Computer Sciences Commons^™

Evolving Solutions For Red Blood Cell Preservation, Ali Alkafaji, Charles A. Elder, Mohammad Zaidi, Kavin Parthiv, Michael A. Menze

The Cardinal Edge

In emergencies such as natural disasters, armed conflicts, or during outer space missions, the availability of transfusable blood can mean the difference between life and death. Red blood cells (RBCs) must be stored at +4 ± 2 °C and have a shelf life of just 42 days, which makes maintaining a stable blood supply during adverse conditions extraordinarily challenging. This challenge was especially apparent during the COVID-19 pandemic when hospitals faced severe blood shortages. Freeze-drying, or lyophilization, offers a promising avenue to extend the shelf life of RBCs for transfusion during crises. However, a significant hurdle in dry preservation is …

Impediments To Transforming The Healthcare Delivery System: Shifting The Paradigm From Provider Centric To Patient Centric, Elizabeth A. Regan, Manasa Devi Chinta

Faculty Publications

Introduction: 

Stated aims for digital healthcare transformation frequently cite goals for better coordinated patient-centric systems. However, despite advances in medical science, digital technologies, health policies, and billions of dollars invested over the past 25 years, most healthcare providers are far from fully realizing the demonstrated benefits of today's digital technologies for improving patient care. Sharing information across healthcare systems remains challenging. Problems with fragmentation, quality, inequities, and rising costs of care delivery persist. A recent study of 1,026 U.S. hospital systems found that only 15.8 percent achieved a digital maturity level needed to provide digitally enabled healthcare services to better …

An Ai Approach To Differentiating Lung Squamous Cell Carcinoma From Metastases Of Other Origins, Mark G Evans, Jennifer Ribeiro, Todd Maney, Anthony Helmstetter, Jennifer Johnson, Anthony Karnezis, Casey Bales, George Sledge, David Spetzler, Ari Vanderwalde, Matthew Oberley, Balazs Halmos, Hossein Borghaei, Farah Abdulla, David Bryant, Fred Hirsch, Hassan Ghani

Department of Medical Oncology Faculty Papers

IMPORTANCE: Distinguishing primary lung squamous cell carcinoma (SCC) from squamous metastases to the lung is a clinical challenge due to histopathologic similarities. Accurate diagnosis is essential to guide treatment decisions.

OBJECTIVE: To assess the utility of an artificial intelligence (AI) approach that includes evaluation of key orthogonal evidence in distinguishing primary lung SCCs from metastatic tumors of other tissue origins.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used GPSai, a tissue-of-origin AI model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung SCC. Molecularly profiled cases within the Caris Life …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Homogeneous Syringe-Sharing Network, Seun Ale, Que Thi Nguyet Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model assumes homogeneous mixing among syringe-sharing agents, without any form of heterogeneity in the agents interactions or syringe-sharing attitude. All syringe-sharing PWID are treated as identical in terms of their interaction frequency and syringe-sharing probability. Interactions are generated dynamically using proximity-based sampling at each timestep (one day), allowing agents to form syringe-sharing interactions based on spatial closeness. The number of daily interaction events is fixed at the population level, and each syringe-sharing agent has the same probability …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission With Inter- And Intra-Group Structural And Behavioural Heterogeneous Syringe-Sharing Networks (5), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates with additional intra-group variability. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned syringe-sharing probabilities that vary at the individual level around their …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Two-Group Structural Heterogeneous Syringe-Sharing Network (M2), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model extended a baseline homogeneous model by incorporating structural heterogeneity via a two-group interaction framework. The syringe-sharing population in the model is divided into inner and outer circle groups representing individuals with differing levels of syringe-sharing interaction intensity. While all agents share the same syringe-sharing probability and epidemiological processes remain identical across agents, the number of daily interaction opportunities differs between the two groups. Interactions in the model are generated dynamically using proximity-based sampling at each timestep …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Three-Group Structural Heterogeneous Syringe-Sharing Network (M3), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. While the syringe-sharing rate and all epidemiological processes remain identical across agents, the number of daily interaction opportunities differs by agent grouping, capturing variation in structural position within the syringe-sharing network. Interactions are generated dynamically using proximity-based sampling at each …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission With Inter-Group Structural And Behavioural Heterogeneous Syringe-Sharing Networks (M4), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned distinct syringe-sharing probabilities, reflecting variation in risk-taking behaviour across structural groups within the syringe-sharing …

Brain Computer Interfaces: Enhancing Low-Cost Eeg Performance Through Deep, Anwar Rassoul

Shelby Hall Graduate Research Forum Posters

The field of Brain Computer Interfacing (BCI) has traditionally been confined to clinical and research environments due to the high cost and complexity of medical-grade EEG systems. However, the emergence of low-cost hardware exemplified has catalyzed a shift toward accessible, portable BCI applications. While these devices lower the barrier to entry for developers and researchers, they often suffer from a lower signal-to-noise ratio (SNR). This increased noise makes it difficult to extract the clean neural signatures required for high-accuracy control, particularly when operating in non-shielded, real-world environments.

