Research ArticleEAR INFECTION

Detecting middle ear fluid using smartphones

See allHide authors and affiliations

Science Translational Medicine  15 May 2019:
Vol. 11, Issue 492, eaav1102
DOI: 10.1126/scitranslmed.aav1102
  • Fig. 1 Using a smartphone to detect middle ear fluid.

    (A) Location of speaker and microphone on the bottom of an iPhone 5s, without and with paper funnel attached. (B) Process of assembling smartphone funnel. (C) Proper placement of smartphone and funnel at ear canal entrance. (D) Raw acoustic waveform obtained when chirps are played into an ear with middle ear fluid (red) and without fluid (blue). The SD (gray) is computed across 10 chirp instances on a patient’s ear.

  • Fig. 2 Classification of patient ears from clinical testing.

    (A) ROC curve for our middle ear fluid detection algorithm, cross-validated on data collected from patients using an iPhone 5s (n = 98), with operating point denoted by the red circle. (B) Comparison of performance for smartphone-based detection, acoustic reflectometer, and spectral angle–only classification during parallel clinical testing (n = 98). (C and D) Mean acoustic dip classified by the algorithm as with middle ear fluid (red) and without middle ear fluid (blue). Shaded region represents one SD from the mean. (E) Feature analysis indicating the weight that the classifier places on each frequency around the acoustic dip.

  • Fig. 3 Classification of patient ears under 18 months.

    (A) Demographic table of patients under 18 months. (B) Confusion matrix of the algorithm’s performance for patients under 18 months. (C and D) Mean acoustic dip of ears of patients under 18 months (n = 15) classified by the algorithm as with middle ear fluid (red) and without fluid (blue). Shaded region represents one SD from the mean.

  • Fig. 4 Classification performance across other mobile platforms.

    (A) ROC curve for our middle ear fluid detection algorithm, cross-validated on data collected from patients using a Samsung Galaxy S6 (n = 98). (B) Confusion matrices comparing performance on three other smartphones.

  • Fig. 5 Performance testing with trained clinicians versus untrained parents.

    (A) Demographic table of patients that were tested by parents. (B) Confusion matrix of the algorithm’s performance for patient ears (n = 25) tested by parents. (C and D) Mean acoustic dip of ears tested by parents (black) and clinicians classified by the algorithm as with middle ear fluid (red) and without fluid (blue).

  • Fig. 6 Benchmark testing across different scenarios.

    (A) Different paper types used to construct the funnel. (B) Different tip diameters of the funnel. (C) Different funnel placement angles. (D) Different background noise (infant crying) intensities. (E) Funnels created by different individuals. (F) Different chirp volumes. Solid and dashed lines indicate conditions where the algorithm classifies the waveform correctly and incorrectly, respectively. The figure shows the mean for each test and an SD computed across five chirp instances.

Supplementary Materials

  • stm.sciencemag.org/cgi/content/full/11/492/eaav1102/DC1

    Fig. S1. Funnel template for smartphone.

    Fig. S2. Conceptual diagram of the smartphone-based system.

    Fig. S3. Processed waveforms of patients under 18 months of age.

    Fig. S4. Comparison of waveforms from an ear across different smartphones.

    Fig. S5. Individual patient waveforms obtained from different smartphones.

    Fig. S6. Processed waveforms of testing by parents.

    Fig. S7. Processed waveforms of confounding ear pathologies.

    Fig. S8. Effect of angle of insertion on system performance in a patient with middle ear fluid.

    Fig. S9. Processed waveforms for a crying 2-year-old patient with partial head movement.

    Fig. S10. Effect of funnel deformation on system performance.

    Fig. S11. Effect of cerumen on acoustic waveforms.

    Fig. S12. Design for earbud headphones.

    Table S1. Demographic summary for first clinical study.

    Table S2. Interchirp reliability testing.

    Table S3. Funnel construction times and usability ratings.

    Movie S1. Video illustrating proper technique for testing.

    Movie S2. Instructional video for funnel construction.

  • The PDF file includes:

    • Fig. S1. Funnel template for smartphone.
    • Fig. S2. Conceptual diagram of the smartphone-based system.
    • Fig. S3. Processed waveforms of patients under 18 months of age.
    • Fig. S4. Comparison of waveforms from an ear across different smartphones.
    • Fig. S5. Individual patient waveforms obtained from different smartphones.
    • Fig. S6. Processed waveforms of testing by parents.
    • Fig. S7. Processed waveforms of confounding ear pathologies.
    • Fig. S8. Effect of angle of insertion on system performance in a patient with middle ear fluid.
    • Fig. S9. Processed waveforms for a crying 2-year-old patient with partial head movement.
    • Fig. S10. Effect of funnel deformation on system performance.
    • Fig. S11. Effect of cerumen on acoustic waveforms.
    • Fig. S12. Design for earbud headphones.
    • Table S1. Demographic summary for first clinical study.
    • Table S2. Interchirp reliability testing.
    • Table S3. Funnel construction times and usability ratings.
    • Legends for movies S1 and S2

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mp4 format). Video illustrating proper technique for testing.
    • Movie S2 (.mp4 format). Instructional video for funnel construction.

Stay Connected to Science Translational Medicine

Navigate This Article