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

NCT05268263 | Completed FDA Device

• 1 other identifier: Pulmo AI LRH
• Study Type: interventional
• Target: 60
• Locations: 1 country
• Sites: 1

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.

Conditions

Respiratory; Lung

Outcome Measures

Primary Outcomes (2)

• 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

• 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 Outcomes (1)

• Performance analysis of three digital stethoscopes: Littmann, NoaScope, and eSteth

  2 months

Interventions

Artificial Intelligence Algorithm DEVICE

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.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Ages all
• Written consent provided

You may not qualify if:

• Subject condition unstable
• Chest wall deformity or wounds in adhesive application areas
• Written consent not provided

Sponsors & Collaborators

• Innova Smart Technologies (Pvt.) Ltd: lead
• Lady Reading Hospital, Pakistan: collaborator
• NOABIO LLC: collaborator

Study Sites (1)

Lady Reading Hospital, Pakistan

Peshawar, 25000, Pakistan

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 8, 2022
• First Posted: March 7, 2022
• Study Start: January 6, 2022
• Primary Completion: February 22, 2022
• Study Completion: February 22, 2022
• Last Updated: April 6, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will not share

Locations

PA (1)