About

Angelos Barmpoutis is currently a Professor and coordinator of research and technology in the Digital Worlds Institute at the University of Florida. He is also an affiliate faculty of the Computer and Information Science and Engineering Department and the Biomedical Engineering Department, University of Florida. His current research projects focus on automated analysis of human motion, 3D reconstruction and dissemination of digital cultural heritage, applications of virtual and augmented reality, and medical image analysis.

Research topics

• Computer Science
• Artificial Intelligence
• Computer Security
• Multimedia
• Psychology
• Human–computer interaction
• Art history
• Art
• Simulation
• Library science
• Mathematics education
• Computer vision

Selected publications

• Automated Imaging Differentiation for Dementia

  Neurology Open Access · 2026-04-13

  articleOpen access

  Differentiation of Alzheimer disease dementia (ADD) and dementia with Lewy bodies (DLB) remains a challenge. Free-water (FW) imaging has been investigated in neurodegenerative diseases and was found to be associated with neurodegeneration and neuroinflammation. This retrospective cohort study tested whether Automated Imaging Differentiation for Dementia (AIDD), combining diffusion free-water imaging (FWI) and support vector machine, predicts ADD vs DLB with high accuracy.

  PublisherDOI

• Integrated Telehealth and Extended Reality to Enhance Home Exercise Adherence Following Total Hip and Knee Arthroplasty

  ArXiv.org · 2025-02-19

  preprintOpen access

  Nearly one million total hip and knee arthroplasties (THA/TKA) are performed annually in the United States, with most patients discharged home and prescribed home exercise programs (HEPs) to enhance lower extremity function. Traditional paper-based HEPs, while accessible and low-cost, often lack engagement and real-time feedback, which are critical for adherence and performance optimization. Extended reality (XR) and telehealth (TH) systems offer promising solutions, combining engagement and feedback, though each has limitations. To address these gaps, we designed and executed a pilot study that compared exercise performance in individuals with THA/TKA using a conventional paper-based HEP versus a proof-of-concept system, dubbed Tele-PhyT, that included the ideal characteristics of a future XR technology that would enable seamless HEP-TH systems, with robust marker-less full body tracking, real-time visual feedback, and performance quantification. The pilot study used a randomized cross-over design and targeted two types of users: therapists and patients. Participants favored Tele- PhyT for its real-time feedback and ease of use, and noted its potential to improve HEP adherence and exercise accuracy.

  PublisherOA PDFDOI

• Automated Imaging Differentiation for Parkinsonism

  JAMA Neurology · 2025-03-17 · 36 citations

  articleOpen access

  Importance: Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation of Parkinson disease (PD), multiple system atrophy (MSA) parkinsonian variant, and progressive supranuclear palsy (PSP). A prospective study is needed to test whether the approach meets primary end points to be considered in a diagnostic workup. Objective: To assess the discriminative performance of Automated Imaging Differentiation for Parkinsonism (AIDP) using 3-T diffusion MRI and support vector machine (SVM) learning. Design, Setting, and Participants: This was a prospective, multicenter cohort study conducted from July 2021 to January 2024 across 21 Parkinson Study Group sites (US/Canada). Included were patients with PD, MSA, and PSP with established criteria and unanimous agreement in the clinical diagnosis among 3 independent, blinded neurologists who specialize in movement disorders. Patients were assigned to a training set or an independent testing set. Exposure: MRI. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUROC) in the testing set for primary model end points of PD vs atypical parkinsonism, MSA vs PSP, PD vs MSA, and PD vs PSP. AIDP was also paired with antemortem MRI to test against postmortem neuropathology in a subset of autopsy cases. Results: A total of 316 patients were screened and 249 patients (mean [SD] age, 67.8 [7.7] years; 155 male [62.2%]) met inclusion criteria. Of these patients, 99 had PD, 53 had MSA, and 97 had PSP. A retrospective cohort of 396 patients (mean [SD] age, 65.8 [8.9] years; 234 male [59.1%]) was also included. Of these patients, 211 had PD, 98 had MSA, and 87 had PSP. Patients were assigned to the training set (78%; 104 prospective, 396 retrospective) or independent testing set, which included 145 (22%; 60 PD, 27 MSA, 58 PSP) prospective patients (mean age, 67.4 [SD 7.7] years; 95 male [65.5%]). The model was robust in differentiating PD vs atypical parkinsonism (AUROC, 0.96; 95% CI, 0.93-0.99; positive predictive value [PPV], 0.91; negative predictive value [NPV], 0.83), MSA vs PSP (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.98; NPV, 0.81), PD vs MSA (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.97; NPV, 0.97), and PD vs PSP (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.92; NPV, 0.98). AIDP predictions were confirmed neuropathologically in 46 of 49 brains (93.9%). Conclusions and Relevance: This prospective multicenter cohort study of AIDP met its primary end points. Results suggest using AIDP in the diagnostic workup for common parkinsonian syndromes.

