Future Perspectives for Automated Neurodegenerative Disorders Diagnosis: Challenges and Possible Research Directions

Abstract

Artificial intelligence (AI) and machine learning (ML) models have been increasingly used in the diagnosis of neurodegenerative disorders. These models have the potential to improve diagnostic accuracy, reduce the burden on healthcare systems, and improve patient outcomes. However, there are several challenges that need to be addressed for these models to be widely adopted in clinical practice. This article provides a summary of the current state of AI and ML models for neurodegenerative disorder diagnosis, including their strengths and limitations. It also discusses the challenges faced in the field, such as the need for large and diverse datasets, the difficulty of obtaining accurate and reliable medical imaging data, and the need for robust and interpretable models. Furthermore, it gives an overview of the recent developments in the field such as the use of deep learning, transfer learning, and multimodal medical image fusion techniques for the diagnosis of neurodegenerative disorders. The article highlights the need for more research and development in the field, specifically in areas such as the integration of multiple data modalities, the use of explainable AI for clinical decision making, and the development of personalized treatment plans. Finally, it suggests future research directions for the field, such as the need for more rigorous evaluation of AI models in clinical settings, the integration of AI with other diagnostic and therapeutic modalities, and the development of AI-based decision support systems for clinicians.

1 Introduction

Neurodegenerative disorders are a group of progressive conditions that affect the brain and nervous system, leading to the degeneration of nerve cells and the loss of cognitive and motor functions. These disorders are typically chronic, with no known cure and can be debilitating for patients and their families. Some of the most common neurodegenerative disorders include Alzheimer's disease (AD), the most common cause of dementia, characterized by the gradually loss of memory, language, and other cognitive abilities, Parkinson's disease (PD), a disorder of the nervous system that affects movement and causes tremors, stiffness, and difficulty with balance and coordination [1]. Huntington's disease (HD), a genetic disorder characterized by the progressive loss of cognitive and motor functions, leading to dementia and death. Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, a progressive disorder that affects nerve cells in the brain and spinal cord, leading to muscle weakness and atrophy [2]. Multiple sclerosis (MS), a chronic disorder that affects the central nervous system and causes a wide range of symptoms, including muscle weakness, spasticity, and cognitive impairment. These disorders can have a significant impact on the quality of life for those affected and their families and can also be costly for the healthcare system. There is a need for early and accurate diagnosis and treatment to slow the progression of these disorders and improve the quality of life for those affected [3].

Artificial intelligence (AI) and machine learning (ML) models have been increasingly used in the diagnosis of neurodegenerative disorders, such as AD and PD [4]. These models have the potential to improve diagnostic accuracy, reduce the burden on healthcare systems, and improve patient outcomes. However, there are several challenges that need to be addressed for these models to be widely adopted in clinical practice [5].

One of the main challenges in the field is the need for large and diverse datasets to train and validate these models. In addition, obtaining accurate and reliable medical imaging data can be difficult, especially in the case of NDDs, which are characterized by subtle changes in brain structure and function [6].

Description: The above diagram shown in Fig. 1 describes about the process for using AI and ML for automated neurodegenerative disorder diagnosis. First, (1) a large dataset of neuroimaging and clinical data is gathered from patients with neurodegenerative disorders and healthy controls. (2) The data is then pre-processed to make it suitable for model training, which includes cleaning, normalizing and transforming the data. (3 and 4) Machine learning models such as deep neural networks are then trained to identify patterns and features associated with neurodegenerative disorders. (5)The trained models are evaluated on a separate test dataset to assess their performance in accurately identifying neurodegenerative disorders. (6) Once a suitable model is selected, it can be deployed in a clinical setting to aid in the diagnosis of NDDs, and (7) the model performance is continuously monitored and retrained with new data to improve the model's performance over time.

Fig. 1

AI and ML for automated neurodegenerative disorder diagnosis

Recent developments in the field include the use of deep learning, transfer learning, and multimodal medical image fusion techniques for the diagnosis of neurodegenerative disorders [7]. These techniques have the potential to improve diagnostic accuracy and reduce the need for manual analysis of medical images.

However, there is still a need for more research and development in the field, specifically in areas such as the integration of multiple data modalities, the use of explainable AI for clinical decision making, and the development of personalized treatment plans [8].

Future research directions in the field include the need for more rigorous evaluation of AI models in clinical settings, the integration of AI with other diagnostic and therapeutic modalities, and the development of AI-based decision support systems for clinicians [9]. These efforts will be crucial to ensure the safe and effective use of AI in the diagnosis and treatment of neurodegenerative disorders.