This research focuses on Steady-State Visually Evoked Potentials (SSVEP), a robust BCI paradigm …

Algorithmic Opacity In Opioid Risk Scoring And The Need For Transparent Ai Regulation, Sherry Yun Wang, Ryan Stofer, Zhouzhou Chu, Xiao Huang, Ang Li

Pharmacy Faculty Articles and Research

NarxCare®, a proprietary opioid risk scoring system embedded in Prescription Drug Monitoring Programs (PDMPs), has generated significant patient complaints. We adhered to the technical specifications and applied them to PDMP and IQVIA PharMetrics® Plus Closed Health Plan claims database. Despite adding socioeconomic covariates, precision (0.01–0.32) was far below the reported benchmark of 0.75, and F1 scores (0.02–0.39) were also substantially lower than the benchmark value of 0.65, across all our reconstructed models.

Pediatric Inflammatory Bowel Disease Tissue Classification From Pathology Slide Images: Detecting Phenotypes Using Computer Vision, Chloe Martin-King, Ali Nael, Louis Ehwerhemuepha, Blake Calvo, Quinn Gates, Jamie Janchoi, Elisa Ornelas, Melissa Perez, Andrea Venderby, John Miklavcic, Peter Chang, Aaron Sassoon, Brian Rubio, Ghislaine Barrigan, Kenneth Grant

Food Science Faculty Articles and Research

Background and Aims

Methods

Results …

Ai Scribe Use In Residency Training: A Call For Specialty Society Guidance In Graduate Medical Education, Julia A. Giordano, Elizabeth Jones

Department of Dermatology and Cutaneous Biology Faculty Papers

Artificial intelligence (AI) is increasingly used for documentation purposes in clinical practice, yet guidance for resident use is limited. Given the substantial documentation burden on medical trainees, AI-powered scribing tools may offer benefits, but their integration into residency training raises educational, supervisory, and patient safety considerations. This study aimed to assess the availability of resident-specific guidance on AI scribe use from major medical and specialty organizations and to summarize current evidence on AI scribes in residency. We reviewed five major medical and specialty society websites (AAD, AMA, ACGME, AAMC, ABMS) via website searches and direct emails and conducted a PubMed …

Fedda-Tsformer: Federated Domain Adaptation With Vision Timesformer For Left Ventricle Segmentation On Gated Myocardial Perfusion Spect Image, Yehong Huang, Chen Zhao, Rochak Dhakal, Min Zhao, Guang-Uei Hung, Zhixin Jiang, Weihua Zhou

Michigan Tech Publications

BACKGROUND: Accurate assessment of left ventricular function is essential for diagnosing and managing cardiovascular disease. Gated myocardial perfusion SPECT (MPS) enables simultaneous evaluation of perfusion and function, but reliable contour extraction is challenged by image noise, resolution limits, and anatomical variability. Multi-center validation is further restricted by data privacy concerns, underscoring the need for robust and privacy-preserving contouring methods. METHODS: In this study, we propose a novel approach, FedDA-TSformer, which integrates Federated Domain Adaptation with the TimeSformer model for the task of left ventricle segmentation using MPS images. The proposed model captures spatial and temporal features through a Divide-Space-Time-Attention mechanism, …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

Detecting Stigmatizing Language In Clinical Notes With Large Language Models For Addiction Care, Rohan Sethi, John Caskey, Yanjun Gao, Matthew M. Churpek, Timothy A. Miller, Anoop Mayampurath, Elizabeth Salisbury-Afshar, Majid Afshar, Dmitriy Dligach

Computer Science: Faculty Publications and Other Works

Intensive care units (ICU) produce numerous progress notes that may contain stigmatizing language that perpetuate negative biases and punitive approaches against patients. Patients with substance use disorders are particularly vulnerable to stigma. This study examined the performance of Large Language Models (LLMs) in the identification of stigmatizing language. We annotated a dataset with over 77,000 stigmatizing and non-stigmatizing notes from the MIMIC-III database. We utilized Meta's Llama-3 8B Instruct LLM to run the following experiments for stigma detection: zero-shot; in-context learning; in-context learning with a selective retrieval; supervised fine-tuning (SFT); and keyword search. All approaches were evaluated on a held-out …

