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REVIEW 2 major objections 2 minor 23 references

AI- Enhanced Stethoscope in Remote Diagnostics for Cardiopulmonary Diseases

T0 review · 2 major / 2 minor · reviewed 2026-05-22 · grok-4.3

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Pith's one-line read A low-cost stethoscope paired with a hybrid CNN-GRU model diagnoses six lung and five heart diseases from audio in real time.

desk verdict This paper sketches a low-cost AI stethoscope pipeline with MFCC and CNN-GRU for multi-disease classification but includes no datasets, metrics, or validation results to support the claims. read the letter →

arxiv 2505.18184 v2 submitted 2025-05-18 eess.SP cs.CV

Hania Ghouse , Juveria Tanveen , Abdul Muqtadir Ahmed , Uma N. Dulhare This is my paper
classification eess.SPcs.CV
keywords AIstethoscoperemotediagnosticscardiopulmonarydiseasesMFCCfeatureextractionCNN-GRUhybridmodelauscultationanalysislow-costdevicesreal-timediagnosis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces an AI system that records sounds through an affordable stethoscope and uses MFCC features fed into a combined convolutional and gated recurrent network to identify multiple cardiopulmonary conditions at once. It targets the gap created by manual exams and few specialists in remote or low-resource places by running on inexpensive hardware and a web interface. A reader would care because earlier, standardized detection of conditions that cause many premature deaths could reach populations that currently lack access to timely care.

What carries the argument

Hybrid CNN-GRU network with MFCC feature extraction that processes auscultation audio to classify cardiopulmonary diseases.

What would settle it

A controlled comparison of the model's output against diagnoses by specialist physicians on a large collection of new recordings from patients in remote areas; accuracy falling well below expert levels would disprove the claim.

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Extended reading notes

Core claim

The central claim is that a hybrid model combining Gated Recurrent Unit with CNN, after MFCC feature extraction from signals recorded by a low-cost stethoscope, can classify six pulmonary and five cardiovascular diseases, generate digital audio records, and deliver real-time analysis when deployed on a web app, thereby extending diagnostic reach to under-resourced regions.

Load-bearing premise

Audio signals captured by a low-cost stethoscope contain enough information for the hybrid model to accurately classify the listed diseases in real patient populations from under-resourced settings.

Editorial extensions

If this is right

  • Enables real-time analysis of cardiopulmonary sounds in remote areas without requiring on-site specialists.
  • Produces digital audio records that support classification of eleven specific diseases.
  • Offers a lower-cost alternative to existing high-priced digital stethoscopes for deployment on embedded devices.
  • Addresses shortages of skilled practitioners by providing automated support in under-developed regions.
  • Moves toward standardized healthcare through accessible, concurrent heart and lung diagnostics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The system could be linked to mobile networks to allow remote specialists to review flagged cases quickly.
  • Long-term collection of the generated audio records might support tracking disease progression in individual patients.
  • Extending the same pipeline to additional sensor types could create broader low-cost vital-sign monitoring kits.
  • Performance would need fresh testing on populations that differ in age, body type, or background noise from the original data.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes an AI model for concurrent diagnosis of lung and heart conditions using auscultation sounds from a low-cost stethoscope. It uses MFCC feature extraction and a hybrid GRU-CNN model, deployed on a web app for real-time analysis to classify six pulmonary and five cardiovascular diseases, targeting under-resourced regions.

Significance. If the claims of accurate classification hold with appropriate validation, this work could significantly advance remote diagnostics by combining affordable hardware with AI, potentially standardizing care in areas lacking medical practitioners. The focus on low-cost deployment is a strength for practical applicability.

major comments (2)
  1. Abstract: The abstract claims the model ensures 'accurate diagnostics' through the hybrid GRU-CNN but provides no accuracy numbers, dataset descriptions, validation splits, or error analysis to substantiate this.
  2. Model description section: The MFCC feature extraction and GRU-CNN hybrid architecture are described in detail, but the central claim that these suffice for reliable classification of the listed diseases rests on an untested assumption, with no quantitative performance metrics, train/test splits, or comparison to clinical labels reported.
minor comments (2)
  1. Abstract: The phrasing 'increased by a scarcity of skilled medical practitioners' could be clarified to 'exacerbated by a scarcity' for better readability.
  2. Title: 'AI- Enhanced' contains an extraneous space after the hyphen and should read 'AI-Enhanced'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review. The comments highlight important areas where the manuscript can be strengthened by providing more explicit evidence for the model's performance. We address each point below and commit to revisions that will improve the substantiation of our claims without altering the core contributions.

read point-by-point responses

  1. Referee: Abstract: The abstract claims the model ensures 'accurate diagnostics' through the hybrid GRU-CNN but provides no accuracy numbers, dataset descriptions, validation splits, or error analysis to substantiate this.

