Eye-Tracking and Machine Learning Significance in Parkinson’s Disease Symptoms Prediction

Chudzik, Artur; Szymański, Artur; Nowacki, J.; Przybyszewski, Andrzej W.

doi:10.1007/978-3-030-42058-1_45

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…A dedicated data science framework written in Python was used and based on the Scikit Learn and Pandas libraries that implemented different multiclass strategies, such as K Neighbors Classifier, Support Vector Classifier, Decision Tree Classifier, and Random Forest Classifier. In this trial, Random Forest Classifier achieved the highest overall accuracy score of 0.75 and an accuracy of 0.7 when predicting subclasses of UPDRS for patients in advanced stages of the disease who responded to treatment, with a global 0.57 accuracy score for all classes [39].…”

Section: Prediction Of the Disease Progression In Different Pd Groupsmentioning

confidence: 88%

“…The goal in [39] was to predict Parkinson's disease progression in advanced-stage patients based on data obtained from patients under different treatments and in different stages of the disease. Patients from the BMT (only on medication) group (3rd visit), DBS (after recent deep brain stimulation surgery) group (3rd visit), and POP (after older DBS surgery) group (1st visit) were used as a training dataset (a model).…”

Section: Prediction Of the Disease Progression In Different Pd Groupsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski¹,

Śledzianowski²,

Chudzik³

et al. 2022

Preprint

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Humans are a vision-dominated species, and what we see depends on where we look. Therefore, eye movements (EM) are essential to our interactions with the environment, and experimental findings show EM is affected in neurodegenerative disorders (ND). It could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to determine if changes in EM-evoked responses can tell us about ND, such as Alzheimer’s (AD) and Parkinson’s Disease (PD) progression in different stages. In the present review, we have analyzed the results of neurological, psychological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with Machine Learning (ML) methods. Described predictive algorithms are using various approaches, including Granular Computing, Naive Bayes, Decision Trees/Tables, Logistic Regression, C-/LinearSVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are also navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes. In conclusion: EM parameters with clinical symptoms are powerful precision instruments that, in addition to predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

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Section: Prediction Of the Disease Progression In Different Pd Groupsmentioning

confidence: 88%

Section: Prediction Of the Disease Progression In Different Pd Groupsmentioning

confidence: 99%

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski¹,

Śledzianowski²,

Chudzik³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Los resultados al clasificar los pacientes varían en función de las mediciones realizadas. Se han publicado datos de precisión tan bajos como 0,57 para las sacadas y las antisacadas 66 , pero los números mejoran cuando se evalúa el seguimiento, con precisiones de 0,74-0,77 67,68 .…”

Section: Análisis De Los Movimientos Ocularesunclassified

Inteligencia artificial en la enfermedad de Parkinson y otros trastornos del movimiento

Lapeña Motilva,

Sánchez-Ferro

2024

KRANION

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La inteligencia artificial se ha ido abriendo paso de forma progresiva y los trastornos del movimiento no han sido una excepción a esta tendencia. La relativa facilidad de la recogida de datos hace que existan muchas iniciativas de inteligencia artificial para intentar diagnosticar o evaluar a los pacientes con trastornos del movimiento. Se han realizado estudios exitosos recogiendo datos de wearables u otros dispositivos, imágenes radiológicas, vídeos de los pacientes o señales neurofisiológicas. Los resultados en general son variables, pero la mayor parte de los estudios presentan una precisión por encima del 80%. Actualmente, la monitorización mediante wearables es la que se encuentra más avanzada, y esto se plasma en la aparición de dispositivos comerciales que ya se encuentran disponibles. Pese a toda la expectación que hay sobre estas nuevas tecnologías, quedan retos en cuanto a su validez, seguridad y responsabilidad que deben resolverse para llegar a una utilización generalizada en la práctica clínica diaria.

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“…The goal in [ 39 ] was to predict Parkinson’s disease progression in advanced-stage patients based on data obtained from patients under different treatments and at different stages of the disease. Patients from the BMT group (only on medication, third visit), DBS group (after recent deep brain stimulation surgery, third visit), and POP group (after older DBS surgery, first visit) were used as a training dataset—a model.…”

Section: Eye Movements and Neurodegenerative Diseasesmentioning

confidence: 99%

“…A dedicated data science framework written in Python was used and based on the Scikit Learn and Pandas libraries that implemented different multiclass strategies, such as k-Nearest Neighbors Classifier, Support Vector Classifier, Decision Tree Classifier, and Random Forest Classifier. In this trial, the Random Forest Classifier achieved the highest overall accuracy score of 0.75 and an accuracy of 0.7 when predicting subclasses of UPDRS for patients in advanced stages of the disease who responded to treatment, with a global 0.57 accuracy score for all classes [ 39 ].…”

Section: Eye Movements and Neurodegenerative Diseasesmentioning

confidence: 99%

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski

Śledzianowski

Chudzik

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer’s (AD) and Parkinson’s diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

show abstract

Eye-Tracking and Machine Learning Significance in Parkinson’s Disease Symptoms Prediction

Cited by 4 publications

References 10 publications

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Inteligencia artificial en la enfermedad de Parkinson y otros trastornos del movimiento

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

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