Multi-granular Computing Can Predict Prodromal Alzheimer’s Disease Indications in Normal Subjects

Przybyszewski, Andrzej W.

doi:10.1007/978-3-031-36024-4_22

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…Another BIOCARD study utilizes multi-granular computing to refine the process of classifying cognitive data related to Alzheimer’s Disease, aiming for early detection [ 127 ]. Researchers modified the number of attributes used in the BIOCARD study, increasing the variety of granules from five to seven attributes, compared to the constant fourteen attributes used previously.…”

Section: Ai and Machine Learning Can Predict Symptoms And Progression...mentioning

confidence: 99%

“…The goal is to develop a more accurate and reliable system for early diagnosis, which is critical for effective intervention. Researchers are testing various models to determine the most effective ones for identifying different stages of diseases like Alzheimer’s and Parkinson’s, and they seek classifications that remain consistent irrespective of the algorithms used [ 127 ]. Therefore, the overarching aim is to develop a more precise and reliable diagnostic system for early intervention.…”

Section: Ai and Machine Learning Can Predict Symptoms And Progression...mentioning

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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Section: Ai and Machine Learning Can Predict Symptoms And Progression...mentioning

confidence: 99%

Section: Ai and Machine Learning Can Predict Symptoms And Progression...mentioning

confidence: 99%

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

Chudzik,

Śledzianowski,

Przybyszewski

2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Multi-granular Computing Can Predict Prodromal Alzheimer’s Disease Indications in Normal Subjects

Cited by 1 publication

References 12 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

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