  PublisherOA PDFDOI

• Integrated Telehealth and Extended Reality to Enhance Home Exercise Adherence Following Total Hip and Knee Arthroplasty

  2025-03-08 · 4 citations

  article
  PublisherDOI

• Investigating How Interaction with Physical Objects Within Virtual Environments Affects Knowledge Acquisition and Recall

  Communications in computer and information science · 2024-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

• Enhancing Museum Experience with VR by Situating 3D Collections in Contex

  2024-06-14 · 7 citations

  article

  Recent advancements in photogrammetry and 3D LiDAR scanners have led to a noticeable increase in the creation of 3D scans of museum artifacts. This trend has opened up new possibilities for museums to create interactive and immersive experiences using virtual reality (VR). In collaboration with the Florida Natural History Museum, we have developed an interactive VR game that utilizes the extensive 3D digital collection of the Florida Museum. The goal is to enhance the traditional museum experience by immersing visitors in dynamic VR environments that showcase 3D museum collections within context. Specifically, our VR game highlights 3D scans of endangered species of underwater creatures in the ocean. Children can swim alongside these sea creatures while an AI conversational agent provides scientific insights. Our VR game was showcased to the public at the Florida Natural History Museum during a public outreach event, attracting visits from three K-12 school trips. We conducted field observations to evaluate children’s interactions with the VR game and conducted semi-structured interviews with children’s guardians as well as museum staff. The findings from our observations emphasized the importance of shared experiences among visitors, which could be facilitated by projecting the VR gameplay on a large screen to mitigate the isolated nature of the HMD VR experience limitations. Additionally, museum staff emphasized the significance of considering visitor traffic when designing VR experiences in museum settings. The findings also highlighted a preference for seated experiences over standing ones due to safety concerns related to children colliding with others and museum artifacts. This paper provides an overview of our design process and the challenges of implementing HMD VR in museum settings, offering valuable insights for future endeavors aimed at designing public VR educational experiences targeting children.

  PublisherDOI

• Saving lives with coding: the global impact of an undergraduate project

  2024-01-01

  articleOpen access1st authorCorresponding

  digital technology that we use, from smartphones to gaming consoles, relies on code that is written and designed by people.

  PublisherOA PDFDOI

• Assessing the Influence of Passive Haptics on User Perception of Physical Properties in Virtual Reality

  Lecture notes in computer science · 2024-01-01

  book-chapterSenior author
  PublisherDOI

• Reinscribing the 3rd dimension in epigraphic studies and transcending disciplinary boundaries

  Ausonius Éditions eBooks · 2023-12-15

  book-chapterOpen access1st authorCorresponding
  PublisherDOI

• Prostate Capsule Segmentation in Micro-Ultrasound Images Using Deep Neural Networks

  2023-04-18

  articleSenior author

  Prostate cancer is the most common internal malignancy among males. Micro-Ultrasound is a promising imaging modality for cancer identification and computer-assisted visualization. Identifying the prostate capsule area is essential in active surveillance monitoring and treatment planning. In this paper, we present a pilot study that assesses prostate capsule segmentation using the U-Net deep neural network framework. To the best of our knowledge, this is the first study on prostate capsule segmentation in Micro-Ultrasound images. For our study, we collected multi-frame volumes of Micro-Ultrasound images, and then expert prostate cancer surgeons annotated the capsule border manually. The lack of clear boundaries and variation of shapes between patients make the task challenging, especially for novice Micro-Ultrasound operators. In total 2099 images were collected from 8 subjects, 1296 of which were manually annotated and were split into a training set (1008), a validation set (112), and a test set from a different subject (176). The performance of the model was evaluated by calculating the Intersection over Union (IoU) between the manually annotated area of the capsule and the segmentation mask computed from the trained deep neural network. The results demonstrate high IoU values for the training set (95.05%), the validation set (93.18%) and the test set from a separate subject (85.14%). In 10-fold cross-validation, IoU was 94.25%, and accuracy was 99%, validating the robustness of the model. Our pilot study demonstrates that deep neural networks can produce reliable segmentation of the prostate capsule in Micro-Ultrasound images and pave the road for the segmentation of other anatomical structures within the capsule, which will be the subject of our future studies.

  PublisherDOI

Frequent coauthors

• Baba C. Vemuri

  University of Florida

  19 shared

• Eleni Bozia

  University of Florida

  13 shared

• John R. Forder

  University of Florida

  7 shared

• Alexandra Kondyli

  University of Kansas

  6 shared

• Virginia P. Sisiopiku

  University of Alabama at Birmingham

  6 shared

• Ritwik Kumar

  4 shared

• Robert S. Wagman

  4 shared

• James Oliverio

  University of Florida

  4 shared

Education

• Ph.D., Digital Arts and Sciences

  University of Florida

• M.S., Computer and Information Science and Engineering

  University of Florida

• B.S., Computer and Information Science and Engineering

  University of Florida

Awards & honors

• Merit Award from the IEEE International Conference on Connec…
• Finalist for the Rome Prize for Historic Preservation and Co…
• UF Research Foundation Professor for 2020-2023