A Novel Ear Impression-Taking Method Using Structured Light Imaging and Machine Learning: A Pilot Proof of Concept Study with Patients' Feedback on Prototype.
J Clin Med
; 13(5)2024 Feb 21.
Article
em En
| MEDLINE
| ID: mdl-38592043
ABSTRACT
INTRODUCTION:
Taking an ear impression is a minimally invasive procedure. A review of existing literature suggests that contactless methods of scanning the ear have not been developed. We proposed to establish a correlation between external ear features with the ear canal and with this proof of concept to develop a prototype and an algorithm for capturing and predicting ear canal information.METHODS:
We developed a novel prototype using structured light imaging to capture external images of the ear. Using a large database of existing ear impression images obtained by traditional methods, correlation analyses were carried out and established. A deep neural network was devised to build a predictive algorithm. Patients undergoing hearing aid evaluation undertook both methods of ear impression-taking. We evaluated their subjective feedback and determined if there was a close enough objective match between the images obtained from the impression techniques.RESULTS:
A prototype was developed and deployed for trial, and most participants were comfortable with this novel method of ear impression-taking. Partial matching of the ear canal could be obtained from the images taken, and the predictive algorithm applied for a few sample images was within good standard of error with proof of concept established.DISCUSSION:
Further studies are warranted to strengthen the predictive capabilities of the algorithm and determine optimal prototype imaging positions so that sufficient ear canal information can be obtained for three-dimensional printing. Ear impression-taking may then have the potential to be automated, with the possibility of same-day three-dimensional printing of the earmold to provide timely access.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
J Clin Med
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Singapura