2 Current AI and ML Models for Neurodegenerative Disease Diagnosis

AI and ML models have been increasingly used for the diagnosis and classification of NDDs, such as AD and PD. These models are designed to automatically extract relevant features from medical data, such as EEG and MRI, and use them to make predictions about the presence or progression of a disorder. One of the main advantages of these models is their ability to process large amounts of data quickly and efficiently, making them useful for large-scale screening and monitoring of patients. Additionally, these models can often identify patterns and features that are difficult for human experts to detect manually, which can lead to more accurate diagnosis [10]. There have been several case studies in which predictions about the presence or progression of neurodegenerative disorders have been made using EEG and MRI data. Some examples include: A study in which a deep learning model was trained on EEG data from patients with AD and healthy controls. The model was able to accurately classify the two groups, with a sensitivity of 92% and a specificity of 88%. A study in which a ML model was trained on MRI data from patients with Parkinson's disease and healthy controls. The model was able to accurately classify the two groups, with an accuracy of 85%. The study also showed that the model was able to predict the progression of PD by analyzing changes in the MRI data over time [11]. A study that used a combination of EEG and MRI data to predict the progression of AD in patients. The study used ML techniques to analyze the data and found that the combination of EEG and MRI data improved the accuracy of the predictions compared to using either modality alone [12]. Another study that used a combination of EEG and MRI data, along with clinical data, to predict the onset of AD in individuals with mild cognitive impairment. The study used ML techniques to analyze the data and found that the combination of data improved the accuracy of the predictions [13].

Description: The above flowchart that is Fig. 2 illustrates the various AI and machine learning models used in the diagnosis of neurodegenerative disorders. The process starts with input data, which can include medical images such as MRI and CT scans, as well as EEG signals [9]. These data are then analyzed using different types of models, including deep learning models such as convolutional neural networks and recurrent neural networks, support vector machines, random forests, decision trees, and Bayesian models. The final output of the process is a diagnosis of a neurodegenerative disorder. Additionally, genetic algorithms are used to optimize the parameters of other machine learning models to improve their performance in classifying patients.

Fig. 2

Model of AI and ML in the diagnosis of neurological disorders

Deep learning (DL) techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied for the classification of neurodegenerative disorders [14]. CNNs are particularly useful for image-based data, such as MRI scans, while RNNs are used for time-series data, such as EEG signals. Another approach that has been used is the combination of multiple data modalities, such as using both EEG and MRI data, which can improve the accuracy of the diagnosis. Transfer learning, which is the method of using a pre-trained model on one task and adapting it to a new task, has also been used to classify neurodegenerative disorders. Some of the AI and ML models include: DL models: These models, such as CNNs and RNNs, have been used to analyze medical images, such as MRI and CT scans, to identify patterns associated with NDDs [15]. They have also been used to analyze EEG and other signals to classify patients with NDDs, Support vector machines (SVMs): These models have been used to classify patients with neurodegenerative disorders based on various features, such as those extracted from medical images or other signals, Random forests: These models have been used to classify patients with NDDs based on various features, such as those extracted from medical images or other signals [16], Decision trees: These models have been used to classify patients with neurodegenerative disorders based on various features, such as those extracted from medical images or other signals, Bayesian models: These models have been used to classify patients with NDDs based on various features, such as those extracted from medical images or other signals, ANNs: These models have been used to classify patients with neurodegenerative disorders based on various features, such as those extracted from medical images or other signals, Genetic algorithms: These models have been used to optimize the parameters of other machine learning models, such as ANNs, in order to improve their performance in classifying patients with neurodegenerative disorders [17].

3 Strengths of AI and Machine Learning Models for Neurodegenerative Disorder Diagnosis

AI and ML models have the potential to be powerful tools for the diagnosis of neurodegenerative disorders. The strengths of these models include high accuracy, the ability to handle large amounts of data, automation, and the ability to identify patterns and features that are difficult for human experts to detect manually [18]. These models can often achieve high accuracy in classifying patients with neurodegenerative disorders, which can aid in early and accurate diagnosis. And, also these can able to process large amounts of data quickly and efficiently, which can be beneficial in analyzing medical images and signals that contain a lot of information [15]. AI and ML can automate the diagnostic process, which can save time and reduce the need for human expertise, which can be beneficial in certain scenarios. Further more, these can uncover patterns and features that are difficult for human experts to detect manually, which can lead to more accurate diagnoses [19].

Description: The diagram above illustrates the strengths and limitations of using AI and machine learning models for neurodegenerative disorder diagnosis. As shown, the strengths of AI and machine learning models include high accuracy, efficient handling of large amounts of data, and automation in the diagnostic process. These benefits can aid in early and accurate diagnosis, uncover patterns and features that are difficult for human experts to detect manually, and save time and reduce the need for human expertise. However, the limitations of AI and machine learning models are also highlighted in the diagram, such as lack of interpretability, lack of generalization, lack of transparency in decision making, lack of availability of data, and dependence on specific modalities. These limitations must be considered when implementing AI and machine learning models in neurodegenerative disorder diagnosis [20].

