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OpenTrials
Completed

NCT Number: NCT05268263

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

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.

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Key information

Conditions

Respiratory Lung

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Lady Reading Hospital, Pakistan

Peshawar, 25000, Pakistan

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. 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

  2. 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

Secondary outcomes

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

    Time frame: 2 months

    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

Sponsors and collaborators

Lead sponsor

Innova Smart Technologies (Pvt.) Ltd

Other

Collaborators

  • Lady Reading Hospital, Pakistan
  • NOABIO LLC

Registry information

Official study title

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

Important dates

Study start
2022
Primary completion
2022
Study completion
2022
First posted
Mar 7, 2022
Registry last updated
Apr 6, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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