Biomedical Commons^™

Visual Question Answering: A Survey, Gehad Assem El-Naggar

Future Computing and Informatics Journal

Visual Question Answering (VQA) has been an emerging field in computer vision and natural language processing that aims to enable machines to understand the content of images and answer natural language questions about them. Recently, there has been increasing interest in integrating Semantic Web technologies into VQA systems to enhance their performance and scalability. In this context, knowledge graphs, which represent structured knowledge in the form of entities and their relationships, have shown great potential in providing rich semantic information for VQA. This paper provides an abstract overview of the state-of-the-art research on VQA using Semantic Web technologies, including knowledge …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Approximate And Sample Entropy Of Center Of Pressure In Unperturbed Tandem Standing: Contribution Of Embedding Dimension And Tolerance, Jayla Mashae Wesley

Masters Theses

Approximate entropy (ApEn) and sample entropy (SampEn) are statistical methods designed to quantify the regularity or predictability of a time series. Although ApEn has been a prominent choice for use, it is currently unclear as to which method and parameter selection combination is optimal for its application in biomechanics. The goal of this thesis was to examine the difference between ApEn and SampEn related to center of pressure (COP) data during standing balance tasks, while also refining tolerance r, to determine entropy optimization. Six participants completed five 30-second, feet together and tandem standing, trials under eyes-open and eyes-closed conditions. Ground …

Dense & Attention Convolutional Neural Networks For Toe Walking Recognition, Junde Chen, Rahul Soangra, Marybeth Grant-Beuttler, Y. A. Nanehkaran, Yuxin Wen

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait disorder where children’s initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are …

Portable Diffuse Reflectance Spectroscopy For Non-Invasive And Quantitative Assessment Of The Parathyroid Glands Viability During Surgery, Mark Romine, Linh Luong, Alex Moazzen, Katie Cho, Paul Lee

Symposium of Student Scholars

Portable Diffuse Reflectance Spectroscopy for Non-invasive and Quantitative Assessment of the Parathyroid Glands Viability During Surgery

Mark Romine, Linh Luong, Alex Moazzen, Katie Cho and Paul Lee

The parathyroid glands (PTGs) are responsible for the regulation of calcium levels in the blood by secreting a parathyroid hormone. This parathyroid hormone then regulates the body’s absorption, storage, and secretion of calcium, which can directly affect the way muscles and nerves operate. PTGs are often at risk of damage, or accidental removal during thyroid surgeries, because it is challenging to identify PTGs and to determine their viability. Current methods of visual inspections …

Wireless, Handheld Diffuse Reflectance Spectroscopy To Quantify Tissue Microvascular Hemodynamics, Linh Luong, Alex Moazzen, Mark Romine, Katie Cho, Paul Lee

Symposium of Student Scholars

Diffuse Reflectance Spectroscopy (DRS) is a non-invasive optical method to characterize tissue optical properties for disease diagnosis and health monitoring. Two optical fibers are often used in a DRS system: one to deliver light to the tissue and the other to gather diffuse reflectance spectra, which provide quantitative details about the structure and composition of the tissue. The conventional DRS system, however, is expensive, bulky, and composed of fragile optical fibers and multiple electrical connections. Here we propose to build a wireless, handheld, and fiber-less diffuse optical spectroscopy system. Unfortunately, the diffusion approximation utilized for data analysis of the conventional …

Computational Design Of Fiber-Optic Probes For Biosensing, Suwarna Karna

Electrical Engineering Theses

This thesis presents a study on the optical characteristics of hollow-core photonic crystal fibers (HC-PCFs) with a band gap cladding structure and their applications in optical fiber sensing. This 800B HC-PCF exhibited excellent optical properties and has a flexible structure, which makes them suitable for a wide range of industrial applications. Finite element simulations and structural optimization designs were conducted using the surface plasmon resonance (SPR) technique to determine the optimal performance parameters of the 800B HC-PCF. The fiber was further modified using the SPR technique to improve its practical detection capabilities. The performance of the modified fiber was observed …

Split And Join: An Efficient Approach For Simulating Stapled Intestinal Anastomosis In Virtual Reality, Di Qi, Suvranu De

Engineering Faculty Articles and Research

Colorectal cancer is a life-threatening disease. It is the second leading cause of cancer-related deaths in the United States. Stapled anastomosis is a rapid treatment for colorectal cancer and other intestinal diseases and has become an integral part of routine surgical practice. However, to the best of our knowledge, there is no existing work simulating intestinal anastomosis that often involves sophisticated soft tissue manipulations such as cutting and stitching. In this paper, for the first time, we propose a novel split and join approach to simulate a side-to-side stapled intestinal anastomosis in virtual reality. We mimic the intestine model using …

Dataset For Effects Of Single-Session Practice Structure On Motor Skill Acquisition And Alpha And Beta Eeg Oscillations, Audrey Porter, Ronald V. Croce, Wayne Smith

Faculty Publications

Although it is known that practicing a motor skill updates the associated internal model, it is still unclear as to how cortical oscillations linked with the motor skill change under differing practice schedules. The current study investigated α- and β-power changes associated with motor skill acquisition. Firstly, we investigated the behavioral effects of practice on motor learning and retention during repetitive (RP) and variable (VP) practice schedules on an anticipation timing task. Secondly, we investigated changes in cortical α (10-13 HZ) and β (15-30 Hz) event-related synchronization and dyssynchronization (ERS/ERD) under RP and VP during early (EP) and late …

Heart Disease Prediction Using Stacking Model With Balancing Techniques And Dimensionality Reduction, Ayesha Noor, Nadeem Javaid, Nabil Alrajeh, Babar Mansoor, Ali Khaqan, Safdar Hussain Bouk

School of Cybersecurity Faculty Publications

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the …

Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)

Electrical & Computer Engineering Faculty Publications

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

An Xai Approach For Covid-19 Detection Using Transfer Learning With X-Ray Images, Salih Sarp, Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali, Huseyin Kusetogullari, Yanxiao Zhao, Gungor Ates, Ozgur Guler

Engineering Technology Faculty Publications

The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use …

Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …

Virtual Surgical Planning In Craniomaxillofacial Surgery: A Structured Review, Kaye Verlarde, Rentor Cafino, Armando Isla Jr., Karen Mae Ty, Xavier-Lewis Palmer, Lucas Potter, Larry Nadorra, Luchin Valrian Pueblos, Lemuel Clark Velasco

Electrical & Computer Engineering Faculty Publications

Craniomaxillofacial (CMF) surgery is a challenging and very demanding field that involves the treatment of congenital and acquired conditions of the face and head. Due to the complexity of the head and facial region, various tools and techniques were developed and utilized to aid surgical procedures and optimize results. Virtual Surgical Planning (VSP) has revolutionized the way craniomaxillofacial surgeries are planned and executed. It uses 3D imaging computer software to visualize and simulate a surgical procedure. Numerous studies were published on the usage of VSP in craniomaxillofacial surgery. However, the researchers found inconsistency in the previous literature which prompted the …

Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides

Electrical & Computer Engineering Faculty Publications

This editorial introduces the JMI Special Section on Artificial Intelligence for Medical Imaging in Clinical Practice.

An Optimized Deep Learning-Based Framework For Predicting Diabetes Mellitus Using Ffnn, Norhan S. Elmongy, Sally M. Elghamrawy, Amr M. T. Ali-Eldin, Ali I. Eldesouky

Mansoura Engineering Journal

Diabetes mellitus (DM) is a major public health problem in Egypt, and the illness is regarded as a contemporary epidemic across the world. Diabetes is becoming more common, which is a cause for serious concern. As a result, precise and timely identification of the illness is critical. Health and research institutions have also recently expressed a serious interest in developing and implementing cutting-edge healthcare systems. Therefore, it is necessary to accurately and quickly identify the condition. To solve this issue, scientific research has been carried out, but the outcomes have fallen short. Four layers make up the proposed Diabetes mellitus …

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Dfhic: A Dilated Full Convolution Model To Enhance The Resolution Of Hi-C Data, Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Hi-C technology has been the most widely used chromosome conformation capture(3C) experiment that measures the frequency of all paired interactions in the entire genome, which is a powerful tool for studying the 3D structure of the genome. The fineness of the constructed genome structure depends on the resolution of Hi-C data. However, due to the fact that high-resolution Hi-C data require deep sequencing and thus high experimental cost, most available Hi-C data are in low-resolution. Hence, it is essential to enhance the quality of Hi-C data by developing the effective computational methods.

Results: In this work, we propose …

Machine Learning For Biosensors, Gayathri Anapanani

Graduate Theses, Dissertations, and Problem Reports

Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and …