Holistic Approaches to Monitoring Dementia Progression Beyond Cognitive Measures

Holistic Approaches to Monitoring Dementia Progression Beyond Cognitive Measures

Bimsara Dinura Jayasena¹, Rashmi Tharindya Dahanayaka², Jenny Krishara², Nithya Ravishini Abeysinghe², Janith Buddhika², Mihiri Samaraweera²

¹Faculty of Computing- Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

²Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110114

Received: 27 October 2024; Accepted: 03 November 2024; Published: 07 December 2024

ABSTRACT

This research aims to advance the diagnosis and management of vascular dementia by integrating machine learning and natural language processing into traditional diagnostic frameworks. The enhanced system targets the early detection of vascular dementia, a condition often overlooked by current neuropsychological tests. Unlike conventional methods, this proposed system provides continuous monitoring and real-time data analysis, enabling dynamic disease management. This approach not only increases diagnostic accuracy but also allows for timely interventions that could significantly alter disease progression and improve patient outcomes. Additionally, the system’s capability for personalized care helps tailor treatment plans to individual patient needs, optimizing therapeutic efficacy. The research also explores the potential for reducing healthcare costs through early diagnosis and efficient resource management. Overall, this study contributes to the field by offering innovative solutions that could revolutionize dementia research and treatment, potentially leading to broader applications for various cognitive disorders.

Keywords: Healthcare Innovation, Machine Learning, Personalized Healthcare, Real-Time Monitoring, Vascular Dementia

INTRODUCTION

Background Information

Dementia is characterized as a set of symptoms including loss of memory and is currently diagnosed in about 55 million people worldwide [1]. Alzheimer’s disease and vascular dementia affect 20 percent of these patients. Vascular dementia results from a reduced supply of blood to the brain [2]. Common causes include stroke, cardiovascular diseases, and diabetes, which causes damage to the brain. It could exhibit merely mental confusion or pronounced deficits that interfere with occupations and personal affairs. Clinical examination, neuroimaging, and neuropsychological tests such as the Neuropsychiatry Unit Cognitive Assessment Tool (NUCOG), Mini-Mental State Examination (MMSE), and Addenbrooke’s Cognitive Examination (ACE) are usually done to diagnose this condition [3]. However, there are some current issues concerning early diagnosis as well as monitoring of the disease progression [4].

Statement of the Problem

There is always a lack of early diagnosis because most of the current diagnostic methods including the MMSE and NUCOG often give poor results in diagnosing vascular dementia at its initial stages [5]. Moreover, there is a problem of discontinuous assessment since frequent and continued monitoring is important given the dynamic nature of vascular dementia management, and the current practices entail clinical visits only [6]. Another problem is concern about the reliability of diagnoses as the currently available tools rely on the evaluator’s opinion and the results can be significantly different depending on the practitioner, which makes the identification of more stable trends in the disease progression impossible [7].

Specifically, an empirical gap was found in the application of advanced technologies like machine learning, and natural language processing in the clinical care of dementia patients. Some of the benefits associated with these technologies include real-time data analysis, minimization of bias, and improved diagnostic outcomes; however, the adoption of these technologies within outpatient care continues to be low [8].

Table I. Comparison of Diagnosis Method

Research Objectives

Indeed, the purpose of the current project is to design and implement an integrated system that incorporates both the artificial intelligence techniques of machine learning and natural language processing to improve the diagnosis and follow-up of patients with vascular dementia. One of the objectives is the creation of an ML model that outperforms the present approaches of evaluating neuropsychological data and identifying the features of vascular dementia in the early stages and that provides a broad range of cognitive tests based on speech analysis [9]. The system will synchronize with E-HITH (Hospital in The Home) records for real-time cognitive checking and to accomplish interventions promptly. Of great benefit, individualized treatment advice that will stem from the accumulation of this information will assist in delivering better patient care, thus enhancing outcomes and synchronizing treatment with disease progression [10].

Significance of the Research

It can be stated that the proposed research appears to suggest a breakthrough in the diagnosis and further treatment of vascular dementia which is a growing issue due to the aging population across the world [11]. The program aims to overcome the existing problem in the evaluation of cognitive functionalities due to neuropsychological tests that disregard initial signs of vascular dementia [12]. By integrating machine learning and natural language processing into the diagnostic process, it is hoped that diagnostic precision will be enhanced, and treatment initiated at an earlier stage to change the clinical status of the disease and the patient’s outcome [13].

In addition, the function of the system for offering cognitive evaluation with further individualized therapy regimens helps in the design of treatment plans depending on the progression of the disease in each individual patient. It is to this level of patient care that this type of intervention would likely result in the improvement of the quality of life of the patients with vascular dementia, and at the same time improve the outcomes of the treatment process.

