Radiology Commons^™

Ai-Driven Segmentation And Volumetric Response Modeling Of Liver Regions To Radiotherapy, Aashish Chandra Gupta

Dissertations and Theses (Open Access)

In liver-directed radiotherapy (RT), liver regions receiving higher doses typically undergo atrophy while contralateral/adjacent lower-dose regions may exhibit compensatory hypertrophy through regeneration of healthy tissue. Optimizing the RT plan to promote regional hypertrophy while minimizing the risk of developing atrophy has the potential to enhance post-RT liver function and long-term survivorship. However, current clinical practice largely relies on global liver dose-volume metrics during RT-planning, which may obscure favorable dose-response correlation and limit actionable guidance for clinicians. Therefore, we hypothesized that post-RT regional liver response is governed by a combination of region-specific dose-volume and patient clinical features, and that these responses …

A Multi-Agent Large Language Model Framework To Automatically Assess Performance Of A Clinical Ai Triage Tool, Adam Flanders, Yifan Peng, Luciano Prevedello, Robyn Ball, Errol Colak, Prahlad Menon, George Shih, Hui-Ming Lin, Paras Lakhani

Department of Radiology Faculty Papers

Radiology reports can be used as a surrogate for performance of clinical AI tools. Radiology reports were analyzed by an ensemble of eight open-source LLM models and a internal version of GPT-4o using a single multi-shot prompt that assessed for presence of ICH. Performance of the open-source models, consensus of models and GPT-4o were compared to human report review. Three ideal consensus LLM ensembles were tested for rating the performance of the triage tool. The capability of each LLM varied. The highest AUC performance was achieved with llama3.3:70b and GPT-4o. Using MCC the ideal combination of LLMs were: Full-9 Ensemble, …

Artificial Intelligence In Radiology: A Comparative Study Of Accuracy And Efficiency Across X-Ray, Ct, And Nuclear Medicine, Courtney J. Mello, Sophia D. Galvan, Britney R. Williams, Tuyen T. Thai, Phuong P. Mor, Saleha Zafar

Research Methods Poster Session 2026

Artificial intelligence (AI) is increasingly used in radiology to improve diagnostic accuracy and workflow efficiency; however, there is limited standardized research comparing its performance across imaging modalities. This study aimed to evaluate the accuracy and efficiency of AI in X-ray, computed tomography (CT), and nuclear medicine. A systematic review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2015 and 2025. Data were extracted on key performance metrics, including sensitivity, specificity, interpretation time, and error rates, to enable cross-modality comparison. Results demonstrated that AI achieved high diagnostic accuracy in X-ray imaging, with average sensitivity and specificity near 91% and …

Artificial Intelligence Decision Support In Automated Breast Ultrasound: Improving Diagnostic Accuracy And Reducing Unnecessary Biopsies, Chayaporn Hasdiseve, Jenjeera Prueksadee

Chulalongkorn Medical Journal

Background: Emerging roles of artificial intelligence support in breast imaging leads to improved radiologists’ performance

Objective: To assess the diagnostic performance of the artificial intelligence decision support in the evaluation of breast masses using automated breast ultrasound (ABUS).

Methods: 182 patients (415 breast masses) who received ABUS were included. Two readers, including the experienced breast radiologist (reader1) and the breast imaging fellow (reader2), separately reviewed the ABUS images and the artificial intelligence decision support according to the ACR BI-RADS 5^th edition.

Results: All of the 415 masses; 395 masses (95.2%) were benign and 20 masses …

Teaching Ai For Radiology Applications: A Multisociety-Recommended Syllabus From The Aapm, Acr, Rsna, And Siim, Felipe Kitamura, Timothy Kline, Daniel Warren, Linda Moy, Roxana Daneshjou, Farhad Maleki, Igor Santos, Judy Gichoya, Walter Wiggins, Brian Bialecki, Kevin O'Donnell, Adam E. Flanders, Matt Morgan, Nabile Safdar, Katherine P. Andriole, Raym Geis, Bibb Allen, Keith Dreyer, Matt Lungren, Monica J. Wood, Marc Kohli, Steve Langer, George Shih, Eduardo Farina, Charles E. Kahn, Ingrid Reiser, Maryellen Giger, Christoph Wald, John Mongan, Tessa Cook, Neil Tenenholtz

