Alzheimer's disease is a neurological ailment that worsens over time and gradually deteriorates memory and thinking abilities, eventually robbing individuals of their ability to perform even the simplest tasks independently. As the primary cause of dementia, Alzheimer's accounts for a significant majority of cases, affecting over 55 million individuals globally. To identify the biomarkers responsible for Alzheimer's disease, various scanning methods are employed. In the early stages of Alzheimer's, changes in the chemical composition of the brain have shown a correlation with the macular region of the eye. Optical imaging is a fast-developing discipline in medical research and clinical practice. It provides a non-invasive method of visualizing as well as diagnosing a variety of medical disorders. When combined with AI, it becomes a strong tool for detecting Alzheimer's disease in its infancy. Quantum computing is a revolutionary technology that is based on quantum physics. It is capable of revolutionizing healthcare. Beyond its applications in expediting drug development and enabling customized medicine, quantum computing also has the potential to unravel brain puzzles and improve medical imaging. This research investigates the use of quantum technology to unravel the enigma of Alzheimer's disease, particularly in its nascent stages.
Alzheimer's disease is a neurological ailment that worsens over time and gradually deteriorates memory and thinking abilities, eventually robbing individuals of their ability to perform even the simplest tasks independently. As the primary cause of dementia, Alzheimer's accounts for a significant majority of cases, affecting over 55 million individuals globally. To identify the biomarkers responsible for Alzheimer's disease, various scanning methods are employed. In the early stages of Alzheimer's, changes in the chemical composition of the brain have shown a correlation with the macular region of the eye. Optical imaging is a fast-developing discipline in medical research and clinical practice. It provides a non-invasive method of visualizing as well as diagnosing a variety of medical disorders. When combined with AI, it becomes a strong tool for detecting Alzheimer's disease in its infancy. Quantum computing is a revolutionary technology that is based on quantum physics. It is capable of revolutionizing healthcare. Beyond its applications in expediting drug development and enabling customized medicine, quantum computing also has the potential to unravel brain puzzles and improve medical imaging. This research investigates the use of quantum technology to unravel the enigma of Alzheimer's disease, particularly in its nascent stages.
Quantum machine learning is a new research field combining quantum information science and machine learning. Quantum computing technologies appear to be particularly well-suited for addressing problems in the health sector efficiently. They have the potential to handle large datasets more effectively than classical models and offer greater transparency and interpretability for clinicians. Alzheimer’s disease is a neurodegenerative brain disorder that mostly affects elderly people, causing important cognitive impairments. It is the most common cause of dementia and it has an effect on memory, thought, learning abilities and movement control. This type of disease has no cure, consequently an early diagnosis is fundamental for reducing its impact. The analysis of handwriting can be effective for diagnosing, as many researches have conjectured. The DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset contains handwriting samples from people affected by Alzheimer’s disease and a group of healthy people. Here we apply quantum AI to this use-case. In particular, we use this dataset to test classical methods for classification and compare their performances with the ones obtained via quantum machine learning methods. We find that quantum methods generally perform better than classical methods. Our results pave the way for future new quantum machine learning applications in early-screening diagnostics in the healthcare domain.
Alzheimer’s is a progressive and degenerative brain disorder causing impairment in memory and cognitive function. It is the most common form of Dementia and the prevalence of Alzheimer’s disease (AD) increases with each decade of life. This usually starts in late middle age to old age resulting in progressive memory loss, impaired thinking, disorientation and changes in personality. Neurons are first lost in the hippocampus (the brain’s centre for memory and learning), and presence of neurofibrillary tangles and plaques containing beta-amyloid cells. Owing to the progressive nature of the disease and the complex structure of the brain, the exact causes, mechanisms and how Alzheimer’s spread is not completely understood thereby inhibiting in the production of a cure for the disease. Quantum Machine Learning (QML) is a promising area of research for the detection and diagnosis of Alzheimer’s disease. PET scan helps in diagnosing Alzheimer’s by detecting the amyloid plaques and tau tangles. It reveals areas with reduced metabolism, which helps differentiate Alzheimer’s from other conditions. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. In this study, we propose a model based on quantum machine learning classifiers to classify Alzheimer’s disease. This showcases the feasibility of Quantum enhanced classification and detection of AD for extremely limited medical datasets.
