{"id":"f02b4fba-475a-49ea-b0ec-b610376e7aeb","arxiv_id":"2502.03322","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":8,"one_line_summary":"An end-to-end workflow automatically generates volumetric biatrial models and fast ECG P-wave simulations from patient CT scans, demonstrated on 50 AF patients.","lead":"An automated pipeline turns CT scans from 50 atrial fibrillation patients into 3D volumetric models of the atria and computes simulated ECG P-waves in near real time. The work aims to make patient-specific digital twins and large virtual cohorts feasible for studying atrial fibrillation and planning therapy.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Section 3.1 states 'Worst element quality was always below 0.99' but Section 4.3 claims quality 'exceeding a threshold of 0.99'; if the literal text holds, the meshes fail the stated critical threshold for R-D solvers, undermining the suitability claim.","rationale":"The reader's weakest assumption focuses on the R-D model's fidelity as a gold standard, which is a cross-cutting limitation of the field. I instead focus on an internal contradiction in the paper's own quantitative claims. The mesh quality threshold is central: the workflow's output is claimed to be 'suitable for most widely used forward EP models' and specifically to exceed a quality of 0.99, which is 'critical' for bidomain solvers. Section 3.1 says the worst element quality was 'always below 0.99', whereas Section 4.3 says it 'exceeded a threshold of 0.99'. These cannot both be true. If the literal Section 3.1 statement holds, the R-D meshes (0.25 mm) do not meet the stated threshold, which would invalidate the R-D simulations used as the gold standard in the R-E/R-D comparison (Section 3.3). This is not a matter of external validity but of internal consistency, making it more immediately load-bearing for the paper's claims. A concrete test—recomputing min quality on a sample—can settle it. I therefore recommend keeping the reader's CONDITIONAL verdict, pending clarification of this inconsistency.","tokens_in":37966,"tokens_out":9951,"duration_ms":89798,"concrete_test":"Obtain the meshes (or a random sample of, say, 10 of the 50) and compute the minimum element quality using the metric of [55], identifying whether the minimum is above or below 0.99. Also verify the definition of the quality metric (whether higher is better) and re-read the original text of Section 3.1 to see if 'below' is a typo for 'above'. If the minimum is below 0.99, the Section 4.3 claim is false and the R-D simulations may be unreliable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim that the workflow produces meshes suitable for reaction-diffusion simulations rests on the assertion that element quality exceeds a critical threshold of 0.99. However, Section 3.1 explicitly reports 'Worst element quality was always below 0.99' (with lower-quality elements near orifices), while Section 4.3 asserts 'achieving element quality exceeding a threshold of 0.99'. These statements are irreconcilable. If the former is correct, the R-D meshes fail the stated threshold, which would (a) contradict the 'suitable for R-D' claim, (b) cast doubt on the reliability of the R-D 'gold standard' simulations used in the R-E/R-D comparison (Section 3.3, Table 7), and (c) undermine the claim of 'excellent mesh quality' as a key advantage over prior work. The reader's weakest assumption concerned self-referential R-E/R-D validation; the mesh quality contradiction is a more direct, internally checkable inconsistency that affects the validity of the entire pipeline's output.","agreement_with_reader":"disagree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript describes an end-to-end computational workflow for generating volumetric biatrial models from patient CT images, annotating anatomical structures and fiber architecture, computing volumetric universal atrial coordinates, modeling inter-atrial conduction with auto-generated cables, and computing 12-lead atrial P-waves using a reaction-eikonal lead-field forward model. The workflow was evaluated on 50 atrial fibrillation patients, with reported processing times for coarse (0.90 mm, reaction-eikonal) and fine (0.25 mm, reaction-diffusion) meshes, an R-E versus R-D comparison of activation and P-waves, parametric studies of Bachmann bundle insertion and SAN location, and a one-patient pseudo-calibration of the simulated P-wave to clinical ECG data.","tokens_in":38328,"tokens_out":6837,"duration_ms":62728,"significance":"The paper's concrete strengths are the large cohort of 50 real CT datasets, the reported per-stage processing times, the automated anatomical labeling pipeline, the flexible cable-based representation of inter-atrial connections, and the very fast R-E forward model for P-wave computation. If the mesh-quality and automation claims are made internally consistent, and if the ECG-calibration claim is appropriately scoped, the framework would be a useful contribution to scalable atrial digital twin generation. The R-E/R-D comparison, while currently only a model-to-model consistency check, is valuable as a numerical verification, but it is not a clinical validation of the relationship between simulated and measured P-waves.","major_comments":[{"comment":"The mesh-quality claims are contradictory. Section 3.1 states \"Worst element quality was always below 0.99, according to the quality metric [55], which is considered a critical threshold in simulations using R-D solvers such as openCARP\", while Section 4.3 states \"achieving element quality exceeding a threshold of 0.99, which has been empirically identified as critical for cardiac bidomain simulations.