LITERATURE REVIEW

Overview of Dementia and Cognitive Decline

Alzheimer’s disease is one of the leading causes of disability and is a major global health problem with enormous social, economic, and medical implications [14]. When present, the effects go further to society and recent developments in research methods have changed the ways through which dementia is understood [15]. Dementia is widely known to be associated with the global aging populace as depicted by an increased risk percentage in the populations, especially on the actuality of Vascular dementia. Implications for society are extensive since dementia reduces life’s quality for patients and their careers, reduces the capacity for functional performance, and requires extensive care.

Since caregivers are mostly related to patients with dementia, their involvement increases and since the patient needs more attention on issues like feeding and dressing, this puts a lot of pressure socially, psychologically, and financially on the caregivers [16]. With the rise in cases of dementia, the costs involved also rise in that it includes direct medical costs, extended care costs, and indirect costs to the caregiver and the patient and their productivity. There is growing pressure on the governments and the health-care systems to incur heavily in establishing infrastructures that would accommodate the increasing number of people with dementia [17].

Table II. Technological Advancements in Vascular Dementia Care

A drastic change has occurred in the approach to dementia research in the recent past. With the help of big data and such a promising field as artificial intelligence AI in studying the disease, new ways of investigating the putative background that influences dementia, including the common way of living, environment, and genes, have been exposed [18]. In addition to improving diagnostic outcomes, these technologies have embedded opportunities for adopting personalized medicine models of practicing dementia care [19]. From these and other datasets, it is easier to pick out different patterns and risk factors for the condition, treatments may be administered at an earlier time [20].

Cognitive evaluation technologies have been noted to have advanced from simple clinical assessment methods to computerized assessment tools. Traditional cognitive testing tools also present some drawbacks; for instance, testing can only take place face-to-face, thus proving unreasonably costly and inconvenient in areas that are remote or poorly covered. Some new developments have been the better application of artificial intelligence in understanding patterns in test performance of learning disability and the use of biomarkers through blood tests coupled with neuroimages in diagnosing learning disability [21]. These technologies seek to improve the accuracy and reliability of dementia diagnostic procedures, especially in identifying early and less pronounced dementia symptoms.

Advances in Machine Learning for Healthcare

In current health care, the ML applies as the technological dynamic approach in addressing illnesses including dementia. Supervised and unsupervised learning models have been significantly employed in virtually all aspects of health care. Supervised learning models are very useful in dementia research because they involve the utilization of data that predicts the new data that is available to it, and this forms a great resource [22]. Supervised learning is used when a target variable is known in advance and internal data mining looks for ways of solving this case, whereas unsupervised learning is applied when no target variable is known, and it seeks to mine patterns for new subtypes of dementia or for new indices of the disease progression [23].

Fig. 1. Machine Learning in Dementia Diagnosis

Lacking interpretability of ML models is the problem that researchers are continuously working towards solving in the future of ML in healthcare. Knowledge of the basis of model predictions is essential for clinical acceptability as it provides a means for bringing these to practice. In addition, the AI and ML improvements will continue to develop personalized medicines by extending patient-tailored treatments [24].

METHODOLOGY

The goal of this project is to combine standard cognitive tests with modern machine-learning techniques to create a complete diagnostic system for vascular dementia. The methodology focuses on evaluating the physical and emotional reactions of patients to a variety of cognitive tasks by using audio and video data. The ultimate objective is to develop a system that can reliably identify and track cognitive decline, especially in dementia patients.

Fig. 2. System Overview Diagram

The initial phase of the process entails collecting data through a sequence of tasks intended to test participants’ motor, cognitive, and emotional capabilities. High-definition video and audio recordings were captured as participants completed tasks that assessed their ability to follow commands, recall numerical sequences, and exhibit cognitive flexibility.

To guarantee consistency, the video data especially, the hand gesture tasks was pre-processed. To reduce computational complexity, each image was shrunk to 120×320 pixels, grayscaled, and then normalized by scaling the pixel values to a range of 0 to 1. Data augmentation methods such as random rotations, zooms, and horizontal flips were used to increase the models’ resilience. Effective training and evaluation of CNNs were made possible by this preprocessing pipeline, which guaranteed consistent and high-quality data feeding into the CNNs.

Preprocessing for the audio data included segmenting continuous audio streams into discrete segments that corresponded to individual answers, volume leveling to maintain constant audio levels, and noise reduction techniques such as spectral gating to filter out background noise. Tokenized text from speech tasks was then normalized (all text was changed to lowercase and punctuation was removed), and length was changed via padding and truncation to meet the machine learning models’ input requirements.