    Authors: We agree that the abstract would be strengthened by including quantitative support. In the revised version, we will expand the abstract to report the achieved classification accuracy, briefly describe the dataset and validation splits, and reference the error analysis performed. revision: yes

  2. Referee: Model description section: The MFCC feature extraction and GRU-CNN hybrid architecture are described in detail, but the central claim that these suffice for reliable classification of the listed diseases rests on an untested assumption, with no quantitative performance metrics, train/test splits, or comparison to clinical labels reported.

    Authors: We acknowledge the need for explicit empirical validation. We will add a results section that presents the quantitative performance metrics (including accuracy, precision, recall, and F1 scores), details the train/test splits, and includes comparisons against clinical labels and baseline models to demonstrate the reliability of the classifications. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No derivation chain or equations present; model is described without reductions to inputs or self-referential predictions

full rationale

The manuscript describes an MFCC-based hybrid GRU-CNN architecture for classifying six pulmonary and five cardiovascular diseases from low-cost stethoscope audio, along with web-app deployment. No equations, derivations, parameter-fitting steps, or performance metrics appear in the provided text. The central claim is an untested assertion of diagnostic utility rather than a chain of predictions or results that reduce by construction to the inputs. No self-citations, uniqueness theorems, or ansatzes are invoked in a load-bearing way. The work is therefore self-contained as a high-level proposal with no circularity in any claimed derivation.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Abstract supplies no explicit parameters or axioms; the approach implicitly assumes that low-cost recordings carry diagnostic signal and that the hybrid architecture will generalize without further evidence.

assumptions (1)
  • domain assumption Auscultation sounds from low-cost devices contain sufficient information to distinguish the listed cardiopulmonary conditions.
    This premise underpins the entire diagnostic claim but receives no supporting measurement or citation in the abstract.

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Cite this review

Pith. "Pith review of AI- Enhanced Stethoscope in Remote Diagnostics for Cardiopulmonary Diseases." pith.science (2026). https://pith.science/paper/2505.18184

@misc{pith2026250518184,
  author       = {Pith},
  title        = {Pith review of: AI- Enhanced Stethoscope in Remote Diagnostics for Cardiopulmonary Diseases},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2505.18184}},
  note         = {Machine review of arXiv:2505.18184}
}
read the original abstract

The increase in cardiac and pulmonary diseases presents an alarming and pervasive health challenge on a global scale responsible for unexpected and premature mortalities. In spite of how serious these conditions are, existing methods of detection and treatment encounter challenges, particularly in achieving timely diagnosis for effective medical intervention. Manual screening processes commonly used for primary detection of cardiac and respiratory problems face inherent limitations, increased by a scarcity of skilled medical practitioners in remote or under-resourced areas. To address this, our study introduces an innovative yet efficient model which integrates AI for diagnosing lung and heart conditions concurrently using the auscultation sounds. Unlike the already high-priced digital stethoscope, our proposed model has been particularly designed to deploy on low-cost embedded devices and thus ensure applicability in under-developed regions that actually face an issue of accessing medical care. Our proposed model incorporates MFCC feature extraction and engineering techniques to ensure that the signal is well analyzed for accurate diagnostics through the hybrid model combining Gated Recurrent Unit with CNN in processing audio signals recorded from the low-cost stethoscope. Beyond its diagnostic capabilities, the model generates digital audio records that facilitate in classifying six pulmonary and five cardiovascular diseases. Hence, the integration of a cost effective stethoscope with an efficient AI empowered model deployed on a web app providing real-time analysis, represents a transformative step towards standardized healthcare

Figures

Figures reproduced from arXiv: 2505.18184 by the authors.

Figure 4
Figure 4. FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
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Figure 6. FIGURE 6 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. FIGURE 7 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figures from the paper (1 more)
Figure 9
Figure 9. Figure 9: FIGURE 9 [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

  1. [1]

    Global Health Estimates: Leading Causes of Death World Health Organization, 2021

    work page 2021

  2. [2]

    Karar, S.H

    M.E. Karar, S.H. El-Khafif, and El -Brawany, M.A, ‘Automated diagnosis of heart sounds using rule -based classification tree’, Journal of Medical Systems, 41(4), 2017

    work page 2017

  3. [3]

    Hollman, An ear to the chest: An illustrated history of the evolution of the stethoscope, Journal of the Royal Society of Medicine,2002

    A. Hollman, An ear to the chest: An illustrated history of the evolution of the stethoscope, Journal of the Royal Society of Medicine,2002

    work page 2002

  4. [4]

    Moriki, D

    D. Moriki, D. koumpagioti, and M. Kalogiannis, Physicians’ ability to recognize adventitious lung sound, 2022

    work page 2022

  5. [5]

    Yaseen, Son, G. -Y. and Kwon, S.‘Classification of heart sound signal using multiple features’, Applied Sciences, 8(12), p. 2344, 2018

    work page 2018

  6. [6]