4 Limitations of AI and Machine Learning Models for Neurodegenerative Disorder Diagnosis

AI and ML models for neurodegenerative disorder diagnosis, while showing promising results, also have certain limitations [21]. One of the main limitations is the lack of interpretability, which can make it challenging to understand how the models arrived at their diagnoses. Another limitation is the lack of generalization capability, which can make the models less robust to new data. Additionally, the lack of transparency in decision making can be a challenge, as it can be difficult to understand what factors went into the decision making process [22]. Furthermore, the availability of large datasets with diverse population and labelled data can be a limitation in the development of models. Also, these models are dependent on the modalities used for the analysis, such as MRI or EEG, which can limit their applicability in certain scenarios. There are also ethical concerns, such as the potential for biased models based on biased data, which can lead to unfair predictions [19].

5 Challenges

The use of AI and ML in the diagnosis of neurodegenerative disorders is a rapidly growing field, but it faces many challenges. One of the biggest challenges is the limited availability and quality of data, particularly for rare disorders, which can make it difficult to train and validate machine learning models, leading to lower accuracy and generalizability [23]. Another challenge is the variability in the data, including variations in imaging protocols and other physiological signals. This can make it difficult for machine learning models to generalize to different populations and settings. Additionally, there is a lack of explainability in the results, which can make it difficult for clinicians to understand and trust the results. Overfitting is also a common problem with machine learning models, which can lead to poor performance on new data. Label noise is another challenge faced in the field, as it is a common problem when the data is collected from a real-world setting where the labels are not always accurate [16]. Bias in the data can also affect the accuracy of the model and lead to poor performance on certain groups of patients. Privacy and security are major concerns when dealing with medical data, particularly when using AI and machine learning models [24]. Neurodegenerative disorders are complex conditions that can have a wide range of symptoms and progression rates, making it difficult to accurately diagnose these disorders and develop models that can generalize to different patients. Additionally, many AI and machine learning models are black boxes, making it difficult to understand how the model arrived at its diagnosis. Different modalities, such as MRI and EEG, have their own specific challenges, such as noise, variability, and the need for specialized equipment. There are also ethical and legal considerations in the use of AI and machine learning models in healthcare, such as issues of privacy and data security, and the impact on the doctor-patient relationship [25]. There is currently a lack of standardization in the field of automated neurodegenerative disorder diagnosis, which can make it difficult to compare results and progress across different studies. Despite recent advances in the field, there is still a limited understanding of the underlying mechanisms of neurodegenerative disorders [26]. Validating AI and machine learning models in the field of neurodegenerative disorders can be challenging due to the lack of large, diverse, and well-labeled datasets. Additionally, validation can be difficult due to the variability of symptoms and progression rates of neurodegenerative disorders [27]. There is also a challenge in integrating AI and machine learning models into clinical practice, including not only technical issues, such as data integration and interoperability, but also organizational and cultural challenges, such as resistance to change and lack of understanding of the potential benefits of these models [28].

Description: As shown in the diagram Figs. 3 and 4, major challenges include limited availability and quality of data, variability in data, lack of explainability and interpretability, overfitting, label noise, bias, privacy and security concerns, complexity of disorders, modality-specific challenges, ethical and legal considerations, lack of standardization, limited understanding of underlying mechanisms, and difficulty in validating models. Each of these challenges can have a significant impact on the accuracy and generalizability of machine learning models for neurodegenerative disorder diagnosis.

Fig. 3

Strengths and limitations of AL ML model in neurodegenerative disease diagnosis

Fig. 4

Challenges of using AI and ML models for Automated Neurological Disorder (ANDs) diagnosis

6 Recent Developments

In current years, there have been a number of developments in the field of automated NDDs diagnosis [29]. One key area of focus has been the use of ML and DL models such as CNNs, RNNs and SVMs to analyze medical images, such as MRI and CT scans, as well as EEG and other physiological signals [30]. These models have been shown to be effective in identifying and classifying NDDs.

Another area of development has been in the integration of data from multiple modalities, such as MRI, CT, and EEG. This has been done using multimodal image fusion techniques and multimodal deep learning models [10]. This integration of data from multiple sources can improve the accuracy of diagnosis. Natural Language Processing (NLP) has also been used to analyze clinical notes and other text data to extract information relevant to neurodegenerative disorders diagnosis. Edge computing has been used to enable the use of AI and ML models in clinical settings. This can be useful for situations where data must be analyzed in real-time or where there is limited internet connectivity. Explainable AI has also been a focus, particularly in the context of healthcare. This includes the use of techniques such as feature visualization, attention mechanisms, and decision tree-based models which can make AI models more interpretable and explainable [31].

Lastly, researchers have been exploring the use of transfer learning to overcome the challenges of limited data availability and variability in neurodegenerative disorders [32]. This involves using pre-trained models to learn from other related tasks and fine-tuning them for the specific task of neurodegenerative disorders diagnosis [11].

