2021
DOI: 10.2196/25816
|View full text |Cite
|
Sign up to set email alerts
|

A Portable Smartphone-Based Laryngoscope System for High-Speed Vocal Cord Imaging of Patients With Throat Disorders: Instrument Validation Study

Abstract: Background Currently, high-speed digital imaging (HSDI), especially endoscopic HSDI, is routinely used for the diagnosis of vocal cord disorders. However, endoscopic HSDI devices are usually large and costly, which limits access to patients in underdeveloped countries and in regions with inadequate medical infrastructure. Modern smartphones have sufficient functionality to process the complex calculations that are required for processing high-resolution images and videos with a high frame rate. Rec… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
6
0

Year Published

2022
2022
2025
2025

Publication Types

Select...
9

Relationship

2
7

Authors

Journals

citations
Cited by 12 publications
(6 citation statements)
references
References 40 publications
0
6
0
Order By: Relevance
“…[ 14 ] Because smartphones are ubiquitous, a smartphone-based laryngoscope was invented, which connects the endoscope to a smartphone with an adaptor. [ 20 ] Even tethered capsule endomicroscopy has been reported. The swallowed capsule continuously obtained 10- μ m-resolution cross-sectional images as they traversed the esophagus.…”
Section: Discussionmentioning
confidence: 99%
“…[ 14 ] Because smartphones are ubiquitous, a smartphone-based laryngoscope was invented, which connects the endoscope to a smartphone with an adaptor. [ 20 ] Even tethered capsule endomicroscopy has been reported. The swallowed capsule continuously obtained 10- μ m-resolution cross-sectional images as they traversed the esophagus.…”
Section: Discussionmentioning
confidence: 99%
“…Computer-aided tongue diagnosis algorithms require smaller memory space lower inference time and high segmentation accuracy [2]. Therefore, the speed of inference and the total number of parameters of the model need to be considered while improving the segmentation accuracy [12]. In section 3.6.1 we will use the example of 512 × 512 × 3 images to compare with the SOTA models in terms of both the number of model parameters and inference time.…”
Section: Comparison With Sota Approachesmentioning
confidence: 99%
“…Zhou et al [11] propose a TongueNet tongue image segmentation network based on U-net as the backbone segmentation network and combined with morphological layers. Although the aforementioned tongue image segmentation methods exhibit high accuracy, they are hindered by large model sizes and slow inference speeds [12]. Given the rapid advancement of Internet-of-Things (IoT) applications, there is an urgent demand for training lightweight and efficient tongue image segmentation algorithms to cater to practical application requirements.…”
Section: Introductionmentioning
confidence: 99%
“…Previous such devices often substituted the camera components of an endoscope with a smartphone rather than maximizing the utility of the smartphone as a powerful image-processing device let alone incorporating other functions such as high-speed video capture [ 26 , 27 ]. Although there have been attempts to utilize the high-speed capture function of newer smartphones [ 28 ] to visualize vocal cord movement, image processing was typically performed on a separate device, such as a computer. Furthermore, there was no way to view the processed image in real time, making it less useful in tasks requiring real-time visualization such as surgery.…”
Section: Introductionmentioning
confidence: 99%