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REVIEW 6 major objections 5 minor 159 references

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights

T0 review · 6 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This systematic review sets out to be the first comprehensive map of machine learning in congenital heart disease diagnosis: it aggregates 432 Scopus-indexed papers, reviews 74 in depth, and tabulates 22 datasets across eight diagnostic…

desk verdict The survey has the right aim but the central tables don't hold up; I wouldn't cite it as printed, though a careful audit could make it useful. read the letter →

arxiv 2501.04493 v1 pith:ZUHDP3UJ submitted 2025-01-08 eess.IV cs.AIcs.CV

Khalil Khan , Farhan Ullah , Ikram Syed , Irfan Ullah This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords congenitalheartdiseasemachinelearningdeepsystematicliteraturereviewmedicalimagingdatasetsechocardiographyelectrocardiogramdiagnosticmodalities
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

This paper is a systematic literature review of machine learning (ML) and deep learning (DL) applied to congenital heart disease (CHD) diagnosis. It aggregates 432 Scopus-indexed papers published between 2018 and 2024, selects 74 for detailed review, and compiles the datasets, algorithms, evaluation metrics, and reported results into two large tables. The authors claim this is the first review in the field to offer a complete public dataset inventory, spanning heart sounds, X-rays, echocardiography, MRI, ECG, CT, pulse oximetry, and non-clinical data. They report that DL methods dominate (81% of the 74 studies), echocardiography is the most-used modality, only about half of the datasets are publicly available, and the main barriers are data scarcity, annotation cost, and privacy. If the compilation is accurate, it gives new researchers a single reference for choosing datasets and methods, while highlighting gaps such as the unexplored use of vision transformers and synthetic data.

What carries the argument

The load-bearing mechanism is the two extraction tables. Table 5 catalogs 22 datasets across eight modalities, giving availability, age range, and CHD types; Table 6 records each of the 74 studies' ML method, data type, dataset, and reported evaluation metrics. These tables ground every trend claim in the paper: the DL-to-classical ratio, the modality distribution, the accuracy comparisons, and the public-data share. The screening funnel described in the methods (from 4065 initial records down to 74 full-text reviews) is the selection machinery that produces these tables.

What would settle it

Take any ten rows of Table 6, open the cited primary papers, and check whether the dataset name, data type, method, and metric values match. If more than two of the ten rows misreport the source, the review's per-study performance comparisons and its trend claims lose their evidentiary basis.

Watch

Extended reading notes

Core claim

The central discovery is the landscape itself: CHD-ML research is small, recent, and fragmented. Across 74 studies, DL outnumbers classical ML by 60 to 14; CNNs are the most common architecture; echo is the leading modality at 26%, followed by CMR and ECG at 18% each; and only 51% of the reported datasets are public. The paper's contribution is a structured inventory in Tables 5 and 6 that maps each study to its modality, algorithm, dataset, and evaluation metrics, alongside a bibliometric profile of contributing countries, journals, publishers, and funding patterns.

Load-bearing premise

The load-bearing premise is that Tables 5 and 6 faithfully transcribe each of the 74 studies' dataset, method, and metrics, and that the Scopus-based screening captured the relevant literature; internal evidence, including one reference used for two different works, a perfect Dice score of 1, and a dataset attributed to the wrong source paper, shows that premise is not fully satisfied.

Editorial extensions

If this is right

  • A researcher entering CHD-ML can use Table 5 to locate a public dataset for echo, MRI, ECG, CT, or heart-sound work without re-searching the literature.
  • The 81% DL share and the reported metric ranges indicate that hand-crafted feature approaches are largely superseded, though the paper notes direct comparisons across studies are blocked by dataset heterogeneity.
  • The claim that only 51% of datasets are public identifies reproducibility as a structural problem of the field, not a per-project oversight.
  • The modality distribution (echo 26%, CMR and ECG 18% each) points to where annotation bottlenecks and clinical validation efforts will concentrate.
  • The review's stated gaps, such as vision transformers, synthetic data, and interdisciplinary collaboration, define concrete next projects for the community.

Reading between the lines

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

  • Because the table rows show internal inconsistencies (one reference assigned to two different studies, a reported Dice of exactly 1, and a dataset attributed to the wrong source paper), the per-study metric columns should be treated as unreliable even if the dataset inventory and broad trend claims survive.
  • If only half of the datasets are public, the reported state-of-the-art accuracies are likely optimistic; a community benchmark with standardized splits and hidden test sets would be a natural extension that the paper points toward but does not propose.
  • The review's own evidence that GAN-generated synthetic images train segmenters within about 1% of real-data performance suggests that synthetic data, rather than larger real collection, is a promising route to break the annotated-data bottleneck.
  • A machine-readable version of Tables 5 and 6, with stable dataset identifiers and links to each primary study, would convert this static review into a living map; without such a resource, the inventory will age quickly as new datasets and models appear.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

6 major / 5 minor

Summary. The paper claims to present a systematic review and meta-analysis of 432 references on machine learning for congenital heart disease (CHD) recognition between 2018 and 2024, with an in-depth review of 74 primary studies. It organizes the field by diagnostic modality (PCG, X-ray, echo, pulse oximetry, cardiac MRI, ECG, CT, and non-clinical data), lists reported datasets in Table 5, summarizes methods and evaluation metrics in Table 6, and discusses challenges, research gaps, and future directions. The central claim is that this is the first systematic literature review to provide a comprehensive inventory of CHD-ML datasets and a comparison of ML/DL algorithms.

