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.
项目摘要
中耳炎(OM)是经历了五出六名儿童在他们的第三个生日,30-40%的人患有
复发性感染,导致美国每年发生1600万次感染。耳朵感染是导致
6岁以下儿童的抗生素处方,是听力损失的第二大常见原因,
导致终身后遗症。诊断取决于亲自到诊所就诊和护理人员的目视检查
提供者,给患者和护理人员带来极大的不便,并给医疗保健系统带来巨大的成本,
估计每年40亿美元。虽然大多数OM病例在一周内消退,症状可能
可以通过非处方药管理,10-20%不需要,需要额外的抗生素治疗,或者,
在极端情况下,插入鼓膜造口管,为中耳提供通风,并帮助引流液体。
另一个复杂的因素是获得耳鼻喉科医生的准确诊断和感染有限
管理远程医疗的扩展有可能通过快速、方便和
虽然价格实惠,但迄今为止,还没有平台支持和促进有效的虚拟访问,以进行OM诊断。
第一阶段提案的第一个具体目标是建立一个包含数千个数据的综合数据库,
患有或不患有急性OM的患者的鼓膜图像,以及相关的临床诊断标签,
具体目标2,训练一种新颖的自定义机器学习算法Remmie. ai。卷积神经网络
将被开发来分类与症状的文本描述配对的鼓膜图像。图像分类
将通过数据扩充和通过传输构建的自定义Remmie.ai架构来改进
学习公开可用的培训模型。将揭盲标签与算法读数进行比较,
将盲态测试数据加载到Remmie.ai中,以确保准确性和验证的收敛,
急性OM与正常鼓膜的分类。在具体目标3中,Remmie.at平台与
用于对患者鼓膜进行成像的手持“便携式耳镜”和用户友好的移动终端应用程序将
由最终用户医生进行测试,以获得有关器械和软件可用性的反馈。成果
将成为患者和护理人员监测耳鼻喉科疾病,特别是急性OM,
基于患者提供的图像和症状以及诊断,并在专有的Remmie.ai
算法
项目成果
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