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

Evaluation Of Large Language Models As Decision Support Tools For Head And Neck Cancer Management: A Blinded Multidisciplinary Simulation Study, Sholem Hack, Ron J. Karni, Antonino Maniaci, Christopher E. Fundakowski, Luca Castellani, Fabiola Incandela, Remo Accorona, Miguel Mayo-Yanez, Martina Violati, Lorenzo Giannini, Niccolo' Mevio, Alberto Maria Saibene

Department of Otolaryngology - Head and Neck Surgery Faculty Papers

BACKGROUND: The management of head and neck cancer relies on multidisciplinary expertise; however, access to tumor boards remains variable. Large language models (LLMs) may support guideline-based decision-making, although performance in complex oncologic scenarios is not well defined.

METHODS: Fourteen synthetic cases based on real tumor board encounters were evaluated. Five blinded comparator arms produced recommendations: a human expert, Non-RAG-GPT-4, Non-RAG-GPT-5, RAG-GPT-4, and RAG-GPT-5. Eight head and neck oncologic surgeons scored each recommendation for appropriateness, clarity, specificity, and feasibility using 5-point Likert scales. Paired permutation testing and inter-rater reliability were assessed.

RESULTS: LLM outputs showed close alignment with expert recommendations. RAG-based …

A Virtual-Reality-Based Dental Simulator For Endodontics With Automated Formative Feedback, Yousef Salah Abo El Ela

Theses and Dissertations

Advancements in virtual reality (VR) and haptic technology are transforming the landscape of medical and dental education, offering new avenues for safe, immersive, and repeatable training experiences. Within dentistry, endodontics presents unique challenges for preclinical education due to anatomical complexity, limited access to extracted teeth, ethical concerns, and the shortcomings of conventional plastic models. Despite endodontics specific plastic teeth being available, they fall short of replicating the hardness of real extracted teeth, are relatively costly compared to generic plastic teeth, and are ultimately a disposable item which makes them inadequate as a sustainable long-term solution. Extracted teeth do a much …

Use Of Electrocardiograms To Identify Coronary Artery Disease: Cross-Validation Of An Artificial Intelligence Model, Michael Leasure, Indu Poornima, Adam Butchy, Utkars Jain, Devin Vasoya, Michael Warnick, Brent Williams, John Rehder, Prahlad Menon, Veronica A. Covalesky, Gary S. Mintz

Department of Medicine Faculty Papers

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG).

OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant CAD in a patient population presenting for coronary angiography.

METHODS: From 2019 to 2021, 16,476 patients had a resting 12-lead digital ECG recorded within 90 days prior to coronary angiography. The artificial intelligence model was developed using 10-fold cross-validation methodology. Clinically significant disease was defined as angiographic …

From Latent Manifolds To Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework For Family-Based Kinase Ligand Design, Gennady M. Verkhivker, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and …

Confluence, Vol. 4, Iss. 2, Full Issue

The Confluence

No abstract provided.

Utilizing Machine Learning Techniques For Computer-Aided Covid-19 Screening Based On Clinical Data, Honglun Xu, Andrews T. Anum, Michael Pokojovy, Sreenath Chalil Madathil, Yuxin Wen, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Scott Moen, Eric Walser

Engineering Faculty Articles and Research

The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as …

Task-Optimized Brain Parcellations Reveal Latent Functional Organization For Enhanced Connectivity-Based Neuroimaging Classification, Andrew Hannum, Mario A. Lopez

Computer Science: Faculty Scholarship

Brain parcellation schemes are fundamental to neuroimaging, yet general-purpose atlases may obscure the specific functional architecture relevant to a given cognitive task or clinical condition. This reflects a growing consensus that the “optimal” brain map is context-dependent. Here, we introduce a novel framework that validates this principle by generating task-optimized human brain parcellation maps directly from supervised learning objectives. Our method defines functional parcels by grouping brain regions based on the similarity of their contributions to a classifier's decision boundary for a specific goal (e.g., cognitive state decoding or clinical group separation). This approach prioritizes a region's discriminative role over …

Aura: An Ai-Powered Multimodal Prototype For Adaptive Apraxia Of Speech Therapy And Communication Support, Omotayo Omoyemi, Rachel K. Johnson

Speech-Language Pathology Faculty Publications

Apraxia of Speech (AOS) is a motor speech disorder that significantly limits communication and requires intensive, long-term therapy. Access to consistent treatment is often constrained by shortages of speech-language pathologists, high costs, and limited opportunities for continuous monitoring outside clinical settings. Recent advances in Artificial Intelligence (AI) provide new opportunities to support scalable and personalized speech therapy.