Department of Radiology Faculty Papers

No abstract available

Post-Deployment Monitoring Of Ai Performance In Intracranial Hemorrhage Detection By Chatgpt, Eric Rohren, Mohadese Ahmadzade, Sofia Colella, Nina Kottler, Sriyesh Krishnan, Jason Poff, Neelesh Rastogi, Walter Wiggins, Joyce Yee, Carlos Zuluaga, Phil Ramis, Mohammad Ghasemi-Rad

Faculty, Staff and Students Publications

Rationale and objectives: To evaluate the post-deployment performance of an artificial intelligence (AI) system (Aidoc) for intracranial hemorrhage (ICH) detection and assess the utility of ChatGPT-4 Turbo for automated AI monitoring.

Materials and methods: This retrospective study evaluated 332,809 head CT examinations from 37 radiology practices across the United States (December 2023-May 2024). Of these, 13,569 cases were flagged as positive for ICH by the Aidoc AI system. A HIPAA (Health Insurance Portability and Accountability Act) -compliant version of ChatGPT-4 Turbo was used to extract data from radiology reports. Ground truth was established through radiologists' review of 200 randomly selected …

Midrc Mrale Mastermind Grand Challenge: Ai To Predict Covid Severity On Chest Radiographs, Samuel G. Armato, Karen Drukker, Lubomir Hadjiiski, Carol C. Wu, Jayashree Kalpathy-Cramer, George Shih, Maryellen L. Giger, Natalie Baughan, Benjamin Bearce, Adam E. Flanders, Robyn L. Ball, Kyle J. Myers, Heather M. Whitney, The Midrc Grand Challenge Working Group

Department of Radiology Faculty Papers

PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) mRALE Mastermind Grand Challenge fostered the development of artificial intelligence (AI) techniques for the automated assignment of mRALE (modified radiographic assessment of lung edema) scores to portable chest radiographs from patients known to have COVID-19.

APPROACH: The challenge utilized 2079 training cases obtained from the publicly available MIDRC data commons, with validation and test cases sampled from not-yet-public MIDRC cases that were inaccessible to challenge participants. The reference standard mRALE scores for the challenge cases were established by a pool of 22 radiologist annotators. Using the MedICI challenge platform, participants submitted …

The Impacts Of Artificial Intelligence In Radiology, Misty Farmer, Wendy Trzyna

Theses, Dissertations and Capstones

Introduction: There has been significant growth in the use of Artificial Intelligence (AI) in the healthcare industry, especially in Medical Imaging. Radiology has been the clear frontrunner in the adoption of AI in medicine, due in part to the massive amount of digital data available for use in Deep Learning (DL) AI integration has the potential to solve multiple challenges in radiology, address workload issues and transform the field.

Purpose of the Study: The purpose of the research was to evaluate the impact of implementing Artificial Intelligence in radiology to determine if these technologies have had an impact …

Artificial Intelligence In Radiology, Olivia Sweeney

Theses, Dissertations and Capstones

Introduction: Artificial intelligence (AI) has increasingly transformed radiologic practice by improving diagnostic accuracy, streamlining workflows, and reducing interpretation errors. As AI integration has expanded across imaging modalities, questions have emerged regarding its effectiveness compared to traditional radiologist-only interpretation.

Purpose of Study: The purpose of this study has been to evaluate the impact of AI-assisted radiology on diagnostic accuracy, efficiency, and error reduction, while also assessing clinician perceptions of AI as a collaborative tool in imaging analysis.

