Evaluating the Feasibility of Artificial Intelligence Algorithms in Clinical Settings for Classification of Normal, Wheeze and Crackle Sounds Acquired From a Digital Stethoscope
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 60
- 试验地点
- 2
- 主要终点
- Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard
研究概览
简要总结
Assessing the feasibility and testing the accuracy of the developed artificial intelligence algorithms for detection of wheezes and crackles in patients with lung pathologies in clinical settings on unseen local patient data acquired through three digital stethoscopes.
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
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Written consent provided
排除标准
- •Subject condition unstable
- •Chest wall deformity or wounds in adhesive application areas
- •Written consent not provided
结局指标
主要结局
Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard
时间窗: 2 months
AI models will be tested for their clinical feasibility through comparison of results obtained from AI models with that of the gold standard by measuring positive and negative agreement (NPA \& PPA). The gold standard is the label given to each lung sound recording by an experienced consultant pulmonologist. The AI model is blinded to these labels and is tested independently for detection of normal lung sounds, wheezes, and crackles
Testing the accuracy of artificial intelligence models for detection of wheeze, crackles, and normal lung sounds by measuring the sensitivity and specificity
时间窗: 2 months
Artificial intelligence models are trained on lung sounds collected from three different digital stethoscopes named NoaScope, eSteth, and Littmann individually. Data from all three digital stethoscopes is also merged to train separate AI models. These trained AI models will be evaluated based on sensitivity which is the ability to correctly identify wheezes and crackles, and specificity which is the ability to correctly identify normal lung sounds. True positive (TP), true negative (TN), false positive (FP), and false-negative (FN) values will be used to calculate sensitivity \& specificity using the following expressions. Sensitivity: TP/TP+FN Specificity: TN/TN+FP
次要结局
- Performance analysis of three digital stethoscopes: Littmann, NoaScope, and eSteth(2 months)