A multispectral imaging method and device to detect and quantify the presence of fluid in the middle ear to facilitate the diagnosis and triage of ear infections

Middle ear infections or otitis media that cause inflammation of tympanic membrane and fluid buildup in the middle ear cavity accounts for 2-3 million hospital visits every year [34]. As per an epidemiological study conducted from 2006 - 2016 on 685 children, between the ages of 1-3 years, roughly 60% had at least one hospital visit due to ear infections [3]. Despite the high incidence, the diagnosis of otitis media is only 50% accurate (a coin toss) due to the subjective nature of diagnosis as the physicians look at the ear drum and detect the fluid behind the ear drum. To detect the fluid with high sensitivity and accurately diagnose middle ear infection, we propose a multispectral visible - nIR otoscope that operates in the range of 600 nm - 1050 nm. We have performed experiments to demonstrate the proof of concept of our device on phantoms that includes, 3D printed middle ear structure, tympanic membrane made of silicone, and orange juice as ear fluid all of which mimics the properties of human ear. The multispectral otoscope showed highest contrast between ossicles and fluid at 1000 nm which shows low attenuation of fluid and tympanic membrane at NIR wavelengths. The system is calibrated against a diffuse reflection surface to account for variations in source and detector. Our experiments showed that empty phantoms yielded almost equal contrast across the entire visible- NIR wavelength. Once the fluid is filled, the contrast increased by 30 ± 10 % in the visible wavelength (600 nm - 750 nm) and 120 ± 20 % in nIR wavelength (900 nm - 1000 nm). This 80% - 100% difference in contrast between visible and NIR wavelength is used to detect and highlight the areas of the middle ear filled with fluid.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Rajamanickam, Gokul Prasath.

Advisor dc:contributor.advisor

• Nevan C. Hanumara and Nicholas X. Fang.

• Engineering and Management Program.
• System Design and Management Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/132857

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132857