# Parkinson's disease diagnostics using AI and natural language knowledge transfer

arXiv: https://arxiv.org/abs/2204.12559 · HTML: https://mlsanity.com/paper/2204.12559/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 1 of 1)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)

## Built on by (most cited first, 1 of 1)

- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)

## Abstract (arXiv)

In this work, the issue of Parkinson's disease (PD) diagnostics using non-invasive antemortem techniques was tackled. A deep learning approach for classification of raw speech recordings in patients with diagnosed PD was proposed. The core of proposed method is an audio classifier using knowledge transfer from a pretrained natural language model, namely textitwav2vec 2.0. Method 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 label as PD/non-PD with severity of the disease additionally rated using Hoehn-Yahr scale. The audio recordings were cut into 2141 samples that include sentences, syllables, vowels and sustained phonation. The classifier scores up to 97.92% of cross-validated accuracy. Additionally, paper presents results of a human-level performance assessment questionnaire, which was consulted with the neurology professionals