Biomedical Commons^™

Consensus And Controversies Of International Guidelines For The Diagnosis, Surveillance, And Management Of Fetal Growth Restriction: An Updated Comparison, Daniele Diane Mascio, Suneet P. Chauhan, Tullio Ghi, Asma Khalil, Juliana G. Martins, Sara Sorrenti, Tamara Stampalija, Fabrizio Zullo, Francesc Figueras

Department of Obstetrics & Gynecology Faculty Publications

OBJECTIVE: To compare areas of consensus and disagreements across contemporary international and national guidelines on the diagnosis, surveillance, and management of fetal growth restriction (FGR).

DATA SOURCES: Electronic searches of MEDLINE from database inception up to March 2026 using MeSH terms and keywords related to FGR and guidelines. STUDY ELIGIBILITY CRITERIA: Critical, structured comparison of national or international guidelines on FGR published since 2010. Final inclusion required unanimous agreement from all authors.

STUDY APPRAISAL AND SYNTHESIS METHODS: Pre-specified extraction across domains: definition; prediction/prevention; surveillance tools and frequency; delivery timing and mode; and labor induction methods. Dual data …

C. Difficile Detection Method For First Responder Glove Application, Alli N. Senedak

Williams Honors College, Honors Research Projects

Clostridioides Difficile (C. diff) is a Anaerobic Gram-positive bacillus that is capable of spore formation, making it difficult to control its spread and duration in clinical environments. This phenomenon can provide danger to first responders, healthcare workers, and patients. The goal of this project is to create a biosensor capable of detecting C. diff in a clinical setting that can be applied to a glove apparatus. The project will involve the use of C. diff aptamers activated on the surface of an electrode. Once surface activation has been verified via surface analysis, the electrodes will be exposed to C. diff …

Beyond The Square Pulse: Waveform Shape, Eeg Correlates, And The Pursuit Of Natural Sensation In Tens, Jason Whitson

Honors Undergraduate Theses

This study investigates the relationship between electrical stimulus characteristics of shape and charge on evoked sensations and electroencephalogram (EEG) data during multi-waveform transcutaneous electrical nerve stimulation (TENS) of the median nerve. Neuromodulation methods have traditionally had little control over the location and quality of their associated evoked sensation (e.g., electric, vibration, touch). This experiment utilized five unique stimulus waveforms during TENS stimulation. EEG data were collected concurrently to provide an introductory objective measure of the neural responses underlying these sensory changes. Eleven participants completed three tasks (thresholding, super-threshold stimulation, two-alternative forced choice) using a two-electrode TENS approach. Stimulus waveforms were …

Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data, Muath Alsuhaibani

Electronic Theses and Dissertations

Cognitive impairment detection is on the rise to help reduce the burden of healthcare costs on institutions and individuals. Mild Cognitive Impairment (MCI) is an early stage of cognitive decline progressing to Alzheimer’s disease (AD) or AD-related Dementia (ADRD). Detecting the early stages of AD/ADRD is crucial for early interventions among older adults to mitigate cognitive decline over time. However, the current diagnostic methods are often costly and/or invasive, such as MRI and PET scans. Thus, the search for non-invasive and cost-effective screening tools for the early detection of cognitive impairment using speech, language, visual, and motor data is growing. …

Study Of Ai Applications In Biomedical Data Acquisition, Communication, And Analysis: Cest Mri Acceleration And Ecg Transmissions, Adarsha Bhattarai

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation investigates the application of artificial intelligence in biomedical data acquisition, communication, and analysis to advance neurological research and to enable the early detection of cardiovascular conditions. Despite significant advances in imaging and physiological modalities, challenges persist. Imaging modalities, such as the chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) technique are challenged by a prolonged data acquisition time and high operational costs. In addition, physiological modalities such as electrocardiogram (ECG) sensors face constraints in providing uninterrupted signal monitoring which is crucial for the timely detection of premature cardiac abnormalities. The primary goal of this work is to …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Cochlear Electrode Insertion Training Model, Sarah Powell, Kaelyn E. Kraley, Nathan J. Smith

