Autonomous diagnosis of ear diseases from biomarker data

1. A method for diagnosing an ear malady, the method comprising:

accessing a diagnostic model, wherein the diagnostic model is trained using a plurality of training examples that were generated by, for each of a plurality of patients:

receiving a set of biomarker features extracted from measurement data taken from an ear of a patient by:

obtaining an ear image of a portion of the patient's ear; and

extracting one or more biomarker features from the image by:

obtaining a set of samples of the ear image, each sample corresponding to a location in the ear;

for each of the set of samples, applying the sample to a trained feature detection model, the feature detection model comprising a neural network that is configured to output a likelihood of whether the sample contains an ear image object; and

storing a training example for the patient, the training example comprising at least the biomarker features for the patient and a label that indicates whether the patient has an ear malady;

wherein the diagnostic model is trained by repeatedly applying given ones of the plurality of training examples to the diagnostic model and updating parameters of the diagnostic model to improve an objective performance threshold;

and wherein the method further comprises:

generating new input data for a given patient using a new image of a given portion of a given ear of the given patient;

applying the new input data to the diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear malady from the new input data; and

outputting the probability-based diagnosis from the diagnostic model.

2. The method of claim 1 , wherein the new image is at least one of a two-dimensional image or a three-dimensional optical coherence tomography image.

3. The method of claim 1 , wherein the given portion of the given patient's ear comprises at least one of a tympanic membrane, an anatomical structure adjacent to a tympanic membrane, an ear canal adjacent to the tympanic membrane, a malleus, an umbo, and a light reflex.

4. The method of claim 1 , wherein the new image was re-taken in response to a determination that a prior image included an abnormality that would interfere with disease assessment.

5. The method of claim 1 , wherein receiving the set of biomarker features comprises:

applying a pressure stimulus to inside an ear of the patient;

receiving an acoustic response from the applied pressure stimulus;

extracting acoustic biomarker features from the received acoustic response; and

synthesizing, into the input data, the acoustic biomarker features.

6. The method of claim 5 , wherein the pressure stimulus is applied using at least one of pneumatic otoscopy or tympanometry.

7. The method of claim 1 , wherein the biomarker features each further indicate a confidence value that reflects a confidence that an indicated anatomical feature of the ear was accurately determined.

8. The method of claim 1 , further comprising:

determining a therapy for treatment of the ear malady based on the probability-based diagnosis; and

providing a description of the therapy.

9. The method of claim 8 , wherein determining the therapy comprises:

accessing a therapeutic parameters database;

identifying an entry of the therapeutic parameters database that corresponds to both the determination of whether the given patient has a disease, as well as a probability that the determination that the given patient has the disease is true; and

determining the therapy to be a therapy indicated by the entry based on the determined diagnosis.

10. The method of claim 8 , wherein determining the therapy comprises:

applying a machine learning model previously trained to associate the determined diagnosis and patient data for the given patient with a therapy.

11. A computer program product for diagnosing an ear malady, the computer program product comprising a computer-readable storage medium containing computer program code for:

accessing a diagnostic model, wherein the diagnostic model is trained using a plurality of training examples that were generated by, for each of a plurality of patients:

receiving a set of biomarker features extracted from measurement data taken from an ear of a patient by:

obtaining an ear image of a portion of the patient's ear; and

extracting one or more biomarker features from the image by:

obtaining a set of samples of the ear image, each sample corresponding to a location in the ear;

for each of the set of samples, applying the sample to a trained feature detection model, the feature detection model comprising a neural network that is configured to output a likelihood of whether the sample contains an ear image object; and

storing a training example for the patient, the training example comprising at least the biomarker features for the patient and a label that indicates whether the patient has an ear malady;

wherein the diagnostic model is trained by repeatedly applying given ones of the plurality of training examples to the diagnostic model and updating parameters of the diagnostic model to improve an objective performance threshold;

and wherein the computer program code is further for:

generating new input data for a given patient using a new image of a given portion of a given ear of the given patient;

applying the new input data to the diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear malady from the new input data; and

outputting the probability-based diagnosis from the diagnostic model.

12. The computer program product of claim 11 , wherein the new image is at least one of a two-dimensional image or a three-dimensional optical coherence tomography image.

13. The computer program product of claim 11 , wherein the given portion of the given patient's ear comprises at least one of a tympanic membrane, an anatomical structure adjacent to a tympanic membrane, an ear canal adjacent to the tympanic membrane, a malleus, an umbo, and a light reflex.

14. The computer program product of claim 11 , wherein the new image was re-taken in response to a determination that a prior image included an abnormality that would interfere with disease assessment.

15. The computer program product of claim 11 , wherein receiving the set of biomarker features comprises:

applying a pressure stimulus to inside an ear of the patient;

receiving an acoustic response from the applied pressure stimulus;

extracting acoustic biomarker features from the received acoustic response; and

synthesizing, into the input data, the acoustic biomarker features.

16. The computer program product of claim 15 , wherein the pressure stimulus is applied using at least one of pneumatic otoscopy or tympanometry.

17. The computer program product of claim 11 , wherein the biomarker features each further indicate a confidence value that reflects a confidence that an indicated anatomical feature of the ear was accurately determined.

18. The computer program product of claim 11 , wherein the computer program code is further for:

determining a therapy for treatment of the ear malady based on the probability-based diagnosis; and

providing a description of the therapy.

19. The computer program product of claim 18 , wherein determining the therapy comprises:

accessing a therapeutic parameters database;

identifying an entry of the therapeutic parameters database that corresponds to both the determination of whether the given patient has a disease, as well as a probability that the determination that the given patient has the disease is true; and

determining the therapy to be a therapy indicated by the entry based on the determined diagnosis.

20. The computer program product of claim 18 , wherein determining the therapy comprises:

applying a machine learning model previously trained to associate the determined diagnosis and patient data for the given patient with a therapy.