Remote medical examination

1. A system for at-home imaging of a child's ear canal and diagnosis of middle ear disease, the system comprising:

a light source configured to illuminate the ear canal;

a camera configured to provide downward-and-forward facing images of the ear canal;

an otoscope; and

a web-based application configured to:

receive the downward-and-forward facing images from the camera;

output an insufficient image capture classification and a recommendation to consult with a healthcare provider when the received downward-and-forward facing images from the camera do not depict at least a portion of a tympanic membrane;

autonomously capture the downward-and-forward facing images depicting at least a portion of the tympanic membrane; and

autonomously classify the at least the portion of the tympanic membrane from the captured downward-and-forward facing images depicting the at least the portion of the tympanic membrane to indicate the status of the middle ear, the autonomous classification selected from a group consisting of: normal ear, middle ear fluid (“otitis media with effusion”), and middle ear infection (“acute otitis media”).

2. The system of claim 1 , wherein the camera is located within a smartphone otoscope attachment.

3. The system of claim 1 , wherein the web-based application is further configured to:

output a recommendation to consult with a healthcare provider when the received downward-and-forward facing images from the camera are autonomously classified as middle ear infection (“acute otitis media”); and

output a recommendation for no further action needed or treating at-home when the received downward-and-forward facing images from the camera are autonomously classified as normal ear.

4. The system of claim 3 , wherein the web-based application outputting the recommendation to consult with the healthcare provider when the received downward-and-forward facing images from the camera are autonomously classified as middle ear infection (“acute otitis media”) comprises the web-based application outputting a middle ear infection (“acute otitis media”) diagnosis to a telemedicine healthcare provider and a recommendation for an antibiotic prescription.

5. The system of claim 1 , wherein the web-based application comprises a machine learning software component that has been trained with images of the same resolution as the camera.

6. The system of claim 5 , wherein the software has been trained with images taken at a variety of angles and orientations.

7. The system of claim 5 , wherein the software has been trained with images that include cerumen at least partially blocking the view of the tympanic membrane.

8. The system of claim 1 , further comprising a coating applied to at least one otoscope tip, the coating configured to dissolve, displace, and repel obstructing cerumen.

9. The system of claim 1 , further comprising at least one otoscope tip that can be used for both ears.

10. The system of claim 1 , further comprising an otoscope tip configured for use with a left ear and an otoscope tip configured for use with a right ear.

11. The system of claim 1 , further comprising a plurality of otoscope tips.

12. The system of claim 1 , further comprising an otoscope tip that accommodates the mean angle of the anterior ear canal (148 degrees) and the mean angle of the inferior ear canal (146 degrees).

13. The system of claim 1 , further comprising an otoscope tip that is configured to suspend the otoscope in the ear canal.

14. The system of claim 1 , further comprising an otoscope the tip that is mountable onto the otoscope, the tip comprising:

a distal end configured to receive the otoscope;

a substantially conical portion that narrows as it extends from the distal end; and

a substantially cylindrical portion extending from the substantially conical portion to a proximal end,

wherein the otoscope tip comprises an optical pathway extending through the substantially conical portion and the substantially cylindrical portion,

wherein an anterior angle formed between the substantially conical portion and the substantially cylindrical portion is between about 146 and about 148 degrees, and

wherein an interior angle of the optical pathway between the substantially conical portion and the substantially cylindrical portion is between about 165 and about 170 degrees.

15. The otoscope tip of claim 14 , further comprising a coating configured to dissolve cerumen.

16. The system of claim 1 , wherein the web-based application is further configured to present a medical history questionnaire and receive answers to the medical history questionnaire pertaining to the status of the middle ear.

17. A system for at-home imaging of a child's ear canal and diagnosis of middle ear disease, the system comprising:

a light source configured to illuminate the ear canal;

a camera configured to provide downward-and-forward facing images of the ear canal; an otoscope; and

a web-based application configured to:

receive the downward-and-forward facing images from the camera;

autonomously capture the downward-and-forward facing images depicting at least a portion of a tympanic membrane; and

autonomously classify the at least portion of the tympanic membrane from the captured downward-and-forward facing images to indicate the status of the middle ear,

wherein the web-based application comprises a machine learning software component that has been trained with images of the same resolution as the camera, and wherein the software has been trained with images that include a rim of an at-home otoscope tip.

18. A system comprising:

a light source configured to illuminate an ear canal;

a camera configured to provide downward-and-forward facing images of the ear canal;

an otoscope; and

a web-based application configured to:

receive the downward-and-forward facing images from the camera;

autonomously capture the downward-and-forward facing images depicting at least a portion of a tympanic membrane; and

autonomously classify the at least portion of the tympanic membrane from the captured downward-and-forward facing images to indicate the status of the middle ear,

wherein the web-based application comprises a machine learning software component that has been trained at least in part with: (i) one or more images of a tympanic membrane prior to a myringotomy, and (ii) one or more post-myringotomy surgical findings.

19. The system of claim 18 , wherein the one or more post-myringotomy surgical findings comprise direct visualization of at least a portion of the middle ear through an incision in the tympanic membrane, and wherein the machine learning software component has been trained at least in part with the one or more images of the tympanic membrane prior to a myringotomy and with the one or more post-myringotomy surgical findings from direct visualization of at least the portion of the middle ear through the incision in the tympanic membrane.

20. The system of claim 19 , wherein the web-based application is further configured to:

output an insufficient image capture classification and a recommendation to consult with a healthcare provider when the received downward-and-forward facing images from the camera do not depict at least a portion of a tympanic membrane;

output a recommendation to consult with a healthcare provider when the received downward-and-forward facing images from the camera are autonomously classified as middle ear infection (“acute otitis media”); and

output a recommendation, the recommendation selected from the group consisting of: no further action needed, treat at-home, and consult with a healthcare provider, when the received downward-and-forward facing images from the camera are autonomously classified as normal ear and middle ear fluid (“otitis media with effusion”).