Application of the Belle.AI Comparative Image Reference System for Describing Chronic Ear Infections in Pediatric Patients From Low-Resource Communities in Remote, At-Home Settings
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 20
- 试验地点
- 1
- 主要终点
- Provider-Rated Usefulness
研究概览
简要总结
The purpose of this research is to examine the clinical effectiveness of an investigational AI-powered smartphone app that describes the physical characteristics of an otoscopy image. It is not intended to make any diagnosis. Only healthcare professionals will make the diagnosis. The investigators will compare the diagnosis made by physicians or physician extenders (healthcare professionals) against the description provided by the support AI app tool to determine the clinical relevance of the system and examine its use within a clinical setting.
详细描述
Timely access to primary care physicians for diagnosing and treating otitis media (infection or inflammation of the middle ear) in pediatric patients is often limited in rural, low-resource, and skin-of-color communities, particularly in remote or at-home settings.
This lack of access increases the risk of undiagnosed and untreated pediatric otitis media, potentially leading to severe and permanent complications, including hearing loss, deafness, tinnitus, impaired language development, cognitive deficits, and delayed educational development. A growing body of literature supports the accuracy (as measured by sensitivity and specificity of the model) of automated image analysis of middle ear conditions with machine learning-based algorithms. This proposed study will evaluate the effectiveness of a smartphone-based software that describes the physical nature of otoscopy images, powered by artificial intelligence (AI) and computer vision, in support of healthcare providers diagnosing acute otitis media and otitis media with effusion.
This pilot study will prospectively examine the value of Belle Otoscopy AI in describing otoscopy images in pediatric patients in low resourced communities who present with ear discomfort. It will also examine the utility of Belle.ai's companion software for healthcare providers caring for patients using commercially low-cost otoscope for at home image capture.
This project is funded in whole or in part with Federal funds from the Advanced Research Projects Agency for Health (ARPA-H), Department of Health and Human Services, under Contract No. 75N91023C00045.
A significant volume of research has been published over the past several years about diagnosing ear conditions through the AI methodology of deep learning on otoscopic images and supporting the accuracy of this analysis. Since 2020, studies have primarily trained convolutional neural networks to detect the tympanic membrane, with a strong focus on otitis media.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 1 Year 至 17 Years（Child）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patient must be aged 1 to 17 years old.
- •Patient must present with a primary middle ear complaint.
- •Parent of the pediatric patient must have the ability and willingness to provide informed consent and comply with study procedures and visits.
- •Participants must have access to the required technology (e.g., smartphone with internet access) and be capable of using the provided off-the-shelf otoscope for the required image capture.
排除标准
- •Patients who are unable to comply with study procedures due to physical or mental health limitations (as assessed by study coordinator).
研究组 & 干预措施
Standard of care
Standard of care
干预措施: Smartphone-enabled otoscopy (Diagnostic Test)
结局指标
主要结局
Provider-Rated Usefulness
时间窗: Through study completion, an average of 14 days
Primary care providers' rating of the usefulness of the Belle AI in supporting the assessment of middle ear conditions utilizing an exit survey where higher scores are more favorable than lower scores.
Accuracy of Belle AI Image References
时间窗: Through study completion, an average of 14 days
Comparison of Belle AI's image reference system primary probability diagnosis to the reference diagnosis established by the medical review committee; additional evaluation of the accuracy of the second and third highest probability diagnoses determined by the Bell Image Match Score.
次要结局
- Agreement between diagnoses(Through study completion, an average of 14 days)
- Cost-Impact Analysis(Through study completion, an average of 14 days)