Diagnosis Commons^™

Advances And Challenges In Digitally Connected Point-Of-Care Biosensing, Abdellatif Ait Lahcen, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Point-of-care (POC) biosensors are undergoing a paradigm shift from isolated diagnostic tools to digitally connected, intelligent platforms that enable continuous and decentralized healthcare delivery. This review critically examines recent advances in wearable, implantable, and portable biosensors, highlighting how integration with wireless communication, the Internet of Medical Things (IoMT), and artificial intelligence is transforming their functionality and clinical utility. Particular attention is given to innovations such as smartphone-enabled interfaces, cloud-based analytics, and machine learning-assisted analysis, which collectively enhance sensitivity, specificity, and user accessibility across diverse healthcare settings, from personalized home monitoring and bedside diagnostics to deployment in resource-limited regions. The review …

A Portable Potentiostat Integrated With A Pt/Zno/Lig Electrode For Non-Enzymatic Glucose Detection, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Continuous glucose monitoring is critical for effective diabetes management; however, conventional benchtop potentiostats are bulky, costly, and unsuitable for decentralized point-of-care (PoC) applications. To address these limitations, this work presents a miniaturized, low-cost electrochemical sensing platform integrating a non-enzymatic glucose sensor with a portable potentiostat. The sensing electrode is based on laser-induced graphene modified with zinc oxide and platinum nanostructures via electrodeposition to enable sensitive glucose detection under physiological conditions. A custom-designed portable potentiostat was developed to control electrode potentials and perform electrochemical measurements, and its performance was experimentally validated against a commercial Metrohm system. Glucose detection was evaluated using …

Ai-Assisted Surface-Enhanced Raman Spectroscopy For Cardiovascular Diagnostics: From Plasmonic Materials To Clinical Translation, Anju Joshi, Gymama Slaughter

Center for Bioelectronics Publications

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. …

Electrochemical Hemoglobin Biosensors For Point-Of-Care Diagnostics, Ashwini Dantanarayana, Gymama Slaughter

Center for Bioelectronics Publications

Hemoglobin (Hb) and its glycated variant (HbA1c) are essential clinical biomarkers for diagnosing anemia, acute hemorrhage, and long-term glycemic control. The growing demand for decentralized point-of-care (POC) testing has accelerated the development of rapid, inexpensive, and portable electrochemical sensing platforms. These technologies exploit the intrinsic redox activity of the heme prosthetic group, yet achieving efficient direct electron transfer (DET) remains a fundamental challenge because the electroactive iron center is deeply embedded within the globin structure. This review critically examines recent advances in electrochemical Hb sensing, tracing the evolution of electrode architectures from conventional carbon substrates to nanostructured materials, including graphene, …

Modern Potentiostat Architectures For Electrochemical Sensing: Design, Integration, And Future Directions, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Potentiostats are essential to electrochemical sensing, enabling precise control of electrode potentials and measurement of current responses. As demand grows for portable, wearable, and point-of-care systems, potentiostat design has evolved from benchtop instruments to compact, low-power, and wirelessly connected platforms. This review provides a comprehensive, system-level perspective on modern potentiostat architectures, covering operational principles, analog front-end design, signal generation and acquisition, communication protocols, and software integration. Unlike prior reviews that treat these aspects independently, this work integrates electrochemical theory with electronic design and data communication frameworks. Key components, including operational amplifiers, transimpedance amplifiers, DAC/ADC subsystems, and microcontroller-based control, are examined …

Cold-Sensitive Acral Hand Lesions, Avery H. Seward, Jocelyn H. Larocque

Department of Medicine Faculty Publications

[Case description] An otherwise healthy woman in her 60s presented with a 2-year history of a persistent, itchy, and tender rash on the dorsal hands. The rash fluctuated in severity with the seasons, worsening during the colder months. The patient denied joint pain and fatigue, or lesions elsewhere.

Examination revealed partially blanchable violaceous plaques on the dorsal hands with partial sparing of the proximal and distal interphalangeal joints (Fig 1). A few punctate, crusted erosions and purpuric macules were present on the dorsal and volar distal phalanges (Figs 2 and 3).