7 Need for Research and Development in the Field

While AI and ML models have shown promise in the diagnosis of neurodegenerative disorders, there are still areas that require further research and development. These include limited availability and quality of data, variability in data, and lack of explainability [33]. Overfitting, label noise, bias, and privacy and security concerns are also challenges faced in the field. The complexity of neurodegenerative disorders, lack of interpretability, modality-specific challenges, ethical and legal considerations, and lack of standardization are also key challenges. Additionally, there is a limited understanding of the underlying mechanisms of neurodegenerative disorders, which makes it difficult to develop accurate and reliable diagnostic models [25]. Validating these models can also be difficult due to the lack of large, diverse, and well-labeled datasets. Finally, there are challenges in integrating AI and machine learning models into clinical practice, including technical, organizational, and cultural issues [28]. To address these challenges, more research is needed on how to effectively integrate multiple data modalities, make AI models more transparent and explainable for clinicians, develop personalized treatment plans, and address ethical and legal issues. Additionally, real-time monitoring, large-scale data collection, and multi-modality data integration can help in understanding the disease mechanism and identifying new biomarkers.

8 Future Research Directions

AI has the potential to revolutionize the diagnosis and treatment of neurodegenerative disorders. Some key areas of focus for AI in this field include conducting rigorous evaluations of AI models in real-world clinical settings, integrating AI with other diagnostic and therapeutic modalities, developing AI-based decision support systems for clinicians, advancing natural language processing, creating patient-centered AI systems, using AI-based predictive modeling, AI-based drug discovery, AI-based imaging analysis, AI-based biomarker discovery, and AI-based clinical trial design [34]. These approaches can improve diagnostic accuracy, increase efficiency, and lead to more effective treatments for neurodegenerative disorders [35]. AI can be used in the diagnosis, treatment, and management of neurodegenerative disorders. This includes conducting rigorous evaluations of AI models in real-world clinical settings, integrating AI with other diagnostic and therapeutic modalities, developing AI-based decision support systems for clinicians, using natural language processing techniques to extract information from unstructured data, developing patient-centered AI systems, using AI-based predictive modeling, drug discovery, imaging analysis, biomarker discovery, and clinical trial design. These approaches can lead to more accurate diagnosis, personalized treatment plans, and improved patient outcomes [5].

Furthermore, the use of AI in the diagnosis and treatment of neurodegenerative disorders can lead to more accurate and efficient healthcare delivery [23]. To achieve this, it is important to conduct more rigorous evaluations of AI models in real-world clinical settings to ensure their accuracy, reliability, and generalizability [3]. Integrating AI-based diagnosis with other diagnostic and therapeutic modalities such as imaging, genetics, and biochemistry can provide a more comprehensive understanding of the disease and lead to more accurate diagnosis and treatment. Additionally, AI-based decision support systems can assist clinicians in interpreting complex data, making more accurate diagnoses, and developing personalized treatment plans. Natural language processing techniques can also be used to extract information from unstructured data such as clinical notes and provide valuable insights into the diagnosis and management of neurodegenerative disorders [12]. As AI-based systems become more sophisticated and complex, it will be important to develop models that are more transparent and explainable to patients, giving them more control over their own data. Predictive modeling can be used to analyze large amounts of data to identify patterns and predict the likelihood of developing neurodegenerative disorders, and AI-based drug discovery can help identify new drug targets and speed up the drug development process [36]. AI-based imaging analysis and biomarker discovery can also aid in early detection and monitoring of the disease. Furthermore, AI-based clinical trial design and telemedicine systems can provide remote diagnostic and therapeutic services, and drug repurposing can identify new uses for existing drugs to treat neurodegenerative disorders [37].

9 Conclusion

In conclusion, the use of AI and ML models in the diagnosis of neurodegenerative disorders has the potential to improve diagnostic accuracy, reduce the burden on healthcare systems, and improve patient outcomes. However, there are several challenges that need to be addressed for these models to be widely adopted in clinical practice. These challenges include the need for large and diverse datasets, the difficulty of obtaining accurate and reliable medical imaging data, and the need for robust and interpretable models. Recent developments in the field such as the use of deep learning, transfer learning, and multimodal medical image fusion techniques have the potential to improve diagnostic accuracy and reduce the need for manual analysis of medical images. However, more research and development is needed in the field, specifically in areas such as the integration of multiple data modalities, the use of explainable AI for clinical decision making, and the development of personalized treatment plans. Future research directions in the field should focus on more rigorous evaluation of AI models in clinical settings, the integration of AI with other diagnostic and therapeutic modalities, and the development of AI-based decision support systems for clinicians. These efforts will be crucial to ensure the safe and effective use of AI in the diagnosis and treatment of neurodegenerative disorders.