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arxiv: 2605.22968 · v1 · pith:5SU77MWXnew · submitted 2026-05-21 · 🧬 q-bio.QM · cs.LG· stat.ML

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics

Mitchel J. Colebank This is my paper

Pith reviewed 2026-05-25 05:27 UTC · model grok-4.3

classification 🧬 q-bio.QM cs.LGstat.ML
keywords structural heart diseaseelectrocardiographyechocardiographyBayesian neural networksuncertainty quantificationtriagemachine learning classificationECG screening
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The pith

Bayesian neural networks match or exceed frequentist classifiers for structural heart disease from ECG data while supplying more robust uncertainty estimates for triage.

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

The paper compares frequentist and Bayesian neural network classifiers trained on paired ECG and echocardiogram records to detect structural heart disease. It establishes that the Bayesian versions perform comparably or better and attach more reliable uncertainty measures to their outputs. These measures support a triage workflow that routes high-likelihood or high-uncertainty cases to expert sonographers, addressing review bottlenecks in rural or underserved settings. A sympathetic reader would care because the work shows how probabilistic methods can make low-cost ECG screening more trustworthy for clinical decision-making.

Core claim

We leverage existing ECG-echocardiogram data to compare frequentist and Bayesian neural network classifiers. We show that the Bayesian approach is comparable or better than frequentist methods in SHD classification, and that they have a more robust uncertainty quantification attached to them. We provide an example of how this uncertainty-aware classification scheme can be used for screening SHD, providing a proof-of-concept for how machine learning can help with triage in getting individuals expert sonographer input when SHD is highly likely or measurements are highly uncertain.

What carries the argument

Bayesian neural network classifiers with uncertainty quantification trained on the EchoNext paired ECG-echocardiogram repository for SHD classification and triage.

If this is right

  • Uncertainty estimates from Bayesian models can flag cases for immediate expert review when SHD probability is high or uncertainty is large.
  • Triage systems built on these estimates can reduce unnecessary expert review of low-risk rural clinic data.
  • Probabilistic classification offers a direct path to integrate ECG screening into clinical workflows with quantified reliability.
  • The same uncertainty-aware pipeline can be applied to other paired noninvasive measurement modalities for cardiovascular screening.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Real-world deployment in rural clinics could test whether the triage actually shortens wait times for expert sonography.
  • Combining the uncertainty outputs with additional patient metadata might further refine the triage thresholds.
  • The method could be extended to longitudinal ECG monitoring to track changes in uncertainty over time.

Load-bearing premise

The EchoNext paired ECG-echocardiogram repository contains labels and distributions representative enough for both training the models and for the downstream triage use case.

What would settle it

An independent ECG-echocardiogram dataset in which Bayesian classifiers show lower accuracy or poorer uncertainty calibration than frequentist counterparts would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2605.22968 by Mitchel J. Colebank.

Figure 1
Figure 1. Figure 1: Distribution of values for covariates used in the three designs 𝐷1 , 𝐷2 , and 𝐷3 . PR: PR interval from ECG; QRS: QRS duration; QT: QT interval length; Atr Con: atrial contraction rate; Vent Con: ventricular contraction rate; Peri Eff: possible pericardial effusion; IVS: interventricular septal width; PWT: posterior wall thickness; TR Max Velocity: maximum velocity of regurgitation at the tricuspid valve; … view at source ↗
Figure 2
Figure 2. Figure 2: (Left) Receiver Operating Characteristic (ROC) curve for neural networks under the frequentist and Bayesian paradigm. Frequentist ROCs include 95% confidence intervals while Bayesian ROCs include 95% credible intervals, both also including the mean. Training data include 70,000 measurements, and results are shown for the 20,000 testing data points. (Right) Precision￾Recall Curves (PRCs) for the correspondi… view at source ↗
Figure 3
Figure 3. Figure 3: (Left) Receiver Operating Characteristic (ROC) curve for neural networks under the frequentist and Bayesian paradigm. Frequentist ROCs include 95% confidence intervals while Bayesian ROCs include 95% credible intervals, both also including the mean. Training data include 8,000 measurements, and results are shown for the 2,000 testing data points. (Right) Precision-Recall Curves (PRCs) for the corresponding… view at source ↗
Figure 4
Figure 4. Figure 4: Posterior predictions over the probabilities for test data from the 70,000 training and 20,000 testing dataset. Results are shown for 𝐷2, with specific thresholds for class decisions or “inconclusive” decisions (see main text). All models are neural network with 10 neurons, and a prior variance of 𝜎 2 = 0.25. Results compare three layers (top row), five layers (middle row), and ten layers (bottom row) acro… view at source ↗
Figure 5
Figure 5. Figure 5: Confusion matrices for the (a) 3-layer, (b) 5-layer, and (c) 10-layer neural networks trained in a Bayesian framework with 10 neurons in each layer and a prior variance of 𝜎 2 = 0.25. Predictions that do not satisfy the quantile rules described earlier are deemed “inconclusive.” Bayesian methods might be preferred when data are con￾sidered “trust worthy” only. Nevertheless, we find sufficient evidence here… view at source ↗
read the original abstract

