Search Results for author: Alireza Borjali

Found 6 papers, 0 papers with code

Detecting total hip replacement prosthesis design on preoperative radiographs using deep convolutional neural network

no code implementations27 Nov 2019 Alireza Borjali, Antonia F. Chen, Orhun K. Muratoglu, Mohammad A. Morid, Kartik M. Varadarajan

Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy.

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

A scoping review of transfer learning research on medical image analysis using ImageNet

no code implementations27 Apr 2020 Mohammad Amin Morid, Alireza Borjali, Guilherme Del Fiol

Inception models were the most commonly used in breast related studies (50%), while VGGNet was the common in eye (44%), skin (50%) and tooth (57%) studies.

Data Augmentation Transfer Learning

Improved Diagnosis of Tibiofemoral Cartilage Defects on MRI Images Using Deep Learning

no code implementations30 Nov 2020 Gergo Merkely, Alireza Borjali, Molly Zgoda, Evan M. Farina, Simon Gortz, Orhun Muratoglu, Christian Lattermann, Kartik M. Varadarajan

Conclusion: CNN can be used to enhance the diagnostic performance of MRI in identifying isolated tibiofemoral cartilage defects and may replace diagnostic knee arthroscopy in certain cases in the future.

Decision Making

The use of deep learning enables high diagnostic accuracy in detecting syndesmotic instability on weight-bearing CT scanning

no code implementations7 Jul 2022 Alireza Borjali, Soheil Ashkani-Esfahani, Rohan Bhimani, Daniel Guss, Orhun K. Muratoglu, Christopher W. DiGiovanni, Kartik Mangudi Varadarajan, Bart Lubberts

Model 3 outperformed Model 2 and achieved a nearly perfect performance, misclassifying only one case (F1-score = 0. 91) in the control group as unstable while being faster than Model 2.

Anatomy

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