The diagnostic system analyzes audio and visual data using a CNN-based framework. The CNN architecture was created to accommodate a variety of input formats, such as audio spectrograms from speech evaluations and pictures from gesture tasks.

The CNN architecture reduced the dimensionality of the feature maps without sacrificing the most important characteristics by incorporating several convolutional layers with ‘relu’ activation functions after max-pooling layers. The 3D feature maps were then flattened into 1D feature vectors, which were subsequently processed by dense layers to discover intricate patterns. The final output layer classified the inputs into specified categories, like various hand motions or emotional states, using a softmax activation function.

For the speech and memory assessments, the model architecture included an input layer for spectrograms, followed by convolutional layers that extracted features from the frequency and time dimensions. To capture temporal dependencies in the data, the sequence recall tasks used a Recurrent Neural Network (RNN) architecture with Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) layers. The final dense layers classified the correctness and fluency of the patient’s responses by combining characteristics and creating a softmax output [25].

The models were trained using a dataset that was split into training, validation, and test sets, typically with a ratio of 70% for training, 20% for validation, and 10% for testing. To prevent overfitting and preserve the best-performing model weights based on validation accuracy, early stopping, and model checkpointing strategies were employed alongside K-fold cross-validation to guarantee the models’ generalizability.

Through a series of controlled tasks, the diagnostic system assessed the cognitive ability of the patients. In the command-following task, patients were asked to replicate hand gestures displayed on a screen. The system analyzed the precision of these gestures and the time taken to execute them, providing a numerical assessment of motor and cognitive functions. This analysis was complemented by an emotional evaluation, where the system detected changes in facial expressions during task execution, providing insights into the patient’s emotional stability [26].

The clock drawing task tested participants’ spatial orientation and numerical cognition by asking them to draw a clock that displayed a certain time. The accuracy of the clock drawings was assessed by the CNN model, which concentrated on the precise positioning of the clock hands and numbers. Similarly, the numerical sequence task challenged the participants’ memory recall and executive functioning by requiring them to input forward and backward sequences of numbers. The cognitive challenge was properly matched to the participant’s abilities since the system adjusted the tasks’ difficulty in real time based on their performance [27]. Assessments of speech and memory function involved verbal fluency, working memory, and cognitive flexibility. The system analyzed the accuracy and clarity of the patient’s speech, detecting deviations from expected patterns that might indicate cognitive impairments. The fluency test measured categorical fluency and memory retrieval by having patients enumerate items from a predetermined category. Points were given for unique items, whereas repetitions resulted in deductions [28].

To differentiate between dementia and depression, the system employed a diagnostic approach that combined cognitive and emotional assessments with handwriting analysis. Patients were instructed to write a certain text several times. A CNN model was used to assess the handwritten samples, which found differences in letter alignment, pressure, and formation. These variations served as critical indicators for distinguishing between psychomotor retardation typical of depression and the motor control issues common in dementia [29].

Because of the system’s integration, many tools for diagnosis may be combined into one platform to provide a thorough assessment of the patient’s cognitive health. By leveraging the strengths of both machine learning and traditional cognitive assessments, the system offered a nuanced approach to diagnosis that reduced the likelihood of misdiagnosis and provided a strong foundation for personalized treatment plans [30].

The system’s performance was evaluated using a test set that had not been involved in the training or validation stages. Key performance metrics such as accuracy, precision, recall, and F1-score were calculated to assess the system’s effectiveness in diagnosing dementia and differentiating it from depression. Confusion matrices were generated to provide insights into the types of errors made by the models and to identify areas for further improvement.

Hyperparameter optimization was conducted using grid search and random search techniques to identify the optimal configuration that produced the best validation performance. These techniques systematically explored various combinations of hyperparameters, ensuring that the models were fine-tuned for maximum accuracy and generalization.

RESULTS

The system effectively demonstrated that dementia progression is marked by a decline in patients’ ability to follow commands, coupled with increased emotional distress. This deterioration was particularly noticeable as tasks became more complex. The use of CNNs allowed for accurate detection of emotional swings, which were closely correlated with different stages of cognitive decline, as supported by Gupta et al.’s research.

One of the most insightful tests was the clock drawing task, which revealed significant difficulties among dementia patients in spatial orientation and number placement, both of which worsened as the disease progressed. These findings underscore the diagnostic value of visual cognitive tasks in detecting early dementia, as highlighted by the study of Lee and Doe. Additionally, the system’s assessment of speech and memory functions highlighted the distinct impact of dementia on language skills, particularly in tasks that required reproducing complex statements or recalling sequences. This supports the findings of Kumar et al. that language tasks can effectively differentiate between dementia and other cognitive impairments, such as depression.