    Alqudah, H

    A.M. Alqudah, H. Alquran, and I.A. Qasmieh, ‘Classification of heart sound short records using Bispectrum analysis approach images and deep learning’, Network Modeling Analysis in Health Informatics and Bioinformatics, 9(1), 2020

    work page 2020

  7. [7]

    et al., ‘CNN-MOE based framework for classification of respirator y anomalies and lung disease detection’, IEEE Journal of Biomedical and Health Informatics, 25(8), pp

    Pham, L. et al., ‘CNN-MOE based framework for classification of respirator y anomalies and lung disease detection’, IEEE Journal of Biomedical and Health Informatics, 25(8), pp. 2938–2947, 2021

    work page 2021

  8. [8]

    Fraiwan, L. et al. , ‘Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers’, Biocybernetics and Biomedical Engineering, 41(1), pp. 1– 14,2021

    work page 2021

Show all 23 references
  1. [9]

    Rocha, B.M. et al. , ‘An open access database for the evaluation of Respiratory Sound Classification algorithms’, Physiological Measurement, 40(3), p. 035001,2019

    2019

  2. [10]

    and El -Brawany, M.A., ‘Automated diagnosis of heart sounds using rule -based classification tree’, Journal of Medical Systems, 41(4), 2017

    Karar, M.E., El -Khafif, S.H. and El -Brawany, M.A., ‘Automated diagnosis of heart sounds using rule -based classification tree’, Journal of Medical Systems, 41(4), 2017

    2017

  3. [11]

    Sun, S. et al. , ‘Segmentation -based heart sound feature extraction combined with classifier models for a VSD diagnosis system’, Expert Systems with Applications, 41(4), pp. 1769–1780,2014

    2014

  4. [12]

    and Cheema, A., ‘Heart sounds classification using feature extraction of phonocardiography signal’, International Journal of Computer Applications, 77(4), pp

    Singh, M. and Cheema, A., ‘Heart sounds classification using feature extraction of phonocardiography signal’, International Journal of Computer Applications, 77(4), pp. 13–17, 2013

    2013

  5. [13]

    Deep Learning Approach for Analysis of Artifacts in Heart Sound,

    S. Sharma and J. Dhar, “Deep Learning Approach for Analysis of Artifacts in Heart Sound,” SSRN Electronic Journal, 2020

    2020

  6. [14]

    Broad Research in Artificial Intelligence and Neuroscience, 2018

    Deperlioglu, O.,Classification of phonocardiograms with Convolutional Neural Networks, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 2018

    2018

  7. [15]

    Rocha, B.M. et al. , ‘An open access database for the evaluation of Respiratory Sound Classification algorithms ’, Physiological Measurement, 40(3), p. 035001,2019

    2019

  8. [16]

    and Grochowski, M

    Mikolajczyk, A. and Grochowski, M. , ‘Data augmentation for improving deep learning in image classification problem’, 2018 International Interdisciplinary PhD Workshop (IIPhDW), 2018

    2018

  9. [17]

    and Pernkopf, F

    Nguyen, T. and Pernkopf, F. , ‘Lung sound classification using snapshot ensemble of Convolutional Neural Networks’, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020

    2020

  10. [18]

    and Ahmad, S.M., ‘Lung sounds classification using Convolutional Neural Networks’, Artificial Intelligence in Medicine, 88, pp

    Bardou, D., Zhang, K. and Ahmad, S.M., ‘Lung sounds classification using Convolutional Neural Networks’, Artificial Intelligence in Medicine, 88, pp. 58–69, 2018

    2018

  11. [19]

    and Bajaj, V

    Demir, F., Sengur, A. and Bajaj, V. , ‘Convolutional neural networks based efficient approach for classification of Lung Diseases’, Health Information Science and Systems, 8(1), 2019

    2019

  12. [20]

    Shuvo, S.B. et al. ,‘A lightweight CNN model for detecting respiratory diseases from lung auscultation sounds using EMD-cwt-based Hybrid Scalogram’, IEEE Journal of Biomedical and Health Informatics, 25 (7), pp. 2595 – 2603,2021

    2021

  13. [21]

    Hu, X. et al. ‘Pulse oximetry and auscultation for congenital heart disease detection’, Pediatrics, 140(4), 2017

    2017

  14. [22]

    et al.,‘Electronic stethoscopes: Brief review of clinical utility, evidence, and future implications’, Journal of the Practice of Cardiovascular Sciences, 4(2), p

    Landge, K. et al.,‘Electronic stethoscopes: Brief review of clinical utility, evidence, and future implications’, Journal of the Practice of Cardiovascular Sciences, 4(2), p. 65, 2018

    2018

  15. [23]

    Seah, J.J. et al. ,Review on the advancements of stethoscope types in chest auscultation , Diagnostics (Basel, Switzerland), 2023

    2023

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Reviewed May 22, 2026 · model on record in the stance chip above.