ArticleJournal of cardiovascular translational research2025
Development and Internal Validation of an AI-Enabled Cuff-less, Non-invasive Continuous Blood Pressure Monitor Across All Classes of Hypertension.
Article in Journal of cardiovascular translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Cuffless hemodynamic monitoring with physics-informed machine learning models.Nature communications · 2026Article
- The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026Review
- Digital health technologies in medicine: evidence, artificial intelligence integration, and ethical challenges.Infectious agents and cancer · 2026Review
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-invasive, continuous blood pressure monitoring technologies require additional validation beyond standard cuff-based methods. This study evaluates a non-invasive, multiparametric wearable cuffless blood pressure (BP) diagnostic monitor across all hypertension classes with diverse subjects.
methodsA prospective, multicenter study assessed Nanowear's SimpleSense-BP performance, including induced and natural BP changes, significant BP variations (Systolic BP (SBP) ≥ ± 15 mm Hg and Diastolic BP (DBP) ≥ ± 10 mm Hg), and reference input value validity over 4 weeks.
results303 subjects (18-83 yrs; 50.16% Female) participated in algorithmic development and validation (Normal - 35%, Prehypertensive - 24%, Stage 1 - 24%, Stage 2 - 17%). 54 subjects were tested for induced change performance, 149 exhibited significant changes, and 91 validated reference value duration.
conclusionsThe study clinically validated a continuous, AI-based BP diagnostic monitor using non-invasive wearable data. Further testing on diverse populations and external validation are recommended. The protocol was inspired by ISO 81060-2 and IEEE 1708:2019 standards.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.