Implementation And Clinical Utility Of Ultra-Low-Field Portable Magnetic Resonance Imaging For Postprocedural Neurological Evaluation In Ambulatory Neurosurgery: Illustrative Cases, Devan Patel, Vinay Jaikumar, Taysia P. T. Morioka, Laz Rifkin, Kenneth S. Jacoby, Jaims Lim, Anais Andrade, Aimee C. Degaetano, Pui Man Rosalind Lai, Elad I. Levy

EVMS School of Health Professions Faculty Publications

BACKGROUND

Elective endovascular neurosurgical procedures are increasingly performed in ambulatory neurosurgery centers, enabled by advances in catheter technology, safety of conscious sedation, and refined patient selection. Although complication rates are low, rapid evaluation of postprocedural neurological deficits remains critical. Conventional MRI is often impractical in outpatient or procedural settings, whereas ultra-low-field portable MRI (ULF-pMRI) systems (such as Swoop) allow bedside imaging with favorable diagnostic performance.

OBSERVATIONS

Two women in their 60s developed acute neurological deficits at an ambulatory neurosurgery center (ANSC) after diagnostic cerebral angiography in one case and elective internal carotid artery flow diversion in the other. In both …

Dry Medical Thoracoscopy With Artificial Pneumothorax Induction: A Scoping Review, Nai Chien Huan, Larry Ellee Nyanti, Emilia Sheau Yuin Toh, Kong Meng Tung, Fui Bee Woo, Vikneshwara Shanmugam, Sarvin Vignesh, Y. C. Gary Lee

Research outputs 2022 to 2026

Background: Dry medical thoracoscopy (MT) allows access into the pleural cavity in the absence of effusion; but its role, safety, and techniques remain poorly defined. This scoping review summarises current evidence on indications, procedural approaches, diagnostic yield, and safety of dry MT; and highlights gaps to guide future research. Methods: We conducted a scoping review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. PubMed and Google Scholar databases were systematically searched for original studies reporting on dry MT involving pneumothorax induction. Study selection and data extraction followed the Joanna Briggs Institute …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

Leveraging Cross-Reactivity For Dual Serological Detection Of Closely Related Schistosoma Species, Oyetunde T. Oyeyemi, Lisa M. Shollenberger

Biological Sciences Faculty Publications

Schistosomiasis is a disease of poverty that is highly prevalent in Sub-Saharan African countries. Cross-reacting proteins in Schistosoma mansoni (Sm) and S. haematobium (Sh) have been reported to pose diagnostic challenges. Proteins from these two parasites may help overcome diagnostic limitations caused by cross-reactivity and poor species discrimination in serological assays. The study aims to identify cross-reacting protein markers from S. mansoni and S. haematobium for possible concurrent diagnosis of the two parasites. High-quality protein extracts were prepared from the eggs (soluble egg antigen, SEA) and adult worms (Schistosoma worm antigens preparation, SWAP) of S. haematobium and S. mansoni …

Ensemble Learning For Predicting Periapical Lesions From Dental Radiographs, Fang-Yuan Liao, Ming-Jyun Jian, Abdullah Sarini, Yulianto Jesica, Yen-Kun Lin, Hsun-Yu Huang

Journal of Dental Sciences

Background/purpose: Diagnosing periapical cysts and granulomas using periapical radiographs is challenging due to subtle radiographic differences. This study aimed to develop a machine learning–based diagnostic framework to improve lesion classification through ensemble learning and dentin standardization.

Materials and methods: Five models—Support Vector Classifier (SVC), Nu-SVC, K-Nearest Neighbors (KNN) and Decision Tree—were trained on 144 pre-treatment periapical radiographs (70 cysts and 74 granulomas). Dentin standardization normalized grayscale values to ensure feature consistency. A weighted soft voting strategy was employed to integrate predictions, with model weights derived from individual diagnostic performance. Model performance was evaluated via five-fold cross-validation. Statistical significance among models …

Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization, Marwa Talal

Theses and Dissertations

Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome.

Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature …

Advances In Survival Analyses: Machine Learning Methods And Model Comparison, Ryan Gately, Dharshana Sabanayagam, Wai H. Lim, Lin Zhu, Farzaneh Boroumand, Shuvo Bakar, Armando Teixeira-Pinto, Germaine Wong

Research outputs 2022 to 2026

This is the second article in a 2-part series on survival analysis. In part 1, we discussed the core concepts and traditional methods used in survival analysis. Part 2 explores the novel approaches to predict survival outcomes and evaluate model performance. To facilitate hands-on learning and practical implementation, the R code used in these analyses is provided in the supplementary materials, along with instructions to help readers apply these methods to their data.