Methodology: This qualitative study has used a systematic review of peer-reviewed literature published between 2015 and 2025, following PRISMA guidelines, combined with an interview …

Impact Of Chatgpt And Large Language Models On Radiology Education: Association Of Academic Radiology-Radiology Research Alliance Task Force White Paper, David H. Ballard, Alexander Antigua-Made, Emily Barre, Elizabeth Edney, Emile B. Gordon, Linda Kelahan, Taha Lodhi, Jonathan G. Martin, Melis Ozkan, Kevin Serdynski, Bradley Spieler, Daphne Zhu, Scott J. Adams

School of Medicine Faculty Publications

Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the Association of Academic Radiology-Radiology Research Alliance convened a task force to explore the promise and pitfalls of using LLMs such as ChatGPT in radiology. This white paper explores the impact of LLMs on radiology education, highlighting their potential to enrich curriculum development, teaching and learning, and learner assessment. Despite these advantages, the implementation of LLMs presents challenges, including limits on accuracy and transparency, the risk of misinformation, data privacy issues, …

A Guideline For Open-Source Tools To Make Medical Imaging Data Ready For Artificial Intelligence Applications: A Society Of Imaging Informatics In Medicine (Siim) Survey, Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine Andriole, Peter Chang, Keyvan Farahani, Mona Flores, Les Folio, Sina Houshmand, Maryellen Giger, Judy Gichoya, Bradley Erickson

Department of Radiology Faculty Papers

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora …

Unveiling The Potential: The Role Of Ai-Enhanced Ecg In Cardiovascular Disease Detection, Alisha Vincent

Rowan-Virtua Research Day

Background: The Electrocardiogram (ECG) is a widely utilized, non-invasive, cost-effective cardiac test. Its integration with Artificial Intelligence (AI) has empowered it to become a potent screening tool and a predictor for various cardiovascular diseases, especially in asymptomatic individuals. Objective: This review investigates the utility of AI-powered ECG in early detection of cardiac conditions, focusing on conditions such as low ejection fraction (LEF), atrial fibrillation (AF), aortic valve stenosis (AVS), and cardiac amyloidosis (CA). Methods: A literature review spanning 2018 to 2024 was conducted, analyzing 10 articles - 3 on AF, 3 on AVS, 3 on LEF, and …

The Measure Of Efficiency And Effectiveness When Using Artificial Intelligence (Ai) In Radiology, Jordan Watts

Theses, Dissertations and Capstones

Introduction: The use of artificial intelligence in radiology has helped radiologists identify patterns and abnormalities in medical images to diagnose and treat patients. Deep learning and machine learning algorithms have been used to assist physicians in detecting features that are not noticeable to the human eye. The FDA has approved almost 400 AI algorithms for radiology and estimated that the market for AI in medical imaging would grow from $21.48 billion in 2018 to $264.85 billion in 2028.

Purpose of the Study: The purpose of this research was to evaluate the use of artificial intelligence in radiology to determine its …

Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho

Mathematics & Statistics Faculty Publications

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …

Medical Imaging Applications Of Federated Learning, Sukhveer Singh Sandhu, Hamed Taheri Gorji, Pantea Tavakolian, Kouhyar Tavakolian, Alireza Akhbardeh

Faculty, Staff and Student Publications

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique's inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, …

Multi-Parametric Mri For Radiotherapy Simulation, Tian Li, Jihong Wang, Yingli Yang, Carri K Glide-Hurst, Ning Wen, Jing Cai

Faculty, Staff and Student Publications

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Artificial Intelligence Cad Tools In Trauma Imaging: A Scoping Review From The American Society Of Emergency Radiology (Aser) Ai/Ml Expert Panel, David Dreizin, Pedro V Staziaki, Garvit D Khatri, Nicholas M Beckmann, Zhaoyong Feng, Yuanyuan Liang, Zachary S Delproposto, Maximiliano Klug, J Stephen Spann, Nathan Sarkar, Yunting Fu

Faculty, Staff and Student Publications

BACKGROUND: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

PURPOSE: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

METHODS: Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends.

