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

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.

Study Overview

• Status: Completed
• Conditions: Lung; Respiratory
• Intervention / Treatment: Device: Artificial Intelligence Algorithm

• Study Type: Interventional
• Enrollment (Actual): 60
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Pakistan
  • Peshawar, Pakistan, 25000
    • Lady Reading Hospital, Pakistan

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Innova Smart Technologies (Pvt.) Ltd
• Collaborators: Lady Reading Hospital, Pakistan; NOABIO LLC

Study record dates

Study Major Dates

• Study Start (Actual): January 6, 2022
• Primary Completion (Actual): February 22, 2022
• Study Completion (Actual): February 22, 2022

Study Registration Dates

• First Submitted: January 8, 2022
• First Submitted That Met QC Criteria: February 22, 2022
• First Posted (Actual): March 7, 2022

Study Record Updates

• Last Update Posted (Actual): April 6, 2023
• Last Update Submitted That Met QC Criteria: April 4, 2023
• Last Verified: April 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Signs and Symptoms, Respiratory; Respiratory Sounds
• Other Study ID Numbers: Pulmo AI LRH

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: Yes