Significance. If the data extraction were reliable, the paper would be a useful one-stop inventory for the CHD-ML community: it assembles 22 datasets across 8 modalities, tabulates 74 primary studies with algorithms and metrics, follows a PRISMA-style protocol, and includes a candid limitations section (Section 6.4). The paper's main strengths are its breadth of coverage and its explicit attempt to catalog public datasets, a dimension that previous reviews summarized in Table 1 apparently omit. However, the paper's value is entirely contingent on the fidelity of its tables and bibliometric counts; the internal inconsistencies documented below mean that, as printed, the synthesis does not yet support the abstract's claims. The authors also deserve credit for acknowledging the lack of medical co-authorship and the resulting limits on clinical depth.

major comments (6)
  1. [§1.1, §2.2, Fig. 2] The reported corpus size is inconsistent: the abstract says 432 references, Section 1.1 says 422 research publications, Section 2.2 says 432 articles, and the PRISMA flow diagram (Fig. 2) shows n=432 and then lists 'Excluded 422' next to the 'Paper for full text review (n=74)' box. The arithmetic of the screening stages (4065 to 2240 to 1920 to 1266 to 980 to 854 to 432 to 74) should be spelled out with correct exclusion counts. If the corpus is 422, the abstract and all bibliometric figures must be updated; if it is 432, Section 1.1 must be corrected.
  2. [Table 6] Table 6 contains multiple duplicate citation labels that make the extraction unreliable. Citation [40] labels both 'Arnaout et al.' and 'Rima et al.' in the 2023 block, although reference [40] is the 2021 Nature Medicine paper by Arnaout et al.; citation [51] is used for two different method/dataset combinations in the same block; and citation [128] is assigned to three rows with different data types (Echo/CT/MRI, non-clinical, and CT). Since Section 6.3.4 derives its TML-vs-DL comparison from Table 6, the table must be re-verified against the primary sources and corrected before the review's conclusions can be evaluated.
  3. [Table 6, Diller et al. [118]] The Table 6 row for Diller et al. [118] reports DS c = 1 with no dataset listed. A perfect Dice coefficient on real cardiac MRI is implausible, and Section 5.2.2 describes the same work as reporting a 'segmentation accuracy discrepancy of less than 1%' between synthetic- and real-trained U-Nets. The table should either state the exact metric and dataset or be corrected to the value reported in the original paper.
  4. [§5.1] Section 5.1 misattributes the Shanxi maternal-risk dataset to reference [45]. The text says 'The work proposed in [45] used a dataset collected from Shanxi Province, China' and describes SVM/RF/LR classification of maternal risk factors. Reference [45] is Liu and Kim's ECG-LSTM paper, which Section 5.2.3 correctly describes as an ECG heartbeat classification study. The Shanxi dataset belongs to Luo et al. [83], as the 2018 row of Table 6 itself indicates. This error directly affects the claimed mapping between algorithms and non-clinical data.
  5. [§4, §5.2.8, Table 5] The paper repeatedly assigns the same citation numbers to unrelated studies. Section 5.2.8 attributes to Khan et al. [136] a graph-matching whole-heart CT segmentation method that corresponds to Xu et al. [46], while reference [136] is the authors' SKIMA 2023 paper. Table 5 lists HSS [61] with age range 21-88 years although Section 4.1.3 describes the same dataset as 170 babies. Table 5 also attributes the CirCor DigiScope dataset to reference [60], which in the reference list is an echocardiography-mentoring paper, not the CirCor dataset. Because the dataset inventory is a main claimed contribution, these citation-to-content mismatches must be systematically repaired.
  6. [Abstract, §2] The abstract states the paper conducts 'a meta-analysis of 432 references,' but the Methods section describes a PRISMA-compliant systematic review with qualitative synthesis only; no effect-size pooling or quantitative meta-analytic estimate is presented anywhere in Sections 5-7. The term 'meta-analysis' should be removed from the abstract unless actual pooling (e.g., of reported accuracy or Dice metrics) is added.
minor comments (5)
  1. [§2.1, Fig. 2, Table 4] The typo 'congential' appears in the search keywords in Section 2.1, in Figure 2, and in Table 4; please correct to 'congenital' throughout.
  2. [§4.2.1, Table 5] The DICOM radiograph dataset is cited as [64] in the text and [78] in Table 5; neither reference appears to correspond to a publicly available 828-radiograph CHD dataset. Please add the correct citation or reconcile the two entries.
  3. [§3.5, Table 4] The frequencies in Table 4 are internally inconsistent with the prose: the text says 'congenital heart disease' appears 40 times as a trigram, but the following paragraph says it appears 35 times; please reconcile these counts.
  4. [§6.2.4, Table 5] Section 6.2.4 says 'only 51% datasets are publicly available,' while Table 5 lists 22 datasets with 11 marked '✓' (50%); please either correct the percentage or justify the rounding, and define whether 'publicly available' includes restricted-access datasets.
  5. [Table 1] Table 1's row numbering jumps from 3 to 5, omitting S. No. 4; please renumber the rows.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review makes no derivation-based prediction, and its self-citations are background rather than load-bearing.

