New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease

Ageing Research Reviews, Год журнала: 2023, Номер 90, С. 102013 - 102013

Опубликована: Июль 8, 2023

Язык: Английский

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

Sensors, Год журнала: 2023, Номер 23(4), С. 2293 - 2293

Опубликована: Фев. 18, 2023

Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis achieved clinically on basis different symptoms with considerable delays from onset processes in central nervous system. In this study, we investigated early and full-blown PD patients based analysis their voice characteristics aid commonly employed machine learning (ML) techniques. A custom dataset was made hi-fi quality recordings vocal tasks gathered Italian healthy control subjects patients, divided into diagnosed, off-medication hand, mid-advanced treated L-Dopa other. Following current state-of-the-art, several ML pipelines were compared usingdifferent feature selection classification algorithms, deep also explored a CNN architecture. Results show how feature-based achieve comparable results terms classification, KNN, SVM naïve Bayes classifiers performing similarly, slight edge for KNN. Much more evident predominance CFS as best selector. The selected features act relevant biomarkers capable differentiating subjects, untreated patients.

Язык: Английский

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A Comprehensive Survey on Aquila Optimizer

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(7), С. 4449 - 4476

Опубликована: Июнь 7, 2023

Язык: Английский

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Comprehensive machine and deep learning analysis of sensor-based human activity recognition

Neural Computing and Applications, Год журнала: 2023, Номер 35(17), С. 12793 - 12831

Опубликована: Март 8, 2023

Язык: Английский

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An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 8, 2024

Abstract The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing correlation between lesions and level of provided patients. Computed tomography (CT) imaging will be used analyze three levels received patient: Level 0 (minimum support), 1 (non-invasive such as soft oxygen), 2 (invasive mechanical ventilation). begin segmenting from CT images creating an appearance model each lesion using a 2D, rotation-invariant, Markov–Gibbs random field (MGRF) model. Three MGRF-based models created, one support. This suggests that able differentiate different severity in decide patient neural network-based fusion system, which combines estimates Gibbs energy models. were assessed 307 COVID-19-infected patients, achieving accuracy $$97.72\%\pm 1.57$$ 97.72 % ± 1.57 , sensitivity $$97.76\%\pm 4.08$$ 97.76 4.08 specificity $$98.87\%\pm 2.09$$ 98.87 2.09 indicating high prediction accuracy.

Язык: Английский

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A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 29, 2024

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely intervention personalized treatment strategies. We have developed novel system that extracts both local global appearance markers from images. These are obtained entire retina iso-regions aligned with optical disc. Applying weighted majority voting on best classifiers improves performance, resulting an accuracy 96.85%, sensitivity 93.72%, specificity 97.89%, precision 93.86%, F1 ROC 95.85%, balanced 95.81%, sum 95.38%. not only achieves high but also provides detailed assessment severity each retinal region. approach ensures final aligns physician’s understanding aiding them ongoing follow-up patients.

Язык: Английский

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IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems

Neural Computing and Applications, Год журнала: 2024, Номер 36(20), С. 12185 - 12298

Опубликована: Апрель 20, 2024

Abstract Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved (IHHO) different engineering tasks. The focused on random location-based habitats during exploration phase and strategies 1, 3, 4 exploitation phase. modified hawks in wild would change their perch strategy chasing pattern according to updates both phases. To avoid being stuck solution, values were generated using logarithms exponentials explore new regions more quickly locations. evaluate performance of algorithm, IHHO compared other five recent algorithms [grey wolf optimization, BAT teaching–learning-based moth-flame whale algorithm] as well three modifications (BHHO, LogHHO, MHHO). These optimizers had been applied benchmarks, namely standard CEC2017, CEC2019, CEC2020, 52 benchmark functions. Moreover, six classical real-world problems tested against prove efficiency algorithm. numerical results showed superiority algorithms, which proved visually convergence curves. Friedman's mean rank statistical test also inducted calculate algorithms. Friedman indicated that ranked first HHO.

Язык: Английский

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A review of machine learning and deep learning for Parkinson’s disease detection

Discover Artificial Intelligence, Год журнала: 2025, Номер 5(1)

Опубликована: Март 12, 2025

Millions of people worldwide suffer from Parkinson's disease (PD), a neurodegenerative disorder marked by motor symptoms such as tremors, bradykinesia, and stiffness. Accurate early diagnosis is crucial for effective management treatment. This article presents novel review Machine Learning (ML) Deep (DL) techniques PD detection progression monitoring, offering new perspectives integrating diverse data sources. We examine the public datasets recently used in studies, including audio recordings, gait analysis, medical imaging. discuss preprocessing methods applied, state-of-the-art models utilized, their performance. Our evaluation included different algorithms support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). These have shown promising results with accuracy rates exceeding 99% some studies combining analysis particularly showcases effectiveness symptom Unified Disease Rating Scale (UPDRS), monitoring progression. Medical imaging, enhanced DL techniques, has improved identification PD. The application ML research offers significant potential improving diagnostic accuracy. However, challenges like need large datasets, privacy concerns, quality healthcare remain. Additionally, developing explainable AI to ensure that clinicians can trust understand models. highlights these key must be addressed enhance robustness applicability diagnosis, setting groundwork future overcome obstacles.

Язык: Английский

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A novel approach for Parkinson’s disease diagnosis using deep learning and Harris Hawks optimization algorithm with handwritten samples

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(34), С. 81491 - 81510

Опубликована: Март 9, 2024

Язык: Английский

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ChiGa-Net: A genetically optimized neural network with refined deeply extracted features using χ2 statistical score for trustworthy Parkinson’s disease detection

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129450 - 129450

Опубликована: Янв. 1, 2025

Язык: Английский

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