Publications by authors named "Vinayak Ganeshan"

AI-enabled augmentation of nasal endoscopy video images is feasible in the clinical setting. Edge computing hardware can interface with existing nasal endoscopy equipment. Real-time AI performance can achieve an acceptable balance of accuracy and efficiency.

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Objective: Critical components of the nasal endoscopic examination have not been definitively established for either the normal examination or for clinical disorders. This study aimed to identify concordance among rhinologists regarding the importance of examination findings for various nasal pathologies.

Study Design: A consortium of 19 expert rhinologists across the United States was asked to rank the importance of findings on nasal endoscopy for 5 different sinonasal symptom presentations.

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Despite the widespread adoption of nasal endoscopy (NE) in the evaluation of sinonasal disease, its diagnostic potential may still be underutilized. Developments in endoscopic technology have led to significant improvements in video quality and maneuverability. However, there is concern that NE continues to be used primarily for the identification of gross pathology, with relative neglect of more subtle findings such as surface features of inflammation and mucus.

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A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.

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Sacral fractures are pelvic ring injuries that usually occur following a fall from height and may present with neurological injury. They are divided into several subtypes based on the pattern and location of injury. Certain subtypes require operative management due to the risk of neural compromise and inadequate axial load transfer, limiting mobility.

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