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OTO: The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review

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Manage episode 426585948 series 32402
Content provided by Chris Harris. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Chris Harris or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Editor in Chief Cecelia E. Schmalbach, MD, MSc, is joined by senior author Diana N. Kirke, MD, MPhil, and Associate Editor Lee M. Akst, MD, to discuss the potential for deep learning models to detect voice disorders as outlined in the paper “The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review” which published in the June 2024 special issue of Otolaryngology–Head and Neck Surgery. They compare the accuracy of different models and inputs and ponder the possibility of real-world implementation.

Click here to read the full article.

  continue reading

221 episodes

Artwork
iconShare
 
Manage episode 426585948 series 32402
Content provided by Chris Harris. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Chris Harris or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Editor in Chief Cecelia E. Schmalbach, MD, MSc, is joined by senior author Diana N. Kirke, MD, MPhil, and Associate Editor Lee M. Akst, MD, to discuss the potential for deep learning models to detect voice disorders as outlined in the paper “The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review” which published in the June 2024 special issue of Otolaryngology–Head and Neck Surgery. They compare the accuracy of different models and inputs and ponder the possibility of real-world implementation.

Click here to read the full article.

  continue reading

221 episodes

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