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The Detection and Treatment of Mid-Ear Infections Using Acoustic Reflectometry and Phototherapy

CSEF · 2023 Microbiology (General) Fourth Award

Fan Leanne · 9

Overview

Every year, there are 700 million cases of mid-ear infections (Otitis Media, OM) and nearly 21,000 deaths occur worldwide because of complications arising from OM (Worrall, 2007). The method of ear infection diagnosis hasn’t changed since German Otologist Wilhelm Kramer invented the otoscope in the 19th century. So, I am proposing a low-cost, machine learning (ML) driven diagnosis using echoes from the ear canal converted into spectrograms to provide an instant diagnostic of mid-ear infections. I was inspired by Dr. Neil Finsen, who invented light phototherapy to treat skin diseases a century ago. I integrated a 405nm blue LED into headphones to control mid-ear infections by killing bacteria within the ear canal. I combined a miniature speaker and microphone along with a blue LED into noise-canceling headphones, which I call Finsen headphones. The total cost of materials was $70, compared to the cost of traditional detection and treatment, which may cost up to 360 dollars without insurance. A Cloud-based machine learning service built on Tensorflow.js was used to train my mid-ear infection classification model with spectrograms taken from chirps echoing through a middle ear canal model. It uses transfer learning, an ML technique, to find patterns and trends within the images. The ML model can achieve 80% accuracy. For the blue LED phototherapy, I performed antibacterial property tests to prove the effectiveness of blue light treatment. The results showed that 405nm blue light can kill E.coli bacteria on the surface starting at 45 minutes, and most effectively at 75 minutes. Blue light treatment and machine learning show great promise in the future of medicine, especially due to the rising problem of antibiotic resistant bacteria and the shortage of healthcare professionals in certain areas.

Source coverage

This record comes from a published award list, not a complete project archive. Its abstract comes from CSEF's public project showcase as archived by the Internet Archive before judging (https://web.archive.org/web/20230401224130/https://ca-csef.zfairs.com/showcase/ShowcaseInfo?f=838e60b7-ea75-46e8-865c-fde4864244b3); the version presented may differ.

Awards (1)

Competition history

  • CSEF 2023 Microbiology (General) · Entry S1510

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