Abstract: Various aspects of methods, systems, and use cases may be used to generate an ear disease state prediction to assist diagnosis of an ear disease. A method may include receiving an image an ear, predicting an image-based confidence level of a disease state in the ear by using the image as in input to a machine learning trained model. The method may include receiving text, for example corresponding to a symptom of the patient. The method may include predicting a symptom-based confidence level of the disease state in the ear by using the text as in input to a trained classifier. The method may include using the results of the image-based confidence level and the symptom-based confidence level to determine an overall confidence level of presence of an ear infection in the ear of the patient.

Abstract: An otoscope configured to be used together with a host device, such as a smart phone or other handheld mobile devices. The otoscope can utilize features on the hand-held mobile device, such as camera, software, and display, to allow a user view a person's ear canal and tympanic membrane or eardrum. In some embodiments, the otoscope may comprise a housing; a light source; an speculum; an optical redirection component; one or more image sensors; image-processing circuitry; and communication circuitry configured to transmit the sensed image to the host device.