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