Machine learning methods provide a methodological innovation that can help screen for cardiovascular disease through noninvasive and readily available measurement modalities. Recent investments in using electrocardiogram (ECG) data to screen for structural heart disease (SHD) are one example, where ECGs provide a low-cost, available modality for screening. This has led to the EchoNext dataset, a paired ECG-echocardiogram data repository for testing new methods of SHD detection. However, relatively few studies have investigated how more probabilistic classification through Bayesian inference may improve uncertainty quantification in this setting. Moreover, few studies have considered how triage systems can be developed to alleviate healthcare bottlenecks, such as the review of data from underserved, rural clinics by expert sonographers for SHD assessment. In this study, we leverage existing ECG-echocardiogram data to compare frequentist and Bayesian neural network classifiers. We show that the Bayesian approach is comparable or better than frequentist methods in SHD classification, and that they have a more robust uncertainty quantification attached to them. We provide an example of how this uncertainty-aware classification scheme can be used for screening SHD, providing a proof-of-concept for how machine learning can help with triage in getting individuals expert sonographer input when SHD is highly likely or measurements are highly uncertain.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript compares frequentist and Bayesian neural network classifiers for detecting structural heart disease (SHD) from paired ECG-echocardiogram data in the EchoNext repository. It claims that Bayesian models are comparable or better in classification performance while providing more robust uncertainty quantification, and demonstrates a proof-of-concept for using this uncertainty to triage cases for expert sonographer review, particularly in underserved rural clinics.

Significance. If the quantitative claims hold after adding missing metrics and validation, the work could support uncertainty-aware ML for low-cost SHD screening and triage, addressing access bottlenecks. The focus on Bayesian methods for medical decision support is a constructive direction, though the current lack of performance numbers, calibration evidence, and shift testing limits its immediate contribution.

major comments (2)
  1. [Abstract] Abstract: The claim that 'the Bayesian approach is comparable or better than frequentist methods in SHD classification' and that 'they have a more robust uncertainty quantification' is presented without any numerical performance metrics (e.g., accuracy, AUC, F1), calibration plots, Brier scores, or details on uncertainty measurement/thresholding. This is load-bearing for the central claim and prevents evaluation.
  2. [Abstract] Abstract / triage example: The downstream use case for rural-clinic triage assumes EchoNext labels and distributions generalize to underserved populations. No external validation, demographic stratification, prevalence shift testing, or ECG-quality robustness checks are described, which directly affects transfer of both classification parity and uncertainty robustness.
minor comments (1)
  1. [Abstract] The abstract refers to a 'proof-of-concept' triage scheme but supplies no implementation details, thresholds, or example outputs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and have made revisions to strengthen the presentation of results and limitations.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'the Bayesian approach is comparable or better than frequentist methods in SHD classification' and that 'they have a more robust uncertainty quantification' is presented without any numerical performance metrics (e.g., accuracy, AUC, F1), calibration plots, Brier scores, or details on uncertainty measurement/thresholding. This is load-bearing for the central claim and prevents evaluation.

    Authors: We agree that the abstract should include supporting numerical evidence. The full manuscript reports these metrics in the results (AUC, accuracy, F1, Brier scores, and uncertainty calibration details), but the abstract was overly concise. We have revised the abstract to explicitly state key performance figures (e.g., Bayesian AUC of 0.XX vs. frequentist 0.YY, improved Brier score, and uncertainty thresholding approach for triage) while remaining within length limits. revision: yes

  2. Referee: [Abstract] Abstract / triage example: The downstream use case for rural-clinic triage assumes EchoNext labels and distributions generalize to underserved populations. No external validation, demographic stratification, prevalence shift testing, or ECG-quality robustness checks are described, which directly affects transfer of both classification parity and uncertainty robustness.

    Authors: The referee is correct that the manuscript does not include external validation, demographic stratification, prevalence shift testing, or ECG-quality robustness checks. The work is presented as a proof-of-concept on the EchoNext dataset. We have added explicit language in the discussion section acknowledging these limitations and the assumptions required for transfer to underserved populations, along with recommendations for future validation studies. No new data were available to perform the requested external analyses. revision: partial

Circularity Check

0 steps flagged

No circularity; standard empirical comparison on external data

full rationale

The manuscript applies off-the-shelf frequentist and Bayesian neural-network classifiers to the external EchoNext paired ECG-echocardiogram repository. No custom derivations, equations, or parameter-fitting steps are presented that reduce to the paper's own inputs by construction. All performance and uncertainty claims rest on standard train/test splits of the provided dataset; no self-definitional loops, fitted-input-as-prediction, or load-bearing self-citations appear in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; all modeling details are omitted.

pith-pipeline@v0.9.0 · 5759 in / 978 out tokens · 25859 ms · 2026-05-25T05:27:36.425168+00:00 · methodology

discussion (0)

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Reference graph

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