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Deep learning algorithm classification of tympanostomy tube images from a heterogenous pediatric population
Corey Bryton1, Sruthi Surapaneni2, Nikhil Rangarajan3
1Tufts University School of Medicine, USA.
Importance:
The ability to augment routine post-operative tube check appointments with at-home digital otoscopes and deep learning AI could improve health care access as well as reduce financial and time burden on families.
Objective:
Tympanostomy tube checks are necessary but are also burdensome to families and impact access to care for other children seeking otolaryngologic care. Telemedicine care would be ideal, but ear exams are limited. This study aimed to assess whether an artificial intelligence (AI) algorithm trained with images from an over-the-counter digital otoscope can accurately assess tube status as in place and patent, extruded, or absent.
Design:
A prospective study of children aged 10 months to 10 years being seen for tympanostomy tube follow-up was carried out in three clinics from May-November 2023. A smartphone otoscope was used by non-MDs to capture images of the ear canal and tympanic membranes. Pediatric otolaryngologist exam findings (tube in place, extruded, absent) were used as a gold standard. A deep learning algorithm was trained and tested with these images. Statistical analysis was performed to determine the performance of the algorithm.
Setting:
3 urban, pediatric otolaryngology clinics within an academic medical center.
Participants:
Pediatric patients aged 10 months to 10 years with a past or current history of tympanostomy tubes were recruited. Patients were excluded from this study if they had a history of myringoplasty, tympanoplasty, or cholesteatoma. Main Outcome MeasureCalculated accuracy, sensitivity, and specificity for the deep learning algorithm in classifying tubal status as either in place and patent, extruded in external ear canal, or absent.
Results:
A heterogeneous group of 69 children yielded 296 images. Multiple types of tympanostomy tubes were included. The image capture success rate was 90.8 % in all subjects and 80 % in children with developmental delay/autism spectrum disorder. The classification accuracy was 97.1 %, sensitivity 97.1 %, and specificity 98.6 %.
Conclusion:
A deep learning algorithm was trained with images from a representative pediatric population. It was highly accurate, sensitive, and specific. These results suggest that AI technology could be used to augment tympanostomy tube checks.
