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The Machine Learned Stethoscope Provides Accurate Operator Independent Diagnosis of Chest Disease

Authors Kotb MA, Elmahdy HN, Seif El Dein HM, Mostafa FZ, Refaey MA, Rjoob KWY, Draz IH, Basanti CWS

Received 28 June 2019

Accepted for publication 3 December 2019

Published 23 January 2020 Volume 2020:13 Pages 13—22


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Dr Scott Fraser

Magd Ahmed Kotb,1 Hesham Nabih Elmahdy,2 Hadeel Mohamed Seif El Dein,3 Fatma Zahraa Mostafa,1 Mohammed Ahmed Refaey,2 Khaled Waleed Younis Rjoob,2 Iman H Draz,1 Christine William Shaker Basanti1

1Department of Pediatrics, Faculty of Medicine, Cairo University, Cairo, Egypt; 2Information Technology Department, Vice-Dean of Faculty of Computers and Information, Cairo University, Giza, Egypt; 3Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Cairo University, Cairo, Egypt

Correspondence: Magd Ahmed Kotb
Cairo University, 5, Street 63 El Mokatam, Cairo 11571, Egypt
Tel +20 2 2508 4994

Introduction: Contemporary stethoscope has limitations in diagnosis of chest conditions, necessitating further imaging modalities.
Methods: We created 2 diagnostic computer aided non-invasive machine-learning models to recognize chest sounds. Model A was interpreter independent based on hidden markov model and mel frequency cepstral coefficient (MFCC). Model B was based on MFCC, hidden markov model, and chest sound wave image interpreter dependent analysis (phonopulmonography (PPG)).
Results: We studied 464 records of actual chest sounds belonging to 116 children diagnosed by clinicians and confirmed by other imaging diagnostic modalities. Model A had 96.7% overall correct classification rate (CCR), 100% sensitivity and 100% specificity in discrimination between normal and abnormal sounds. CCR was 100% for normal vesicular sounds, crepitations 89.1%, wheezes 97.6%, and bronchial breathing 100%. Model B’s CCR was 100% for normal vesicular sounds, crepitations 97.3%, wheezes 97.6%, and bronchial breathing 100%. The overall CCR was 98.7%, sensitivity and specificity were 100%.
Conclusion: Both models demonstrated very high precision in the diagnosis of chest conditions and in differentiating normal from abnormal chest sounds irrespective of operator expertise. Incorporation of computer-aided models in stethoscopes promises prompt, precise, accurate, cost-effective, non-invasive, operator independent, objective diagnosis of chest conditions and reduces number of unnecessary imaging studies.

Keywords: machine learned stethoscope, operator independent diagnosis, chest, correct classification rate, CCR, normal vesicular sounds, crepitations, ACA, automatic chest auscultation, wheezes

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