Evidence map›Paper›PMID 32158281›Full record

ArticleMedical devices (Auckland, N.Z.)2020

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

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

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical devices (Auckland, N.Z.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07542509 (Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]), which is not on this map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07542509 recruitingnot on this mapstarted 2024, after this paper: background citation

Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]

TypeobservationalSponsorIRCCS Azienda Ospedaliero-Universitaria di BolognaRan2024 to 2027Enrolled1,000ConditionsCardiac Disease, Auscultation of Heart, Machine Learning
3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Magd Ahmed KotbDepartment of Pediatrics, Faculty of Medicine, Cairo University, Cairo, Egypt.ORCID 0000-0003-2118-3793
Hesham Nabih ElmahdyInformation Technology Department, Vice-Dean of Faculty of Computers and Information, Cairo University, Giza, Egypt.ORCID 0000-0001-6633-1652
Hadeel Mohamed Seif El DeinDepartment of Diagnostic and Interventional Radiology, Faculty of Medicine, Cairo University, Cairo, Egypt.
Fatma Zahraa MostafaDepartment of Pediatrics, Faculty of Medicine, Cairo University, Cairo, Egypt.ORCID 0000-0002-9624-2375
Mohammed Ahmed RefaeyInformation Technology Department, Vice-Dean of Faculty of Computers and Information, Cairo University, Giza, Egypt.
Khaled Waleed Younis RjoobInformation Technology Department, Vice-Dean of Faculty of Computers and Information, Cairo University, Giza, Egypt.ORCID 0000-0003-1540-0376
Iman H DrazDepartment of Pediatrics, Faculty of Medicine, Cairo University, Cairo, Egypt.
Christine William Shaker BasantiDepartment of Pediatrics, Faculty of Medicine, Cairo University, Cairo, Egypt.ORCID 0000-0002-3149-9233

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionContemporary stethoscope has limitations in diagnosis of chest conditions, necessitating further imaging modalities.

methodsWe 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)).

resultsWe 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%.

conclusionBoth 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.

Indexed as

ACAautomatic chest auscultationCCRchestcorrect classification ratecrepitationsmachine learned stethoscopenormal vesicular soundsoperator independent diagnosiswheezes

Identifiers

PMID32158281
PMCPMC6986244

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.