Evaluating the Feasibility of Artificial Intelligence Algorithms in Clinical Settings for Classification of Normal, Wheeze and Crackle Sounds Acquired From a Digital Stethoscope
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Respiratory
- Sponsor
- Innova Smart Technologies (Pvt.) Ltd
- Enrollment
- 60
- Locations
- 1
- Primary Endpoint
- Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard
- Status
- Completed
- Last Updated
- 3 years ago
Overview
Brief Summary
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.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Written consent provided
Exclusion Criteria
- •Subject condition unstable
- •Chest wall deformity or wounds in adhesive application areas
- •Written consent not provided
Outcomes
Primary Outcomes
Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard
Time Frame: 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
Time Frame: 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
Secondary Outcomes
- Performance analysis of three digital stethoscopes: Littmann, NoaScope, and eSteth(2 months)