Quantum computing is quickly revolutionizing medicine, promising unprecedented levels of processing, analysis, and decision-making abilities. This review discusses recent developments in applying quantum computing to medical imaging, diagnostics, and treatment planning. Quantum machine learning and hybrid quantum-classical models have been used by researchers to show improved accuracy, speed, and scalability in medical image classification, segmentation, and disease prediction. Quantum-improved algorithms are being implemented for early detection of multifactorial diseases like cancer and neurologic disorders with potential for advancing clinical prognoses and biomarker discovery. Moreover, quantum cryptography methods are under development to enhance security for private medical information with concerns regarding expanding privacy and security risks. Improvements in quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) also hold promise in enhancing diagnostic efficiency and resource savings. This review synthesizes existing applications, identifies new trends, and delineates challenges and future directions of quantum computing in medicine. It underscores the importance of explainable and interpretable quantum AI systems to maintain clinical trust and utility. The results indicate that quantum technologies, although in their infancy, have transformative potential for precision medicine, personalized therapy, and sophisticated medical decision-making.
Quantum computing is becoming a revolutionary technology with vast applicability across many sectors, including medicine. As the medical science is in front of more complicated problems involving drug discovery, genomics, and disease modeling, the application of quantum computing opens doors to new opportunities. This paper also discusses specific uses of quantum computing in the healthcare domain: drug discovery, genomics and genetics, medical imaging, biomolecular simulations, and public health. The research also embraces how quantum algorithms improve drug discovery, genome data, and disease diagnosis with focus on improving techniques in medical imaging and biomolecular modeling. These include quantum machine learning, applying in personalized medicine, quantum simulations, for protein folding, and increased resolution of imaging techniques. As with all technologies, there are still numerous concerns associated with scaling such a tool, optimizing the employed algorithms, and maintaining the privacy of the patient’s information and the security of this data. Its relevance is in the investigations conducted to show the applicability of current problems in medicine to quantum computing in order to spearhead more research and cooperation across various fields. Over time, quantum technologies’ potential keeps emerging and consequently will revolutionize healthcare, enhance patient experience, and fuel pharmaceutical development.
Alzheimer's disease (AD) is a debilitating neurodegenerative condition that affects millions globally, with early diagnosis being crucial for effective intervention and treatment. This project explores Quantum Neural Networks (QNN) combined with Magnetic Resonance Imaging (MRI) data to enhance diagnosis accuracy. This hybrid quantum- classical approach leverages advanced preprocessing techniques, uncovering intricate patterns in MRI data. The study highlights the potential of quantum computing in revolutionizing early-stage Alzheimer's detection, achieving superior performance over traditional methods. Keywords— Alzheimer’s disease, Quantum Computing, MRI, Machine Learning, Quantum Neural Networks
There is presently no cure for Parkinson's disease (PD), a degenerative and devastating neurological ailment that affects millions of people worldwide. The field of PD could benefit significantly from quantum computing, an emerging technology that has the potential to revolutionize healthcare research. After introducing Parkinson's disease (PD), its symptoms, and current treatments, this paper dives into the role that quantum computing could play in PD diagnosis and treatment. The research explains how quantum machine learning algorithms might enhance the accuracy of PD diagnosis and prediction by analyzing complicated datasets, including genetic data and medical pictures, to uncover biomarkers for early identification. In addition, the feasibility of using quantum computing to expedite drug discovery and back up personalized medicine approaches in PD treatment is investigated. The constraints and ethical concerns of applying quantum computing to healthcare are also discussed. Research is needed to address the limits of quantum computing technology. Still, the study does imply that quantum computing has the potential to dramatically improve healthcare applications, particularly in the field of neurological illnesses.