Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Costantini, Giovanni; Cesarini, Valerio; Leo, Pietro; Amato, Federica; Suppa, Antonio; Asci, Francesco; Pisani, Antonio; Calculli, Alessandra; Saggio, Giovanni

doi:10.3390/s23042293

Cited by 41 publications

(18 citation statements)

References 83 publications

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“…The disruption caused by the COVID‐19 pandemic presented a marked opportunity to accelerate changes in PD care provision 34 . In addition, there is potential for substantial developments in PD diagnostics and care, using machine‐ and deep‐learning, and artificial intelligence‐enabled technology 32,35 . Further research is needed to understand which aspects of care are most amenable to telehealth and other technological advancements.…”

Section: Discussionmentioning

confidence: 99%

Experiences of health service access: A qualitative interview study of people living with Parkinson's disease in Ireland

O' Shea,

Rukundo,

Foley

et al. 2023

Health Expectations

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BackgroundPeople with Parkinson's disease (PD) do not always access specialist outpatient services in a timely manner in Ireland. The perspectives of people living with PD, relating to service access, are largely absent in the existing literature.AimTo explore experiences of PD service access for people living with PD, using a qualitative approach.MethodsPurposive maximum variation sampling was used. Semi‐structured telephone interviews were conducted with 25 service users, including people with PD (n = 22) and supporting carers (n = 3). Informed consent was obtained from all participants. Interviews ranged in duration from 30 to 90 min. Data were managed in NVivo 12 and interpreted inductively using thematic analysis. The researchers were reflexive throughout the research process. The Consolidated Criteria for Reporting Qualitative Research checklist was employed to maximise transparency.ResultsThe findings highlight several key barriers to and facilitators of equitable and timely service access. Three key themes were identified comprising experiences of PD service access including ‘geographical inequity’, ‘discriminatory practices’, and ‘public and private system deficits’. Together, these themes illustrate how a two‐tiered and under‐resourced health system lacks capacity, in terms of infrastructure and workforce, to meet PD needs for both public and private patients in Ireland.ConclusionsThese findings point to problems for PD care, relating to (i) how the health system is structured, (ii) the under‐provision and under‐resourcing of specialist outpatient PD services, including medical, nursing, and multidisciplinary posts, and (iii) insufficient PD awareness education and training across health settings. The findings also show that telemedicine can provide opportunities for making access to certain aspects of PD care more flexible and equitable, but the feasibility and acceptability of technology‐enabled care must be assessed on an individual basis. Implications for policy, practice and research are discussed.Patient or Public ContributionThe design and conduct of this study were supported by an expert advisory group (EAG) of 10 co‐researchers living with PD. The EAG reviewed the interview schedule and the protocol for this study and provided detailed feedback from their perspective, to improve the methods, including the interview approach. The group also reviewed the findings of the study and contributed their insights on the meaning of the findings, which fed into this paper.

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Section: Discussionmentioning

confidence: 99%

Experiences of health service access: A qualitative interview study of people living with Parkinson's disease in Ireland

O' Shea,

Rukundo,

Foley

et al. 2023

Health Expectations

View full text Add to dashboard Cite

show abstract

“…Within the realm of audio processing, deep learning approaches (especially RNNs) have been successfully used for speech recognition [61], voice analysis [62], speed detection (based on the noise generated by vehicles) [63] and traffic noise levels in smart cities [64,65]. AI models are capable of processing raw audio data, extracting important features and generating predictions with astonishing accuracy.…”

Section: Artificial Intelligence For Image and Audio Processingmentioning

confidence: 99%

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Bacanin,

Jovanovic,

Stoean

et al. 2024

Axioms

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Respiratory conditions have been a focal point in recent medical studies. Early detection and timely treatment are crucial factors in improving patient outcomes for any medical condition. Traditionally, doctors diagnose respiratory conditions through an investigation process that involves listening to the patient’s lungs. This study explores the potential of combining audio analysis with convolutional neural networks to detect respiratory conditions in patients. Given the significant impact of proper hyperparameter selection on network performance, contemporary optimizers are employed to enhance efficiency. Moreover, a modified algorithm is introduced that is tailored to the specific demands of this study. The proposed approach is validated using a real-world medical dataset and has demonstrated promising results. Two experiments are conducted: the first tasked models with respiratory condition detection when observing mel spectrograms of patients’ breathing patterns, while the second experiment considered the same data format for multiclass classification. Contemporary optimizers are employed to optimize the architecture selection and training parameters of models in both cases. Under identical test conditions, the best models are optimized by the introduced modified metaheuristic, with an accuracy of 0.93 demonstrated for condition detection, and a slightly reduced accuracy of 0.75 for specific condition identification.

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“…In 2023, Costantini et al [32] have used CNN and ML throughout the process. Initially, the voice recording was collected from PD patients.…”

Section: Literature Surveymentioning

confidence: 99%

FATFA-based DRN: Feedback Artificial Tree Firefly Algorithm-Enabled Deep Residual Network for Pathological Speech Enhancement

Katkam¹

2023

JNACS

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The voice is a most important tool for the communication among the people in their day-to-day life. However, any slight change in the voice production system may affect the quality of the voice. For the past years, researchers worked to develop an effective automatic system for the clinicians to perform a preventive diagnosis for detecting the voice pathologies in an early stage. In this paper, the Feedback Artificial Tree Firefly Algorithm (FATFA)-based Deep Residual Network (DRN) is developed for the pathological speech enhancement. Here, the Hanning window is employed for extracting the frames from the speech signals. The multiband spectral subtraction technique is utilized for improving the extracted frames from the speech signals. Additionally, the DRN classifier is used for enhancing the pathological speech signals where the employed classifier is trained by the proposed optimization algorithm, called FATFA. Here, the developed FATFA-based DRN is the integration of Feedback Artificial Tree (FAT) and Firefly Algorithm (FA). However, the developed pathological speech enhancement method achieved efficient performance in terms of Perceptual Evaluation of Speech Quality (PESQ), and Root Mean Square Error (RMSE) with a higher PESQ of 3.907, and lesser RMSE of 30.64, respectively.

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Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Cited by 41 publications

References 83 publications

Experiences of health service access: A qualitative interview study of people living with Parkinson's disease in Ireland

Experiences of health service access: A qualitative interview study of people living with Parkinson's disease in Ireland

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

FATFA-based DRN: Feedback Artificial Tree Firefly Algorithm-Enabled Deep Residual Network for Pathological Speech Enhancement

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