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The use of deep learning enables high diagnostic accuracy in detecting syndesmotic instability on weight-bearing CT scanning
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.
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
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.
Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network
To increase the confidence in the machine outcome, we also implemented saliency maps to visualize where the CNN looked at to make a diagnosis.
Detecting total hip replacement prosthesis design on preoperative radiographs using deep convolutional neural network
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.