Williams Honors College, Honors Research Projects

Cochlear implant surgery is a delicate procedure performed by Otolaryngologists (ENTs) to implant an electronic device into the inner ear to provide a sense of sound for people who are profoundly deaf or hard of hearing. The current practices of training involve cadavers and 3D-printed models. Cadavers are commonly used but are expensive, single-use, and do not provide visual and haptic feedback, which are essential for medical students. 3D printed models are less commonly used and are hard to fabricate and not as realistic. If medical students are not properly trained for this delicate procedure, then risks are significantly increased …

Identification Of Fiducial Points In Seismocardiographic Cycles Using Manual And Automated Annotation Methods, Jasmine-Vy T. Truong

Honors Undergraduate Theses

There is currently a need for complementary methods for non-invasive cardiac monitoring. Seismocardiography (SCG), the measurement of cardiac-induced vibrations at the chest surface, has shown potential clinical utility. Improving the reliability of detecting fiducial points of electrocardiography (ECG) and SCG, which collectively capture the electro-mechanical cardiac activities, could expand ECG/SCG utility as a low-cost, accessible tool for clinical assessment. This study identifies commonly accepted criteria for fiducial point detection in SCG and ECG through an extensive literature review and signal processing techniques. The previous criteria were evaluated to identify their strengths and weaknesses. Based on the findings, an improved set …

Advanced Nanocomposite-Based Electrochemical Sensor For Ultra-Sensitive Dopamine Detection In Physiological Fluids, Megha Shinde, Gymama Slaughter

Center for Bioelectronics Publications

This study presents a novel point-of-care electrochemical sensor for dopamine (DA) detection, featuring a flexible laser-induced graphene (LIG) modified with a unique nanocomposite comprising Nb₄C₃T_x MXene, polypyrrole (PPy), and iron nanoparticles (FeNPs). The LIG-Nb₄C₃T_x MXene-PPy-FeNPs is characterized by scanning electron microscopy to confirm the successful surface modification. The electrochemical performance of the fabricated sensor via cyclic voltammetry showed significant electrochemical activity upon Nb₄C₃T_x MXene-PPy-FeNPs nanocomposite modification of the LIG surface with an increased peak anodic current (Ipa) from 43 μA to 104 μA. The sensor …

Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

Electrical & Computer Engineering Faculty Publications

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …

Use Of Electrical Bone Growth Stimulators In High-Risk Patients Following Spinal Fusion, Soumya Malhotra, Khavir Sharieff

HCA-NSU MD Research Day

Title: Use of Electrical Bone Growth Stimulators in High-Risk Patients Following Spinal Fusion Authors: Soumya Malhotra, BA., MS1 & Khavir Sharieff, DO, MBA2 1Class of 2027, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Fort Lauderdale, FL 2Assistant Professor of Surgery, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Tampa Bay, FLNova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine Objective: This study aimed to elucidate the advantages of EBGSs following high-risk spinal fusions. Background: Pseudarthrosis is a condition where bones fail to fuse after an injury or surgery. It causes significant …

Precision Medicine For Apical Lesions And Peri-Endo Combined Lesions Based On Transfer Learning Using Periapical Radiographs, Pei Yi Wu, Yi Cheng Mao, Yuan Jin Lin, Xin Hua Li, Li Tzu Ku, Kuo Chen Li, Chiung An Chen, Tsung Yi Chen, Shih Lun Chen, Wei Chen Tu, Patricia Angela R. Abu

Ateneo Laboratory for Intelligent Visual Environments

An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. …

Evaluation Of The Alveolar Crest And Cemento-Enamel Junction In Periodontitis Using Object Detection On Periapical Radiographs, Tai Jung Lin, Yi Cheng Mao, Yuan Jin Lin, Chin Hao Liang, Yi Qing He, Yun Chen Hsu, Shih Lun Chen, Tsung Yi Chen, Chiung An Chen, Kuo Chen Li, Patricia Angela R. Abu

Ateneo Laboratory for Intelligent Visual Environments

The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth’s bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with …

Auxiliary Diagnosis Of Dental Calculus Based On Deep Learning And Image Enhancement By Bitewing Radiographs, Tai Jung Lin, Yen Ting Lin, Yuan Jin Lin, Ai Yun Tseng, Chien Yu Lin, Li Ting Lo, Tsung Yi Chen, Shih Lun Chen, Chiung An Chen, Kuo Chen Li, Patricia Angela R. Abu

Ateneo Laboratory for Intelligent Visual Environments

In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 …

Towards A Wearable Device For Measuring Impedance Plethysmography Of The Radial Artery, Pritom Chowdhury

Dartmouth College Master’s Theses

Recent advancements in bioimpedance technology have demonstrated significant promise in the application of cardiac health monitoring. This research explores the design and development of a forearm-based wearable bioimpedance device for non-invasive measurement of heart rate and respiratory rate at an accuracy level comparable to medical-grade monitors. It utilizes a tetrapolar electrode configuration to analyze bioimpedance changes in the radial artery due to blood flow.