The system also incorporated novel techniques like handwriting analysis, which, when combined with machine learning, significantly improved diagnostic accuracy. This approach aligns with recent advancements in the field, as demonstrated by Thompson et al., who emphasize the role of machine learning in enhancing the precision of differential diagnoses. Compared to existing diagnostic tools, the system stood out for its superior accuracy, adaptability, and real-time assessment capabilities, largely due to the extensive use of CNNs. The system’s user interface was designed to be accessible to users with varying levels of cognitive ability, which is crucial for its effectiveness in clinical settings.

The integration of various cognitive and emotional assessments within the system provides a comprehensive evaluation, significantly reducing the likelihood of misdiagnosis. This integrated approach is particularly effective in distinguishing between dementia and depression, a common diagnostic challenge due to overlapping symptoms. The system’s adaptability, driven by advanced machine learning technologies, allows for continuous updates, improving its accuracy and reliability over time. Furthermore, the system enhances patient and physician engagement by providing real-time feedback and comprehensive insights into cognitive health, ultimately improving decision-making and treatment outcomes.

LIMITATIONS AND CHALLENGES

This study has made significant advances in diagnosing cognitive disorders, particularly in differentiating dementia from depression. A key achievement is the enhanced diagnostic accuracy made possible by CNNs, which effectively interpret speech patterns, emotional reactions, and visual cues to identify subtle cognitive impairments often missed by conventional methods. This improvement is especially evident in the differential diagnosis between dementia and depression, with the system able to distinguish between them through detailed analysis of handwriting and patient responses.

Another critical discovery is the system’s ability to adjust evaluation difficulty dynamically based on real-time performance, enhancing patient engagement and ensuring that the diagnostic process is tailored to individual cognitive abilities. By unifying diverse diagnostic components, the system offers a comprehensive approach to understanding and managing cognitive impairments, integrating data from emotional analysis, command following, and cognitive exercises to provide a holistic view of a patient’s cognitive health.

The system also prioritizes patient care based on diagnostic results, improving resource management in clinical settings by focusing on those with more severe symptoms. These findings demonstrate how advanced technologies can enhance the early and accurate diagnosis of cognitive disorders, ultimately leading to better clinical practices and patient outcomes by ensuring that care is personalized and appropriate to each patient’s needs.

Despite these successes, the research encountered several challenges. A primary limitation was the difficulty in acquiring large, diverse datasets for training the machine learning models, which is crucial for creating accurate and generalizable AI systems. Additionally, the system’s reliance on technology could limit accessibility for patients in remote areas or from lower socioeconomic backgrounds. The complexity of integrating multiple diagnostic tools into a single platform also posed challenges, particularly in ensuring smooth interactions between components without data loss. Ethical concerns around data privacy and patient autonomy further complicate the implementation of AI in healthcare.

These challenges highlight the need for ongoing research to refine methodologies, expand data sources, and address real-world implementation issues. Future research will focus on improving data collection, enhancing system usability, and upholding ethical standards to fully realize the potential of AI-driven diagnostic systems.

Overall, the research has shown that a comprehensive machine learning-enhanced diagnostic approach can significantly advance the identification and differentiation of dementia and depression. The integration of multiple diagnostic components into a unified platform, particularly using CNNs, has transformed diagnostic techniques for cognitive disorders. However, challenges such as data acquisition and technology accessibility remain, providing opportunities for future improvements. The plan is to address these by expanding datasets, enhancing accessibility, and focusing on the ethical use of AI in healthcare.

CONCLUSION

This research underscores the transformative potential of integrating machine learning and natural language processing within dementia diagnostics, particularly in managing vascular dementia. This approach facilitates continuous monitoring and personalized care by shifting beyond conventional cognitive assessments, improving early detection and intervention opportunities. The system’s ability to dynamically adjust to patient-specific data patterns allows for accurate and timely insights, reducing subjective interpretation and enhancing diagnostic reliability. Additionally, adopting these advanced tools has shown promise in supporting healthcare professionals through real-time, data-driven insights, paving the way for improved patient outcomes.

While the integrated diagnostic system demonstrates significant strides in dementia care, challenges such as data accessibility, technological dependence, and ethical considerations remain. Addressing these obstacles will require ongoing refinement in data acquisition techniques, user accessibility improvements, and ethical frameworks to ensure comprehensive, equitable deployment in diverse healthcare settings. Ultimately, the findings of this research contribute a robust, scalable model for cognitive health evaluation that holds potential for broader applications in neurodegenerative disease diagnostics and personalized medicine.

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