Theory of Mind Helps to Predict Neurodegenerative Processes in Parkinson’s Disease

Przybyszewski, Andrzej W.

doi:10.1007/978-3-030-77967-2_45

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The feedback pathways use “modulator logical rules” to help change weak concepts of objects’ physical properties into crisp classifications in psychophysical space [ 117 ].…”

Section: Rough Rules Can Implement Visual System Principiamentioning

confidence: 99%

“…It identifies similarities between granules obtained from one group of more advanced PD patients to estimate the disease progression of other patients. While it was found that the accuracy of prediction increased with disease progression, it is noted that divergent sets of granules characteristic of different parts of the brain might degenerate in different ways with disease progression [ 117 ]. The exploration of various AI methods further underscores this field’s potential.…”

Section: Fuzzy Detectors As Possible Predictive Models Of Ndsmentioning

confidence: 99%

“…• The feedforward pathways use "driver logical rules" to combine properties extracted in each area into hypotheses related to possible objects. • The feedback pathways use "modulator logical rules" to help change weak concepts of objects' physical properties into crisp classifications in psychophysical space [117].…”

Section: Rough Rules Can Implement Visual System Principiamentioning

confidence: 99%

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Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Chudzik,

Śledzianowski,

Przybyszewski

2024

Sensors

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Neurodegenerative diseases (NDs) such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores “digital biomarkers” (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

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“…The feedback pathways use “modulator logical rules” to help change weak concepts of objects’ physical properties into crisp classifications in psychophysical space [ 117 ].…”

Section: Rough Rules Can Implement Visual System Principiamentioning

confidence: 99%

Section: Fuzzy Detectors As Possible Predictive Models Of Ndsmentioning

confidence: 99%

Section: Rough Rules Can Implement Visual System Principiamentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Chudzik,

Śledzianowski,

Przybyszewski

2024

Sensors

Self Cite

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“…However, because of the long period of compensatory mechanisms unique to each patient, there is a famous statement that "No two people face Parkinson's in quite the same way." Therefore, a neurologist must consider not only motor symptoms but many others and must even try to understand a patient's cognitive and emotional symptoms through the Theory of Mind space [6,7] to estimate disease progression in the individual patient.…”

Section: Standard Neurological Approachmentioning

confidence: 99%

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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Concept of Parkinson Leading to Understanding Mechanisms of the Disease

Przybyszewski

Nowacki

Drabik

et al. 2021

Lecture Notes in Computer Science

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Theory of Mind Helps to Predict Neurodegenerative Processes in Parkinson’s Disease

Cited by 6 publications

References 14 publications

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Concept of Parkinson Leading to Understanding Mechanisms of the Disease

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