Detecting middle ear fluid using smartphones

Abstract

The presence of middle ear fluid is a key diagnostic marker for two of the most common pediatric ear diseases: acute otitis media and otitis media with effusion. We present an accessible solution that uses speakers and microphones within existing smartphones to detect middle ear fluid by assessing eardrum mobility. We conducted a clinical study on 98 patient ears at a pediatric surgical center. Using leave-one-out cross-validation to estimate performance on unseen data, we obtained an area under the curve (AUC) of 0.898 for the smartphone-based machine learning algorithm. In comparison, commercial acoustic reflectometry, which requires custom hardware, achieved an AUC of 0.776. Furthermore, we achieved 85% sensitivity and 82% specificity, comparable to published performance measures for tympanometry and pneumatic otoscopy. Similar results were obtained when testing across multiple smartphone platforms. Parents of pediatric patients (n = 25 ears) demonstrated similar performance to trained clinicians when using the smartphone-based system. These results demonstrate the potential for a smartphone to be a low-barrier and effective screening tool for detecting the presence of middle ear fluid.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

“…In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”

eHealth Technologies Enable more Accessible Hearing Care

“…A recent study illustrates this trend very poignantly with the use of consumer smartphones to assess middle ear functioning using acoustic reflectance, demonstrating results equivalent to commercial devices. 30 Similarly, smartphones connected to calibrated transducers, which adhere to international medical device standards, allow clinical audiometry at significantly reduced costs. 21,24 Apart from hearing assessment technologies, facilities typically used for clinical audiometry have traditionally required expensive and stationary sound booths.…”

Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring

“…Smartphones allow for immediate and continuous monitoring of patients, timely information, and point-of-care diagnostics in a range of environments (e.g., homes, rural communities, developing countries). Smartphones support side-channel sensing based MDM by quantifying biomarkers with their range of embedded sensors; accelerometer [42] , [46] , magnetometer, gyroscope, light sensor, fingerprint sensor, microphone [13] , [47] , [48] , and camera [11] , [16] , [49] . Side-channel sensing achieved by re-purposing sensors allows detection of those biomarkers that replicate traditional medical gold standard devices.…”

“…Smartphone microphones can be similarly re-purposed to automatically detect cough frequency [47] , [61] , [62] using audio collected over a long term, or where fluid in the middle ear can be detected by measuring sounds emitted from the speaker and channelled down a paper funnel [48] . This is typical of sensors being utilised to detect modalities outside of their originally intended capabilities or purpose, for example to diagnose lung health (e.g., cystic fibrosis) through analysis of pressure variations as patients blow into a microphone [13] , replicating the gold standard device (a spirometer).…”