This paper presents AURA (Adaptive Understanding and Relearning Assistant for Apraxia), a multimodal AI framework designed to support speech therapy, progress monitoring, and communication for individuals with AOS. The system integrates speech analysis, machine learning–based error detection, reinforcement learning for adaptive therapy, and …

Safety Aware Continual Reinforcement Learning-Based Output Tracking Control Of Nonlinear Continuous-Time Systems, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

An output feedback (OF)-based control scheme utilizing both a scalable multilayer neural network (MNN) observer and actor–critic MNN via integral reinforcement learning (IRL)/adaptive dynamics programming (ADP) approach for a class of nonlinear systems with output constraints is introduced. The proposed observer, critic, and actor MNN weight updates are derived using a singular value decomposition (SVD) of MNN activation function gradient along with output error, Bellman and control input errors, respectively. Next, the approach incorporates continual learning (CL), utilizing a penalty function in the weight update laws for both actor–critic MNNs to consolidate knowledge from previous tasks and enhance learning in …

A Hybrid Response Surface Methodology And Machine Learning Framework For Quantifying Effects Of Physicochemical Parameters On Pfas Distribution, Harsh V. Patel, Jazmin Green, Hyoshin Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Predicting PFAS adsorption across diverse adsorbents and environmental matrices remains challenging because adsorbent physicochemical properties, PFAS molecular descriptors, and operational conditions simultaneously influence adsorption. This study develops and evaluates a unified hybrid modeling framework that integrates Response Surface Model (RSM) with machine-learning algorithms to quantify how six key variables, surface area, Log K_ow, pH_pzc, pK_a, log dose, and log-initial concentration, affect PFAS distribution coefficients (Log K_d). A data set of more than 1000 adsorption observations spanning 15 PFAS compounds, multiple adsorbent types, and a broad operational range was compiled and preprocessed using …

Crossing The Theory Threshold: The Pedagogical Potential Of Generative Artificial Intelligence In Educational Research, Amanda Burbage, Jennifer L. Styron

EVMS School of Health Professions Faculty Publications

Purpose

This paper presents findings from an educational research graduate course in which generative artificial intelligence (AI) was incorporated to strengthen learners' understanding of threshold concepts related to theoretical frameworks. Medical and health professionals often struggle with the transition from a clinical role into the educational research role.

Methods

The study posits that the use of generative AI will help learners understand and apply theoretical frameworks beyond a superficial level, furthering their understanding, constructing new knowledge, and strengthening their ability to develop sound educational research studies. Journal and AI transcripts were analyzed for 37 participants.

Results

Open-ended codes were grouped …

Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human–robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first observer, termed the reference point estimator, enables each robotic agent to estimate the object's reference center using consensus-based learning, even without direct access to global reference trajectories. The second observer, referred to as the human force-to-trajectory estimator, uses human-applied forces to infer the intended position, velocity, and acceleration of the object, enabling real-time estimation of human intent. Together, these two observers allow distributed estimation of human-intended motion. …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

Use Of Tezepelumab For Chronic Rhinosinusitis With Nasal Polyps By Eosinophilic Endotype: Waypoint Post-Hoc Analysis, Shigeharu Fujieda, Nobuyoshi Otori, Joseph K. Han, Tadataka Yabuta, Claudia Chen, Claudio Marchese, Andrews Foster, Sandhia S. Ponnarambil, Yun Chan, Brian J. Lipworth

Department of Otolaryngology (ENT) Faculty Publications

Background

The phase 3 WAYPOINT study (NCT04851964) reported that tezepelumab improved outcomes in patients with chronic rhinosinusitis with nasal polyps (CRSwNP), including nasal polyp size, nasal congestion, and sinonasal symptoms, and reduced the need for surgery and systemic corticosteroids (SCS).

Objective

To evaluate the efficacy and safety of tezepelumab across Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis-defined eosinophilic chronic rhinosinusitis (ECRS) subgroups.

Methods

Adults with severe CRSwNP were randomized to tezepelumab 210 mg or placebo every 4 weeks. Coprimary end points were the change from baseline to week 52 in total Nasal Polyp Score and the biweekly mean Nasal …