Neuroscience · Paper · July 2024
Aryan Singhal, Hursh Shah
Magnetic resonance imaging (MRI) is widely used by neurologists to detect brain abnormalities such as strokes, tumors, and various forms of dementia, including Alzheimer's disease. However, accurately diagnosing the different stages of Alzheimer's disease remains a challenge, with nearly one in five patients misdiagnosed due to symptom overlap with other conditions. This paper introduces QViSTA, a novel hybrid quantum vision transformer (QViT) model that exploits quantum parallelism to improve early diagnosis and differentiation of Alzheimer's disease stages. By integrating quantum variational circuits (VQCs) with vision transformers (ViTs), QViSTA addresses the data scalability and computational efficiency limitations of classical machine learning models. Using a balanced, multi-class dataset of 40,000 MRI images, QViSTA achieved a validation area under the receiver operating characteristic (AUC) of 87.86% and a test AUC of 86.67%, closely matching the performance of a benchmarked classical ViT while reducing feature space by 3.18%. Early and accurate detection of Alzheimer's disease is critical, as it allows for timely interventions that can significantly improve the quality of life for patients and their caregivers. As more hospitals adopt AI for biomedical imaging, QViSTA's innovative approach could dramatically reduce misdiagnosis rates, improve patient outcomes, and reduce costs.
Neuroscience · Paper · June 2024
Tanisha Sood
The gravity of seizures in pediatric populations cannot be overstated. From the youngest infants to adolescents on the brink of adulthood, seizures disrupt not only neurological functions but also the fabric of daily life. According to the Centers for Disease Control and Prevention, about 470,000 children in the United States have epilepsy, which is a neurological disorder characterized by recurrent seizures. Drawing from personal encounters with seizures, I recognize the critical need for effective seizure detection mechanisms. This study addresses the challenge of detecting seizures in children, especially in environments where constant supervision may not be feasible. Utilizing CAD, 3D design, and machine learning algorithms, this prototype predicts a seizure onset by analyzing bio data from a simulation of possible data sets carefully overviewed by my pediatrician Sara MachMan. The prototype integrates insights from a literature search, including the CDC publication on seizure detection, highlighting brain activity, fever, and body movements as triggers. Based on these triggers and thorough data analysis with a pediatrician, I was able to discover a breakthrough. Despite encountering a 15% error rate from my simulated data analysis, the prototype effectively alerts parents and medical professionals, demonstrating its potential to reduce the unpredictability of seizures and positively impact lives.
Neuroscience · Paper · June 2023
Advaith Anand
Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects millions of people worldwide. The disease primarily impacts the dopaminergic neurons in the substantia nigra, leading to motor symptoms such as tremors, muscle rigidity, and loss of balance. Early diagnosis of PD is crucial for better management of the disease, allowing for early initiation of treatment and improved patient outcomes. Currently, there is no definitive test for PD; diagnosis relies primarily on the evaluation of clinical symptoms, which often appear several years after the onset of the disease. This delayed detection limits the potential for early intervention and disease management. As a result, there is a growing need for non-invasive, cost-effective, and reliable methods to detect PD at an early stage. In this study, I propose a novel approach for early PD detection by capturing respiratory breathing patterns during sleep using smartphone-generated ultrasonic rays. I employ a Deep Neural Network (DNN) model combining a convolutional neural network (CNN) and a recurrent neural network (RNN) for the classification of the breathing signals into two classes: PD and control.
Neuroscience · Paper · June 2023
Yashnil Saha
Diagnosing Parkinson's disease is one of the largest challenges healthcare systems face due to the absence of a specific test for the condition and symptoms varying widely from person to person. Designing an automated model to aid in early diagnosis would greatly contribute to solving this problem. Currently, diagnosis for PD relies on clinical evaluation which has an error rate of approximately 20%, indicating the urgent need for an automated system to be developed. Levodopa is used for the treatment of Parkinson's Disease (PD) but can lead to motor complications known as levodopa-induced dyskinesia (LID) when taken for too long. PD and LID are evaluated according to the Unified Parkinson's Disease Rating Scale (UPDRS) and Unified Dyskinesia Rating Scale (UDysRS) scales, respectively, which range from 0 to 4 (0-normal, 4-severely impaired). The tests are conducted by medical personnel and are very subjective. The goal of this project was to design an algorithm using deep learning for assessment of parkinsonism and LID using pose estimation. Two models were created: a regression model to predict the clinical rating from 0 to 4 and a classification model to determine whether the patient had PD or LID. During the feature extraction process, 32 features were extracted per joint trajectory including 15 kinematic, 16 spectral, and the convex hull of the movements. Then, the two neural network models were trained on these features to be able to predict their respective targets. The classification model achieved a mean F1-score greater than 0.8 and the regression model attained a root mean square error less than 0.550, proving that this project was a promising start in the venture to automate diagnosis of Parkinson's disease.