RESULTS: A total of 4052 records were screened, and 233 full-text …

Rapid Assessment Of Fish Freshness For Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy And Fusion-Based Artificial Intelligence, Hossein Kashani Zadeh, Mike Hardy, Mitchell Sueker, Yicong Li, Angelis Tzouchas, Nicholas Mackinnon, Gregory Bearman, Simon A Haughey, Alireza Akhbardeh, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M Tabb, Rosalee S Hellberg, Shereen Ismail, Hassan Reza, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Christopher T Elliott

Faculty, Staff and Student Publications

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and …

Diagnostic Accuracy Of Artificial Intelligence For Detecting Gastrointestinal Luminal Pathologies: A Systematic Review And Meta-Analysis, Om Parkash, Asra Tus Saleha Siddiqui, Uswa Jiwani, Fahad Rind, Zahra Ali Padhani, Arjumand Rizvi, Zahra Hoodbhoy, Jai K. Das

Section of Gastroenterology

Background: Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease.
Methods: We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. …

Point-Of-Care Ultrasound: New Concepts And Future Trends, Yaoting Wang, Huihui Chai, Ruizhong Ye, Jingzhi Li, Ji-Bin Liu, Chen Lin, Chengzhong Peng

Department of Radiology Faculty Papers

Ultrasound (US) technology, with major advances and new developments, has become an essential and first-line imaging modality for clinical diagnosis and interventional treatment. US imaging has evolved from one-dimensional, twodimensional to three-dimensional display, and from static to real-time imaging, as well as from structural to functional imaging. Based on its portability and advanced digital imaging technique, US was first adopted by emergency medicine in the 1980s and gradually gained popularity among other specialists for clinical diagnosis and interventional treatment. Point-of-Care Ultrasound (POCUS) was then proposed as a new concept and developed for new uses, which greatly extended clinical US applications. …

A Deep Learning Approach To Diagnostic Classification Of Prostate Cancer Using Pathology–Radiology Fusion, Pegah Khosravi, Maria Lysandrou, Mahmoud Eljalby, Qianzi Li, Ehsan Kazemi, Pantelis Zisimopoulos, Alexandros Sigaras, Matthew Brendel, Josue Barnes, Camir Ricketts, Dmitry Meleshko, Andy Yat, Timothy D. Mcclure, Brian D. Robinson, Andrea Sboner, Olivier Elemento, Bilal Chughtai, Iman Hajirasouliha

Publications and Research

Background

A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications.

Purpose

To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information.

Study Type

Retrospective.

Population

Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases).

Field Strength/Sequence

1.5 to 3.0 Tesla, T2-weighted image pulse sequences.

Assessment

MR images reviewed and selected …

The Effectiveness Of Image Augmentation In Deep Learning Networks For Detecting Covid-19: A Geometric Transformation Perspective, Mohamed Elgendi, Muhammad Umer Nasir, Qunfeng Tang, David Smith, John Paul Grenier, Catherine Batte, Bradley Spieler, William Donald Leslie, Carlo Menon, Richard Ribbon Fletcher, Newton Howard, Rabab Ward, William Parker, Savvas Nicolaou

School of Medicine Faculty Publications

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often …

Radiology Of Covid-19 - Imaging The Pulmonary Damage, Saba Sohail

Department of Radiology

A large part of the world is presently in the grip of the coronavirus disease (COVID-19) by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), declared a pandemic in March 2020. This document is a brief commentary of the imaging modalities used in the screening, diagnosis and management of COVID-19 pneumonia. Chest x-rays, especially portable, still form a part of majority of official guidelines, with reports of the suggestive radiologic features. The potential of CT scan and ultrasound is also realised, with earlier detection rate. Typical radiologic findings of bilateral, asymmetrical, crazy-paved ground glass opacification, consolidation, reverse halo sign, opacities, …

Artificial Intelligence In Ultrasound Imaging: Current Research And Applications, Shuo Wang, Bs, Ji-Bin Liu, Md, Ziyin Zhu, Md, John Eisenbrey, Phd

Department of Radiology Faculty Papers

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent software or system based on big data information, machine learning and deep learning technologies. The rapid development of science and technology as well as internet communication has enabled AI and big data to gradually apply to many fields of health care. The modern imaging medicine is one of the first areas that AI can play an important role and applications. As cross-sectional imaging, ultrasound (US) is well suitable for AI technology to standardize imaging protocols and improve diagnostic accuracy. This article reviews current AI technology …

Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …