NCT05268263

Feasibility of AI-based Classification of Normal, Wheeze and Crackle Sounds From Stethoscope in Clinical Settings

Evaluating the Feasibility of Artificial Intelligence Algorithms in Clinical Settings for Classification of Normal, Wheeze and Crackle Sounds Acquired From a Digital Stethoscope

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 60 (actual)
• Sponsor: Innova Smart Technologies (Pvt.) Ltd · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

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.

Conditions

• Respiratory
• Lung

Interventions

Type	Name	Description
DEVICE	Artificial Intelligence Algorithm	The enrolled population will include patients with a history of lung pathologies. Artificial intelligence-based models are developed for classification of wheezes, crackles and normal lung sounds. These AI models will be tested and assessed on local lung sounds clinical data.

Timeline

• Start date: 2022-01-06
• Primary completion: 2022-02-22
• Completion: 2022-02-22
• First posted: 2022-03-07
• Last updated: 2023-04-06

Locations

1 site across 1 country: Pakistan

Regulatory

• FDA-regulated device study