full rationale

This paper is a systematic literature review with no mathematical derivation, fitted parameters, or predictive model. Its central claims, that 432 records were screened, that 74 papers were reviewed in depth, that deep learning is used in 81% of surveyed works, and that certain datasets and modalities dominate the literature, are descriptive summaries of the reviewed references and of the authors' own extraction tables. The only self-referential elements are background citations to the authors' prior work (e.g., [19], [85]–[97]) and the inclusion of the authors' own CHD segmentation study [136] as one surveyed method. None of these is load-bearing: removing them would not change any conclusion, and no surveyed trend, dataset inventory, or algorithm summary is justified by a self-citation chain. The extraction errors in Tables 5 and 6, including duplicate reference numbers [40], [51], and [128], a reported Dice score of 1 for Diller et al., and the assignment of the Shanxi dataset to [45] instead of [83], are internal-consistency and reproducibility problems, not circular reasoning; the tables are asserted as observations of the primary literature rather than derived from the paper's conclusions. Accordingly, no step in the claimed derivation chain reduces to its own inputs, and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey's central claims rest on the adequacy of the Scopus search and the accuracy of the authors' extraction from the 74 primary studies. These are domain assumptions rather than mathematical axioms; no free parameters or invented entities are introduced.

assumptions (3)
  • domain assumption Scopus alone indexes a representative and sufficient corpus of CHD-ML research for a comprehensive systematic review.
    Stated in Section 2.1 as the sole data source; affects all bibliometric findings.
  • domain assumption The 74 selected papers are accurately transcribed into Tables 5 and 6 in terms of methods, datasets, and evaluation metrics.
    The central synthesis is built on these tables; internal citation errors in Table 6 contradict this assumption.
  • domain assumption PRISMA criteria provide an adequate methodological foundation for calling this a systematic review.
    Section 2 invokes PRISMA but does not report a completed PRISMA checklist or protocol registration.

how reviews work

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Cite this review

Pith. "Pith review of The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights." pith.science (2026). https://pith.science/paper/ZUHDP3UJ

@misc{pith2026250104493,
  author       = {Pith},
  title        = {Pith review of: The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZUHDP3UJ}},
  note         = {Machine review of arXiv:2501.04493}
}
read the original abstract

Congenital heart disease is among the most common fetal abnormalities and birth defects. Despite identifying numerous risk factors influencing its onset, a comprehensive understanding of its genesis and management across diverse populations remains limited. Recent advancements in machine learning have demonstrated the potential for leveraging patient data to enable early congenital heart disease detection. Over the past seven years, researchers have proposed various data-driven and algorithmic solutions to address this challenge. This paper presents a systematic review of congential heart disease recognition using machine learning, conducting a meta-analysis of 432 references from leading journals published between 2018 and 2024. A detailed investigation of 74 scholarly works highlights key factors, including databases, algorithms, applications, and solutions. Additionally, the survey outlines reported datasets used by machine learning experts for congenital heart disease recognition. Using a systematic literature review methodology, this study identifies critical challenges and opportunities in applying machine learning to congenital heart disease.

Figures

Figures reproduced from arXiv: 2501.04493 by the authors.

Figure 1
Figure 1. The structure of the proposed SLR. literature review methods, lacking adherence to SLR methodologies [33, 29]. Most research works also failed to specify the time-frames of the examined literature, as observed in [25, 27, 30, 31, 29, 33]. Our proposed study over￾comes these limitations by adopting an SLR approach, offering a comprehensive analysis of CHD-ML encompassing datasets, algorithms, and evaluation metrics. … view at source ↗
Figure 2
Figure 2. PRISMA flow diagram used in this research. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Total research papers published per year (2018-2024), papers with funding, and without funding. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Publishers that contributed research publications to CHD-ML. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Countries that contributed in the form of publications to CHD-ML. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: The word count of the most frequent key words in the titles. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Publications on CHD-ML based on subject areas. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Research papers reported so far for each modality using CHD-ML. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.