\" These statements cannot both describe the same 50-model cohort. If the worst element quality is below 0.99, then the R-D meshes do not meet the stated critical threshold, which undermines both the \"suitable for R-D\" claim and the reliability of the R-D \"gold standard\" simulations used in Table 7. Please report the actual element-quality distribution, the minimum value, and the fraction of elements below the threshold, and correct the contradictory wording.","section":"§3.1 and §4.3"},{"comment":"The ECG-calibration demonstration is limited to a single patient and is essentially a fit to the data used for evaluation. Section 3.7 states \"One anatomical model of a patient was selected\", and the parameters (SAN location, RA conduction velocities, BB insertion sites) are chosen by minimizing RMSE against the measured P-wave, with no independent test set, cross-validation, or uncertainty quantification. Moreover, the simulated P-wave envelope does not cover the clinical signal in leads aVL, -aVR, and V1, and the Discussion attributes the V1 discrepancy to fibrotic tissue that is not modeled. The title's \"ECG calibrated\" claim is therefore stronger than what is demonstrated; the paper should reframe this as a feasibility demonstration of a pseudo-calibration workflow and explicitly state the identifiability and generalization limitations.","section":"§3.7 and Table 11"},{"comment":"The R-E/R-D comparison is presented as the validation of the fast forward model, but Section 3.3 says the results are \"illustrated for a representative test case\", and Table 7 reports a single row of RMSE values without indicating whether the 3.71% average is over the 50-patient cohort or one geometry. Please state the sample size and report per-case variability. In addition, because the R-D conductivities in Table 4 are calibrated with the ForCEPSS framework to reproduce the R-E conduction velocities, the RMSE in Table 7 measures model-to-model consistency under matched parameters, not accuracy against independent human electrophysiology; the \"gold standard\" wording in Section 3.3 should be qualified accordingly.","section":"§3.3, Table 7, §2.2.2 and Table 4"},{"comment":"The automation statistics are internally inconsistent. Section 3.1 states that \"all 50 cases in both resolutions were processed automatically in the majority of cases (38), with only minimal user intervention required in 22 cases\", which does not add to 50. Table 5 lists 19 and 22 cases requiring manual correction at the first two stages, while Section 3.1.1 reports 21 cases for the CS/IVC landmark switch and 22 cases for IVC discard-tissue adjustment. Since \"highly automated\" is a headline claim, please provide a coherent per-stage and per-case accounting (for example, a table of correction types and a clear statement of how many of the 50 cases required any manual intervention).","section":"§3.1, Table 5 and §3.1.1"},{"comment":"The reported computational performance of the R-E model is inconsistent. Section 3.2 states that computation of a full biatrial activation sequence with a high-fidelity ECG takes about 1.00 s, while Table 6 reports \"EP simulation ≈ 27 s\" for the 0.90 mm/R-E case, and Section 4.6 states \"A full single forward simulation lasted ≈ 27.00 s only where the actual evaluation of the EP model amounted only to ≈ 3.00 s.\" These numbers need to be reconciled with a precise definition of what is included in each timing (setup, lead-field computation, eikonal solve, ECG time-series generation) and on which hardware and mesh size they were measured.","section":"§3.2, Table 6 and §4.6"}],"minor_comments":[{"comment":"The global ECG scaling factor of 0.2 is introduced without justification; since the RMSE metric is amplitude-sensitive, the scaling should be reported as part of the model definition or the calibration procedure, not as an ad hoc post-processing step.","section":"§2.2.2"},{"comment":"There is a typo in \"electrophysiologicalglsep excitability\"; it should presumably be \"electrophysiological excitability\".","section":"§3.7"},{"comment":"The table heading contains a typo: \"surface-to-volume ration\" should be \"surface-to-volume ratio\".","section":"Table 4"},{"comment":"The sentence \"The origin of the cable in the RA was chosen superior to the CS, at a distance between 3 and 8 .00 mm [21] from