Importance Of Placental Evaluation In Pregnancies At High Risk For Placenta Accreta Spectrum: Expert Clinical Perspective, Rebecca H. Jessel, Alesha White, Hope Y. Yu, Amie L. Hollard, Meena Khandelwal, Carolyn M. Zelop, Jennifer Philips, Alireza A. Shamshirsaz, John C. Kingdom, Alfred Abuhamad, Christina L. Herrera, Pan American Society For Placenta Accreta Spectrum (Pas2)

Department of Obstetrics & Gynecology Faculty Publications

Background

Placenta accreta spectrum (PAS) is a leading cause of obstetric morbidity and peripartum hysterectomy. Rising cesarean delivery rates, advanced maternal age, and assisted reproductive technologies have increased its incidence. Early, standardized diagnosis is essential for multidisciplinary planning and improved outcomes, yet formal screening guidelines are lacking.

Objective

To raise awareness of the importance of antenatal screening for PAS, summarize key clinical and imaging risk factors, and propose a standardized mid-trimester ultrasound protocol for high-risk patients.

Methods

An expert panel convened under the Pan-American Society for the Placenta Accreta Spectrum (PAS2) reviewed available evidence, risk stratification models, and prior consensus …

Cervicovaginal Lavage Fluid Iron And Manganese Levels In Women With And Without Vaginitis, Barbara Kozma, David Ratonyi, Bence Kozma, Peter Takacs, Attila G. Sipos

Department of Obstetrics & Gynecology Faculty Publications

Transition metals such as iron and manganese are critical for cellular metabolism, redox balance, and immune function, and they also influence host–pathogen interactions. Vaginitis is characterized by mucosal inflammation that may alter local trace-element homeostasis. We hypothesize that inflammatory conditions such as vaginitis may be associated with altered cervicovaginal iron and manganese levels. We performed a prospective, single-center cohort study at the University of Debrecen (Hungary) between April 2023 and April 2024, enrolling 80 women ≥ 18 years with vulvovaginal complaints. Participants were assessed at baseline and at weeks 4, 8, and 12 with clinical evaluation and cervicovaginal lavage (CVL) …

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

Multimodal Machine Learning For Alzheimer's Disease Classification Using Adni Biomarker Fusion, Hesameddin Mostaghimi, Hamid R. Okhravi, Bahar Niknejad, Daniel A. Cohen, Michel A. Audette, Alzheimer's Disease Neuroimaging Initiative

Electrical & Computer Engineering Faculty Publications

Despite ongoing advances, accurate diagnosis of Alzheimer’s disease (AD) remains challenging due to its multifactorial nature, comorbidities, and clinical heterogeneity. Accordingly, approaches that combine multimodal data may improve AD classification by integrating complementary information. To investigate this, we evaluated classification performance using a preprocessed ADNI-3 dataset comprising a shared set of clinical/cognitive features along with four imaging modality-based cohorts: trimodal (MRI + amyloid PET + tau PET), MRI + amyloid PET, MRI + tau PET, and MRI-only. We trained a range of supervised machine learning (ML) and deep learning (DL) classifiers using stratified five-fold cross-validation and evaluated performance using accuracy, …

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 …

The Taper Sign: Sonographic Representation Of Uterine Dehiscence In Severe Cases Of Placenta Accreta Spectrum, Uma Deshmukh, Anna M. Modest, Alfred Abuhamad, Alireza A. Shamshirsaz, Jonathan L. Hecht, Scott A. Shainker

Department of Obstetrics & Gynecology Faculty Publications

Introduction: Prenatal diagnosis of placenta accreta spectrum (PAS) depends on reliable ultrasound markers and skilled image interpretation. Existing PAS markers emphasize placental features suggestive of abnormal adherence and require advanced image optimization. However, emerging hypotheses suggest that uterine dehiscence—not placental invasion—is the central mechanism underlying PAS, particularly in severe (Grade 3) disease. Despite this, few established ultrasound markers address this uterine dehiscence. We describe a novel ultrasound marker, the taper sign, which uniquely represents the uterine dehiscence in severe PAS with anterior placenta previa, and outline a standardized technique for image acquisition and interpretation.

Methods: This descriptive report details the …