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Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural

Gun Ho Kim1, Eui-Suk Sung2,3, Kyoung Won Nam4,5,6

  • 1Interdisciplinary Program in Biomedical Engineering, School of Medicine, Pusan National University, Busan, South Korea.

Biomedical Engineering Online
|May 26, 2021
PubMed
Summary

Early detection of laryngeal masses is improved with a new AI model and home-screening system. This technology aims for faster diagnosis and reduced infection risk, enhancing patient outcomes and safety.

Keywords:
Convolutional neural networkDeep learningLaryngeal massPatient safety

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Otolaryngology

Background:

  • Early detection of laryngeal masses is crucial for improving recovery rates and survival.
  • Reducing hospital visits can minimize infection risks associated with laryngeal mass diagnosis and treatment.

Purpose of the Study:

  • To develop an automated laryngeal mass detection model using convolutional neural networks.
  • To create a pilot system for home-based self-screening of laryngeal masses.

Main Methods:

  • A convolutional neural network (CNN) model was developed for automated laryngeal mass detection from hospital-acquired diagnostic images.
  • A pilot system integrating an embedded controller, camera, and LCD was designed for home self-screening.
  • Model performance was evaluated using validation loss and F1-scores on test datasets.

Main Results:

  • The CNN model achieved a validation loss of 0.9152 and an F1-score of 0.8371 before post-processing.
  • The computer algorithm demonstrated an F1-score of 0.8534 after post-processing on printed test images.
  • The embedded pilot system achieved an F1-score of 0.7672 for home-based screening.

Conclusions:

  • The proposed AI technique and pilot system are expected to increase early laryngeal mass detection rates.
  • This approach can reduce the risk of clinical infection spread, enhancing convenience and safety.
  • The technology supports improved patient care and safety for individuals and medical staff.