associated diseases are becoming an increasingly important problem. Very often, successful treatment relies on early diagnosis. In this work, the issue of Parkinson's disease (PD) diagnostics is tackled. It is particularly important, as there are no certain antemortem methods of diagnosing PD - meaning that the presence of the disease can only be confirmed after the patient's death. In our work, we propose a non-invasive approach for classification of raw speech recordings for PD recognition using deep learning models. The core of the method is an audio classifier using knowledge transfer from a pretrained natural language model, namely wav2vec 2.0. The model was tested on a group of 38 PD patients and 10 healthy persons above the age of 50. A dataset of speech recordings acquired using a smartphone recorder was constructed and the recordings were labelled as PD/non-PD with the severity of the disease additionally rated using Hoehn-Yahr scale. We then benchmarked the classification performance against baseline methods. Additionally, we show an assessment of human-level performance with neurology professionals.
Model training was executed on ACK Cyfronet Prometheus cluster, using PLGrid infrastructure.
The work was created as part of research project of Department of Mechanics and Vibroacoustics no. AGH and CMUJ in Kraków.
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