Remmie.ai: a deep learning diagnostic assistance engine for ear-nose-throat diseases
Remmie.ai:耳鼻喉疾病深度学习诊断辅助引擎
基本信息
- 批准号:10602813
- 负责人:
- 金额:$ 34.99万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-12-01 至 2023-11-30
- 项目状态:已结题
- 来源:
- 关键词:AcademyAcuteAddressAdultAgeAge YearsAlgorithmsAmericanAntibiotic TherapyAntibioticsArchitectureArtificial Intelligence platformBlindedCaregiversCaringCertificationChildChild SupportChildhoodClassificationClinicClinic VisitsClinicalCollaborationsComputer softwareCountryCoupledCustomDataData SetDatabasesDevelopmentDevicesDiagnosisDiagnosticDiseaseDrainage procedureEarEaracheEarly DiagnosisEconomic BurdenEnsureFamilyFeedbackFeverFutureHealth Care CostsHealth Insurance Portability and Accountability ActHealthcareHealthcare SystemsHomeImageIncomeInfectionInstitutional Review BoardsLabelLibrariesLiquid substanceMachine LearningMalleusMedicineModelingMonitorNoseOtitis MediaOtolaryngologistOtolaryngologyOtoscopesOutcomePatient imagingPatient observationPatientsPediatric HospitalsPediatricsPersonsPharmaceutical PreparationsPharyngeal structurePhasePhysiciansPositioning AttributeProcessProtocols documentationProviderRecommendationRecurrenceResource-limited settingResourcesSecureSpecialistStructureSupervisionSurveysSymptomsSystemTechnologyTelemedicineTestingTextTimeTissuesTrainingTubeTympanic membraneTympanostomy Tube InsertionsValidationVisitVisualWorkaccurate diagnosisage groupartificial intelligence algorithmburden of illnesscare burdencare providersclinical diagnosisclinical diagnosticsclinical efficacyconvolutional neural networkcostdeep learningdisabilityear infectionefficacy studyexperienceexperimental studyhearing impairmentimprovedinfection managementmachine learning algorithmmiddle earmobile applicationneural network algorithmnovelpediatricianportabilitypreventrecurrent infectionresearch and developmentresponsesuccesssymptom managementtechnological innovationtelehealthtooltransfer learningusabilityuser-friendlyventilationvirtual visit
项目摘要
PROJECT SUMMARY
Otitis media (OM) is experienced by five out of six children before their third birthday, and 30-40% suffer
recurring infections, leading to 16 million annual episodes in the US. Ear infections are the primary reason for
antibiotic prescription for children under 6 years, are the second most common cause of hearing loss, and can
lead to lifelong sequelae. Diagnosis depends upon in-person clinic visits and visual examination by care
providers, at great inconvenience to patients and caregivers and at significant cost to the healthcare system,
estimated at $4 billion per year. Although the majority of OM cases resolve within a week and symptoms may
be managed by over-the-counter medications,10-20% do not, requiring additional antibiotic treatment or, in
extreme cases, tympanostomy tube insertion to provide ventilation to the middle ear and aid in fluid drainage.
Another compounding factor is limited access to otolaryngologists for accurate diagnosis and infection
management. The expansion of telehealth has the potential to address this need with rapid, convenient, and
affordable, but to date, there are no platforms to support and facilitate effective virtual visits for OM diagnosis.
The first Specific Aim of this Phase I proposal involves building a comprehensive database of several thousand
images of eardrums from patients with or without acute OM, with associated clinical diagnostic labels to, in
Specific Aim 2, train a novel custom machine learning algorithm, Remmie.ai. A convolutional neural network
will be developed to classify images of eardrums paired with text description of symptoms. Image classification
will be improved through data augmentation, and the custom Remmie.ai architecture built through transfer
learning of a publicly available training model. Unblinded labels will be compared to the algorithm readout as
blinded testing data are loaded into Remmie.ai to ensure convergence of accuracy and validation for
classification of acute OM versus normal eardrums. In Specific Aim 3, the Remmie.at platform, coupled with a
handheld “portable otoscope” for imaging patients’ eardrums and a user-friendly mobile device application, will
be tested by end-user physicians to derive feedback on the usability of the device and software. The outcome
will be a novel tool for both patients and caregivers to monitor otolaryngic diseases, specifically acute OM,
based on patient-provided images and symptoms, and diagnosis, aided by the proprietary Remmie.ai
algorithm.
项目总结
项目成果
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