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

Lung Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05268263
• Other study ID #: Pulmo AI LRH
• Status: Completed
• Phase: N/A
• Start date: January 6, 2022
• Est. completion date: February 22, 2022

Study information

• Verified date: April 2023
• Source: Innova Smart Technologies (Pvt.) Ltd
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 60
• Est. completion date: February 22, 2022
• Est. primary completion date: February 22, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Ages all

• Written consent provided

Exclusion Criteria:

• Subject condition unstable

• Chest wall deformity or wounds in adhesive application areas

• Written consent not provided

Related Conditions & MeSH terms

• Lung
• Respiratory
• Respiratory Sounds

Intervention

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.

Locations

Country	Name	City	State
Pakistan	Lady Reading Hospital, Pakistan	Peshawar

Sponsors (3)

Lead Sponsor	Collaborator
Innova Smart Technologies (Pvt.) Ltd	Lady Reading Hospital, Pakistan, NOABIO LLC

Country where clinical trial is conducted

Pakistan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Testing the accuracy of artificial intelligence models for detection of wheeze, crackles, and normal lung sounds by measuring the sensitivity and specificity	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	2 months
Primary	Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard	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	2 months
Secondary	Performance analysis of three digital stethoscopes: Littmann, NoaScope, and eSteth	Performance analysis of three digital stethoscopes NoaScope, eSteth, and Littmann will be evaluated using the sensitivity and specificity achieved by each stethoscope. 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	2 months