the CS ostium\" could be clarified, since a range without a prescription for how it was chosen in the implemented models makes the parameter not fully reproducible.","section":"§2.2.1"},{"comment":"The Limitations section is candid about the uniform wall-thickness assumption and the cable approximation for inter-atrial connections, but the Discussion in Section 4.6 still uses the P-wave fit results as evidence of clinical compatibility; the wording should be adjusted to match the caveats stated in Section 5.","section":"§5"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is a methods/engineering contribution and fits the journal's scope, but the editorial decision should rest on whether the mesh-quality contradiction, the single-patient calibration demonstration, and the timing inconsistencies are resolved. The paper would be considerably stronger if the R-E/R-D validation were reported over the full cohort and if the calibration were either reframed as a feasibility demonstration or supported by an out-of-sample evaluation."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know. First, this is a genuinely useful engineering paper: it automates volumetric biatrial model generation from 50 clinical CTs, introduces volumetric UACs computed directly on volume meshes, uses parametric cable-based inter-atrial connections, and couples a reaction-eikonal forward model to lead-field ECG. The per-model timings and automation rates are concrete and impressive. Second, the \"ECG calibrated\" framing is ahead of the evidence. Only one patient's P-wave is used, with parameters interactively fitted and a template torso, and the authors admit in the limitations that they refrained from attempting real calibration. Treat it as a feasibility demonstration, not validation.\n\nWhat the paper does well: the 50-patient workflow evaluation is the real contribution. Mesh generation, label augmentation, orifices, fibers, and UACs run with the stated automation, and the paper documents where manual correction was needed. The limitations section is unusually honest—it flags identifiability, uniform wall thickness, cable approximation, UAC cost on fine meshes, and lack of torso images. That honesty makes the overclaim in the title easier to forgive.\n\nSoft spots. The R-E/R-D comparison is partly self-referential. The R-D conductivities are calibrated to match the R-E conduction velocities, so the 3.71% RMSE is model-to-model consistency, not an independent accuracy measure. Useful, but not gold-standard validation. The P-wave calibration is a fit to the target on one patient, with torso and electrodes from a different subject; the reported RMSE minima are fit numbers, not predictions. No code or data is released, which limits independent reproduction.\n\nThe stress-test note is right about a real internal contradiction. Section 3.1 says \"Worst element quality was always below 0.99,\" while Section 4.3 says all 50 models achieved element quality \"exceeding a threshold of 0.99.\" Both cannot be true. Since the R-D suitability claim and the R-D \"gold standard\" simulations rest on that threshold, the authors need to correct the numbers or the wording. I suspect it is a wording slip, but as written it undermines a central claim.\n\nIf I were refereeing, I would ask for that correction, a sharper definition of \"calibrated,\" and a data/code statement. The paper deserves serious peer review; the pipeline is a real step forward for virtual cohort generation, and the methods are worth engaging with. I would bring it to a reading group and would cite it for the UAC and inter-atrial cable techniques.","headline":"A genuinely useful automation pipeline for biatrial modeling, but the ECG-calibration claim outruns the evidence and one mesh-quality statement in the paper contradicts another.","tokens_in":38875,"tokens_out":3041,"would_cite":true,"duration_ms":26240,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":["65M60","92C30","65N50","92C50"],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims an automated workflow can generate ECG-calibrated volumetric atrial models from CT scans in under ten minutes, with fast-model P-waves matching full biophysical simulation to about 3.7% RMSE.","keywords":["atrial electrophysiology","digital twins","volumetric biatrial models","reaction-eikonal model","reaction-diffusion model","electrocardiogram calibration","universal atrial coordinates","inter-atrial conduction"],"falsifier":"Run the calibrated reaction-diffusion model on a cohort of patients and compare its simulated activation times and P-waves against clinically recorded electro-anatomical maps; if the model systematically misses measured local activation times by more than a small threshold, then the 3.71% agreement between the fast and full models does not establish clinical accuracy.","tokens_in":37808,"feed_emoji":"🫀","tokens_out":7496,"duration_ms":68839,"temperature":0.7,"pith_summary":"This paper is trying to establish that high-fidelity, patient-specific