no code implementations • 17 Nov 2023 • Barbaros Selnur Erdal, Vikash Gupta, Mutlu Demirer, Kim H. Fair, Richard D. White, Jeff Blair, Barbara Deichert, Laurie Lafleur, Ming Melvin Qin, David Bericat, Brad Genereaux
This paper reviews the challenges hindering the widespread adoption of artificial intelligence (AI) solutions in the healthcare industry, focusing on computer vision applications for medical imaging, and how interoperability and enterprise-grade scalability can be used to address these challenges.
no code implementations • 29 Dec 2022 • Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez, Ralf Floca, Laurence Jackson, Brad Genereaux, Sidney Bryson, Christopher P Bridge, Jens Kleesiek, Felix Nensa, Rickmer Braren, Khaled Younis, Tobias Penzkofer, Andreas Michael Bucher, Ming Melvin Qin, Gigon Bae, Hyeonhoon Lee, M. Jorge Cardoso, Sebastien Ourselin, Eric Kerfoot, Rahul Choudhury, Richard D. White, Tessa Cook, David Bericat, Matthew Lungren, Risto Haukioja, Haris Shuaib
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
no code implementations • 16 Feb 2022 • Vikash Gupta, Mutlu Demirer, Robert W. Maxwell, Richard D. White, Barbaros Selnur Erdal
Traditional mammograms are being replaced by tomosynthesis and its other low radiation dose variants (for example Hologic' Intelligent 2D and C-View).
no code implementations • 25 Aug 2021 • Mutlu Demirer, Richard D. White, Vikash Gupta, Ronnie A. Sebro, Barbaros S. Erdal
100% detection for LLIED presence/location; and 2.
no code implementations • 28 Sep 2020 • Vikash Gupta, Clayton Taylor, Sarah Bonnet, Luciano M. Prevedello, Jeffrey Hawley, Richard D. White, Mona G. Flores, Barbaros Selnur Erdal
In order to maximize the efficacy of breast cancer screening programs, proper mammographic positioning is paramount.
no code implementations • 25 Sep 2020 • Vikash Gupta1, Holger Roth, Varun Buch3, Marcio A. B. C. Rockenbach, Richard D. White, Dong Yang, Olga Laur, Brian Ghoshhajra, Ittai Dayan, Daguang Xu, Mona G. Flores, Barbaros Selnur Erdal
The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology.
no code implementations • 10 Aug 2020 • Richard D. White, Barbaros S. Erdal, Mutlu Demirer, Vikash Gupta, Matthew T. Bigelow, Engin Dikici, Sema Candemir, Mauricio S. Galizia, Jessica L. Carpenter, Thomas P. O Donnell, Abdul H. Halabi, Luciano M. Prevedello
The two-phase approach consisted of (1) Phase 1 - focused on the development and preliminary testing of an algorithm for vessel-centerline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection; and (2) Phase 2 - concerned with simulated-clinical Trialing of the developed algorithm on a per-case basis in a more real-world study population (n = 100 with 28% disease prevalence) from an ED chest-pain series.
no code implementations • 24 Feb 2020 • Sema Candemir, Xuan V. Nguyen, Luciano M. Prevedello, Matthew T. Bigelow, Richard D. White, Barbaros S. Erdal
Purpose: This study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit.
no code implementations • 26 Nov 2019 • Sema Candemir, Richard D. White, Mutlu Demirer, Vikash Gupta, Matthew T. Bigelow, Luciano M. Prevedello, Barbaros S. Erdal
We have evaluated the system on a reference dataset representing247 patients with atherosclerosis and 246 patients free of atherosclerosis.
no code implementations • 14 Aug 2019 • Barbaros S. Erdal, Mutlu Demirer, Chiemezie C. Amadi, Gehan F. M. Ibrahim, Thomas P. O'Donnell, Rainer Grimmer, Andreas Wimmer, Kevin J. Little, Vikash Gupta, Matthew T. Bigelow, Luciano M. Prevedello, Richard D. White
CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses).