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Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
- Source :
- Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021), Scientific Reports
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains challenging due to the inherent limitations of visible light otoscopy and user interpretation. Here we describe a powerful diagnostic approach to otitis media utilizing advancements in otoscopy and machine learning. We developed an otoscope that visualizes middle ear structures and fluid in the shortwave infrared region, holding several advantages over traditional approaches. Images were captured in vivo and then processed by a novel machine learning based algorithm. The model predicts the presence of effusions with greater accuracy than current techniques, offering specificity and sensitivity over 90%. This platform has the potential to reduce costs and resources associated with otitis media, especially as improvements are made in shortwave imaging and machine learning.
- Subjects :
- 0301 basic medicine
Radio Waves
Computer science
Science
Common disease
Ear, Middle
Otoscopy
Machine learning
computer.software_genre
Article
Shortwave infrared
Machine Learning
03 medical and health sciences
0302 clinical medicine
Middle Ear Effusions
Physical examination
medicine
Humans
Otoscope
030223 otorhinolaryngology
Multidisciplinary
Otitis Media with Effusion
Extramural
business.industry
Translational research
Otitis Media
030104 developmental biology
Otitis
medicine.anatomical_structure
Middle ear
Medicine
Artificial intelligence
medicine.symptom
business
Biomedical engineering
Shortwave
computer
Algorithms
Subjects
Details
- ISSN :
- 20452322
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....e1e4014ea9a0f203f4c5f8579fe7393e