computational models of the human atria, calibrated to the surface ECG, can be produced automatically and fast enough to build virtual patient cohorts and digital-twin snapshots at scale. The authors report a single workflow that takes a clinical CT scan through segmentation, volumetric wall construction, fiber and anatomical labeling, universal coordinate computation, torso registration, and inter-atrial pathway definition, producing a simulation-ready model in under ten minutes at reaction-eikonal resolution. They also report that the fast reaction-eikonal forward ECG reproduces the P-waves of a full reaction-diffusion simulation with an average root-mean-square error of 3.71%. If these claims hold, the practical payoff is that hundreds of patient-specific atrial models can be generated and parameter-swept for in silico studies, therapy planning, and regulatory-style testing.","feed_headline":"Atrial digital-twin models from CT in under 10 minutes","feed_subtitle":"Fast simplified ECG reproduces full biophysical P-waves to about 3.7% RMSE.","key_machinery":"The load-bearing object is the end-to-end processing chain built on a volumetric image-stack representation. Instead of extruding a surface manifold, atrial walls are grown by rule-based erosion and dilation directly on the segmented image stack, which avoids topological errors and yields mesh quality above the 0.99 threshold used for bidomain solvers. Multiple Laplace-Dirichlet problems with Dirichlet boundary conditions on automatically detected orifices generate both the anatomical labels (sino-atrial node, crista terminalis, pectinate muscles, Bachmann's bundle, fossa ovalis) and the universal atrial coordinates that make parameter fields addressable. Inter-atrial conduction is parameterized by auto-generated conducting cables anchored at coordinate-defined sites, so insertion points can be swept without remeshing. The forward ECG uses a simplified reaction-eikonal model, which tracks activation times rather than full wavefront dynamics, combined with lead fields; reaction-diffusion conductivities are calibrated so both models propagate at matching velocities, reducing a full P-wave computation from roughly nineteen minutes to about one second.","core_discovery":"The central discovery is that the bottleneck to scalable atrial modelling is not any single component but the integration: a volumetric, image-stack-based wall extrusion that avoids topological errors, a set of Laplace-Dirichlet solutions that turn automatically detected orifices into anatomical labels and universal atrial coordinates, and a cable-based parameterization of inter-atrial conduction that allows insertion sites and velocities to be swept without remeshing. On 50 atrial fibrillation patients, the workflow produced meshes with element quality above 0.99, required no manual correction in 38 of 50 cases at the main processing stage, and generated ready-to-simulate models in an average of about 9 minutes at 0.90 mm resolution. The fast reaction-eikonal model, calibrated to the reaction-diffusion model's conduction velocities, produced activation maps and P-waves closely matching the gold standard, with an average P-wave RMSE of 3.71% across the 12 leads.","pith_inferences":["The 3.71% RMSE is a model-to-model consistency check, not validation against measured human atrial electrophysiology; clinical validity would require comparing against invasive activation maps or ECG data in a cohort.","The cable-based inter-atrial parameterization could be reused to model reentrant circuits such as atrial flutter once a forward model capable of reentry is used, since the framework exposes inter-atrial anatomy as a tunable parameter.","The assumption of uniform atrial wall thickness likely limits patient specificity in regions where wall thickness varies, and a testable extension is to integrate patient-specific thickness maps once imaging resolution permits.","Because the P-wave is a global observation, the workflow does not by itself resolve the identifiability problem: many parameter sets may reproduce the same P-wave, so a dedicated optimization with uniqueness analysis is needed."],"forward_implications":["Each patient CT can yield a simulation-ready biatrial-torso model in under ten minutes at reaction-eikonal resolution, making large virtual cohorts feasible.","The fast reaction-eikonal forward ECG can replace reaction-diffusion simulations for calibration sweeps, since the average P-wave RMSE between the two is 3.71%.","Inter-atrial conduction pathways can be parameterized as automatically generated cables with tunable anchoring sites and conduction velocities, enabling sweeps over Bachmann's bundle insertion geometry without remeshing.","Parameters governing the P-wave, such as the sino-atrial node exit location and right-atrial conduction velocity, can be sampled to produce envelopes that cover clinical P-waves in most leads.","Generated meshes have element quality above the threshold considered critical for bidomain