We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
77 ELEKTRONIKA

Download Mobile App




Events

31 Jul 2024 - 02 Aug 2024
02 Aug 2024 - 04 Aug 2024
20 Aug 2024 - 22 Aug 2024

Advanced AI Technology Improves Accuracy and Efficiency of Nasal Endoscopy

By HospiMedica International staff writers
Posted on 04 Jul 2024
Print article
Image: The application of convolutional neural networks can improve the accuracy and efficiency of nasal endoscopy (Photo courtesy of Shutterstock)
Image: The application of convolutional neural networks can improve the accuracy and efficiency of nasal endoscopy (Photo courtesy of Shutterstock)

Nasal endoscopy (NE) is a critical diagnostic tool in rhinology, though its effectiveness is often limited by the nasal cavity's complex structure. Now, an insightful article published in the International Forum of Allergy & Rhinology details a study exploring how convolutional neural networks (CNNs) can enhance the precision and efficiency of NE. The research addresses the challenges inherent in navigating the complex anatomy of the nasal cavity for diagnostic purposes.

Conducted by a team from Ochsner Health (New Orleans, LA, USA), the study focused on a CNN-based model tailored to precisely identify and outline key landmarks in NE imagery. The images utilized were collected from NE procedures carried out at a medical center over the period from 2014 to 2023, employing a standard digital endoscope. A total of 2,111 images were manually segmented by three physicians. The researchers adapted the YOLOv8 object detection model to classify whether a turbinate was present, identify its location, and apply a segmentation mask to outline its boundaries. The model was refined through transfer learning techniques involving backpropagation and stochastic gradient descent. By carefully adjusting hyperparameters and halting training after a 15-epoch lack of improvement in validation performance, the model demonstrated notable success.

The model was able to detect the inferior turbinate (IT) and middle turbinate (MT) with an average accuracy of 91.5%, precision of 92.5%, and recall rate of 93.8%. With a confidence threshold set at 60%, the model achieved an average F1-score of 93.1%. The effective application of the YOLOv8 model marks a significant progression in the field of rhinology. Its capacity to accurately locate and delineate the IT and MT can significantly support clinicians in the diagnosis and treatment of sinonasal conditions. This advancement is especially beneficial for trainees and non-specialists, who may struggle with the intricate anatomy of the nasal cavity.

"This study showcases the potential of CNNs to enhance nasal endoscopy's accuracy and efficiency," said senior otolaryngologist Dr. Edward D. McCoul, who guided the research team. "By leveraging advanced AI technologies, we can markedly improve our diagnostic capabilities and provide superior patient care for those with sinonasal conditions."

Related Links:
Ochsner Health

Gold Member
Disposable Protective Suit For Medical Use
Disposable Protective Suit For Medical Use
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Hemoconcentrator
Hemocor HPH

Print article

Channels

Critical Care

view channel
Image: A visualization of the blood-brain barrier disruption one hour post-treatment as noted by the diffusion of normally impermeant (Photo courtesy of APL Bioengineering)

New Technique Treats Aggressive Brain Tumors by Disrupting Blood-Brain Barrier

Glioblastoma, the most common malignant brain tumor, accounts for more than half of all such cancers. Despite the use of aggressive treatments like surgery, chemotherapy, and radiotherapy, the prognosis... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: POCT offers cost-effective, accessible, and immediate diagnostic solutions (Photo courtesy of Flinders University)

POCT for Infectious Diseases Delivers Laboratory Equivalent Pathology Results

On-site pathology tests for infectious diseases in rural and remote locations can achieve the same level of reliability and accuracy as those conducted in hospital laboratories, a recent study suggests.... Read more

Business

view channel
Image: The finalists have been announced for the IHF Awards 2024 (Photo courtesy of IHF)

International Hospital Federation Awards 2024 Finalists Announced

The International Hospital Federation (IHF; Geneva, Switzerland) has announced the finalists of the IHF Awards 2024 after the judges completed scoring entries in all 7 Award categories. The IHF Awards... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.