Search Results for author: Richard D. White

Found 10 papers, 0 papers with code

Integration and Implementation Strategies for AI Algorithm Deployment with Smart Routing Rules and Workflow Management

no code implementations17 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.


A multi-reconstruction study of breast density estimation using Deep Learning

no code implementations16 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).

breast density classification Density Estimation

Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning

no code implementations25 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.

Segmentation Transfer Learning

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis during CCTA Evaluation of Chest-Pain in the Emergency Department: Preparing an Application for Real-World Use

no code implementations10 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.

Predicting Rate of Cognitive Decline at Baseline Using a Deep Neural Network with Multidata Analysis

no code implementations24 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.

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