An ongoing aspect of this work involves the preliminary development of an embedded framework intended to integrate signal generation, acquisition, and processing within the device to achieve compact and efficient system design, anticipated to contribute to …

Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema, Amy Prendergast

Honors Theses and Capstones

Breast Cancer Related Lymphedema (BCRL) is a common co-morbidity in cancer survivors following neoadjuvant therapies such as chemotherapy, radiation, and/or surgery. It is brought about by the disruption in the lymphatic system (think lymph node biopsy) that leads to a buildup of lymphatic fluid in the arm. Current diagnostic strategies for this condition are merely retroactive, and fairly limited in the parameters that are examined to ensure patient well-being long term. We hypothesize that with an approach that mimics bioimpedance spectroscopy analysis, we will be able to provide a clinical support tool that would better determine early stages of lymphedema …

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

A new lossless video compression technique, Match, is investigated. Match uses the similarity between the frames of a video or the slices of medical images to find a prediction for the current pixel. A portion of the previous frame is searched to find a matching context, which is the pixels surrounding the current pixel, within some distance centered on the current location. The best distance to use for each dataset is found experimentally. The matching context refers to the neighborhood of w, nw, n, and ne, where the pixel in the previous frame with the closest matching context becomes the …

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 …

Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …

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 …

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 …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Creating Reel Designs: Reflecting On Arthrogryposis Multiplex Congenita In The Community, Iris Layadi

Purdue Journal of Service-Learning and International Engagement

Because of its extreme rarity, the genetic disease arthrogryposis multiplex congenita (AMC) and the needs of individuals with the diagnosis are often overlooked. AMC refers to the development of nonprogressive contractures in disparate areas of the body and is characterized by decreased flexibility in joints, muscle atrophy, and developmental delays. Colton Darst, a seven-year-old boy from Indianapolis, Indiana, was born with the disorder, and since then, he has undergone numerous surgical interventions and continues to receive orthopedic therapy to reduce his physical limitations. His parents, Michael and Amber Darst, have hopes for him to regain his limbic motion and are …

Who Will Be Liable For Medical Malpractice In The Future? How The Use Of Artificial Intelligence In Medicine Will Shape Medical Tort Law, Scott J. Schwelkart

Minnesota Journal of Law, Science & Technology

No abstract provided.

Simultaneous Wound Border Segmentation And Tissue Classification Using A Conditional Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Manisa Pipattanasomporn, Ozgur Guler

Engineering Technology Faculty Publications

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images …

Left Atrial Model, Borna Sobati, Sarah Porello, Tess Pate

Biomedical Engineering

The objective is to produce an electrophysiological model of an adult human left atrium. This model will be used to test mapping probe catheters used for locating cardiac arrhythmias against current technology used in practice. Dr. Chris Porterfield requested this model and other physicians or probe catheter manufacturers may also use this product in the future. Dr. Porterfield also discussed the possibility of future senior project groups using the model as a bench test for designing new catheter tips. The model will precisely simulate electrical behaviors of the heart in normal as well as arrhythmic conditions. Ideally, the model will …

Power Mobility Sensor Data Collection Verified Through Standardized Pediatric Assessments, Ayshka Elise Rodriguez-Velez

UNF Graduate Theses and Dissertations

The collaboration between the School of Engineering and the Department of Physical Therapy at the University of North Florida has introduced the possibility of creating a new environment for pediatric physical therapy assessments. There are currently no methods for remotely monitoring children with impairments. However, with embedded sensor technology in the form of power mobility and accepted therapy assessment tools, remote monitoring can become a possibility. As a part of this work, a ride-on toy car was developed as a remote monitoring device and a case study with a child with a mobility impairment was used as a proof of …