Search Results for author: David Shin

Found 5 papers, 0 papers with code

Clinical Applications of Plantar Pressure Measurement

no code implementations9 Jan 2024 Kelsey Detels, David Shin, Harrison Wilson, Shanni Zhou, Andrew Chen, Jessica Rosendorf, Atta Taseh, Bardiya Akhbari, Joseph H. Schwab, Hamid Ghaednia

In this study, we describe different plantar pressure devices available on the market and their clinical relevance.

The Required Spatial Resolution to Assess Imbalance using Plantar Pressure Mapping

no code implementations8 Jan 2024 Kelsey Detels, Shanni Zhou, Harrison Wilson, Jessica Rosendorf, Ghazal Shabestanipour, Elias Ben Mellouk, David Shin, Joseph Schwab, Hamid Ghaednia

Through the measurements obtained from all subjects, we measured the minimum spatial resolution required for plantar pressure mapping devices in assessment of balance.

Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks

no code implementations22 Nov 2022 Ramy Hussein, David Shin, Moss Zhao, Jia Guo, Guido Davidzon, Michael Moseley, Greg Zaharchuk

Such methods may enable more widespread and accurate CBF evaluation in larger cohorts who cannot undergo PET imaging due to radiation concerns, lack of access, or logistic challenges.

SSIM

Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation

no code implementations12 Feb 2022 Ramy Hussein, Moss Zhao, David Shin, Jia Guo, Kevin T. Chen, Rui D. Armindo, Guido Davidzon, Michael Moseley, Greg Zaharchuk

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke.

Multi-Task Learning SSIM +1

Natural Language Processing with Deep Learning for Medical Adverse Event Detection from Free-Text Medical Narratives: A Case Study of Detecting Total Hip Replacement Dislocation

no code implementations17 Apr 2020 Alireza Borjali, Martin Magneli, David Shin, Henrik Malchau, Orhun K. Muratoglu, Kartik M. Varadarajan

In this study we proposed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives.

Event Detection

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