Pith Number

pith:ZFMCS7UB

pith:2026:ZFMCS7UBZ5XDEFOWVKQZVINO6Q

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Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms

Alex Bateh, Anni Zhao, Sandra Billinger, Tyler Baldridge, Xiao Hu

Features from transcranial Doppler measurements of cerebral blood flow velocity enable machine learning models to predict brain vascular age and identify accelerated aging in diseased subjects.

arxiv:2605.16969 v1 · 2026-05-16 · cs.AI

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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

The differences in healthy and diseased subjects' performances suggest that features generated using TCD may be relevant when evaluating accelerated cerebrovascular aging.

C2weakest assumption

That regression models trained exclusively on healthy subjects produce an unbiased baseline for normal cerebrovascular aging, even though the models over-predicted healthy subjects' age by 3.69 years on average.

C3one line summary

Machine learning models trained on TCD-derived features from healthy subjects predict brain vascular age and indicate accelerated cerebrovascular aging in subjects with stroke, Alzheimer's, and other conditions.

References

38 extracted · 38 resolved · 0 Pith anchors

[1] Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.Scientific reports, 12(1):22625, 2022 2022

[2] Photoplethysmogram based vascular aging assessment using the deep convolutional neural network.Scientific Reports, 12(1):11377, 2022 2022

[3] Prediction of brain age based on the community structure of functional networks.Biomedical Signal Processing and Control, 79:104151, 2023 2023

[4] Deep learning to predict future cognitive decline: a multimodal approach using brain mri and clinical data.Frontiers in Neuroimaging, 5:1726037, 2026 2026

[5] A deep ensemble hip- pocampal cnn model for brain age estimation applied to alzheimer’s diagnosis.Expert Systems with Applications, 195:116622, 2022 2022

Receipt and verification

First computed	2026-05-20T00:03:33.615823Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c958297e81cf6e3215d6aaa19aa1aef413753caf533ca61fd9818dcadd291e55

Aliases

arxiv: 2605.16969 · arxiv_version: 2605.16969v1 · doi: 10.48550/arxiv.2605.16969 · pith_short_12: ZFMCS7UBZ5XD · pith_short_16: ZFMCS7UBZ5XDEFOW · pith_short_8: ZFMCS7UB

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZFMCS7UBZ5XDEFOWVKQZVINO6Q \
  | 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: c958297e81cf6e3215d6aaa19aa1aef413753caf533ca61fd9818dcadd291e55

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "107959edea1ed192aa051f4015ec521914b8931451504289ebe4d31de785c748",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-16T12:43:32Z",
    "title_canon_sha256": "32d77cbb5345c61e2bebe5f493472f4ef772d98dbf48b37dd1c5589dfba00971"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16969",
    "kind": "arxiv",
    "version": 1
  }
}