Pith Number

pith:KUVCXGSQ

pith:2026:KUVCXGSQSVAO7MFCSLUOA5J2Z2

not attested not anchored not stored refs resolved

Robust and Explainable Bicuspid Aortic Valve Diagnosis Using Stacked Ensembles on Echocardiography

Christos Chrysanthos Nikolaidis, Nikolas Moustakidis, Pavlos S. Efraimidis, Theofilos Moustakidis, Vasileios Sachpekidis

A stacked ensemble of video models distinguishes bicuspid from tricuspid aortic valves on routine echocardiography cine loops with an F1-score of 0.907.

arxiv:2605.13730 v1 · 2026-05-13 · cs.LG · cs.AI · cs.CV

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\usepackage{pith}
\pithnumber{KUVCXGSQSVAO7MFCSLUOA5J2Z2}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Across fixed outer splits and 10 random seeds, the calibrated stacked ensemble achieved an outer-CV F1-score of 0.907 and recall of 0.877 on N=90 patient studies (48 BAV, 42 TAV).

C2weakest assumption

The assumption that the leakage-aware stratified outer cross-validation on this small internal cohort fully ensures robustness and generalizability to new patients and imaging conditions without external validation.

C3one line summary

Stacked video ensemble model distinguishes BAV from TAV on PLAX cine loops with outer-CV F1 of 0.907 using Grad-CAM and SHAP for explainability.

References

26 extracted · 26 resolved · 0 Pith anchors

[1] Braverman and Andrew Cheng 2021

[2] M. Hillebrand, W. Li, S. Gross, et al. Accuracy of transthoracic echocardiography to diagnose bicuspid aortic valve: comparison with surgical findings and cardiac magnetic resonance imaging.Internatio 2017

[3] R.F. Ayad, J.J. Puthumana, and R. Doukky. Echocardio- graphic accuracy in diagnosing bicuspid aortic valve and its clinical implications.Echocardiography, 28(5): 558–562, 2011 2011

[4] J. Chen, S. Li, and Y . Xiao. Automatic classification of BA V using CNNs on echocardiography.IEEE Journal of Biomedical and Health Informatics, 24(11):3120– 3130, 2020 2020

[5] D. Ouyang, T. He, C. Cao, et al. Video-based AI for beat-to-beat assessment of cardiac function.Nature, 580(7802):252–256, 2020 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:16.565898Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

552a2b9a509540efb0a292e8e0753ace939d74a0f49f05eb963279f001c2133d

Aliases

arxiv: 2605.13730 · arxiv_version: 2605.13730v1 · doi: 10.48550/arxiv.2605.13730 · pith_short_12: KUVCXGSQSVAO · pith_short_16: KUVCXGSQSVAO7MFC · pith_short_8: KUVCXGSQ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KUVCXGSQSVAO7MFCSLUOA5J2Z2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 552a2b9a509540efb0a292e8e0753ace939d74a0f49f05eb963279f001c2133d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "eacc64200159e05eea575f8d678765462bc92d29829a51ffeae3241fcc25e38d",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T16:10:39Z",
    "title_canon_sha256": "aa8c90194b2153afa2f5904667872908c0672019ce6f7e6cd99ed8ee4cc11f41"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13730",
    "kind": "arxiv",
    "version": 1
  }
}