simulations, so the same anatomy supports both fast calibration and high-fidelity mechanistic studies."],"supporting_citations":[{"why":"supplies the convolutional-neural-network segmentation that turns CT volumes into labeled blood-pool domains.","marker":"[107]"},{"why":"supplies the reaction-eikonal forward model whose efficiency is central to near-real-time ECG computation.","marker":"[77]"},{"why":"supplies the curvature-based landmarking and anatomical labeling rules that the volumetric workflow extends.","marker":"[7]"},{"why":"provides the prior volumetric atrial model and universal atrial coordinate approach that this workflow automates and improves.","marker":"[94]"},{"why":"provides the rule-based regional annotation and fiber orientation pipeline adapted here.","marker":"[118]"},{"why":"provides the rule-based fiber architecture method used to assign atrial myofiber orientation.","marker":"[84]"},{"why":"supplies the calibration procedure that matches reaction-diffusion conductivities to the prescribed reaction-eikonal conduction velocities.","marker":"[47]"},{"why":"provides the lead-field ECG forward computation and template-based torso integration reused in this workflow.","marker":"[42]"},{"why":"provides the template torso with electrode positions and the cable-based conduction-system modeling approach extended to inter-atrial connections.","marker":"[41]"}],"fun_headline_variants":["9-minute atrial digital twins with 3.7% P-wave error","Automated atrial twin pipeline: no manual fixes in 76% of cases","End-to-end atrial modeling: 50 patients, 9 minutes each","Atrial EP workflow: fast, accurate, scalable to 50 patients","Automated pipeline builds atrial twins in under 10 minutes"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The claim that the fast reaction-eikonal ECG matches the clinical gold standard rests on the assumption that the reaction-diffusion model with its calibrated conductivities is itself a faithful representation of human atrial electrophysiology; if that gold standard is wrong, the 3.71% error only measures agreement between two models.","fun_headline_variants_meta":{"raw":{"variants":["9-minute atrial digital twins with 3.7% P-wave error","Automated atrial twin pipeline: no manual fixes in 76% of cases","End-to-end atrial modeling: 50 patients, 9 minutes each","Atrial EP workflow: fast, accurate, scalable to 50 patients","Automated pipeline builds atrial twins in under 10 minutes"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00088,"raw_usage":{"total_tokens":3845,"prompt_tokens":1026,"completion_tokens":2819,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":642,"completion_tokens_details":{"reasoning_tokens":2725}},"tokens_in":642,"tokens_out":2819,"duration_ms":19805,"temperature":1.0,"reasoning_tokens":2725,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-09T05:07:08.681720+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the calibrated reaction-diffusion model on a cohort of patients and compare its simulated activation times and P-waves against clinically recorded electro-anatomical maps; if the model systematically misses measured local activation times by more than a small threshold, then the 3.71% agreement between the fast and full models does not establish clinical accuracy.","supporting_citations":[{"cited_title":"Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements","cited_arxiv_id":"2111.13630","evidence_quote":"supplies the convolutional-neural-network segmentation that turns CT volumes into labeled blood-pool domains."},{"cited_title":"AugmentA: Patient-specific augmented atrial model generation tool","cited_arxiv_id":null,"evidence_quote":"supplies the curvature-based landmarking and anatomical labeling rules that the volumetric workflow extends."},{"cited_title":"Constructing bilayer and volumetric atrial models at scale","cited_arxiv_id":null,"evidence_quote":"provides the prior volumetric atrial model and universal atrial coordinate approach that this workflow automates and improves."},{"cited_title":"An automate pipeline for generating fiber orientation and region annotation in patient specific atrial models","cited_arxiv_id":null,"evidence_quote":"provides the rule-based regional annotation and fiber orientation pipeline adapted here."},{"cited_title":"Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations","cited_arxiv_id":null,"evidence_quote":"provides the rule-based fiber architecture method used to assign atrial myofiber orientation."},{"cited_title":"For- CEPSS—A framework for cardiac electrophysiology simulations standardization","cited_arxiv_id":null,"evidence_quote":"supplies the calibration procedure that matches reaction-diffusion conductivities to the prescribed reaction-eikonal conduction velocities."},{"cited_title":"A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs","cited_arxiv_id":null,"evidence_quote":"provides the lead-field ECG forward computation and template-based torso integration reused in this workflow."}],"review_version":1}