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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Cody Karjadi1,2, Huitong Ding1,2, Mahdi Khemakhem1,2

  • 1Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

Digital voice analysis offers a scalable method for early Alzheimer's disease (AD) and related dementias (ADRD) detection. A new open-source toolkit enables accessible voice data processing for scientific advancement.

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Biomedical Informatics

Background:

  • Digital voice analysis is emerging as a less biased and more scalable method for early detection of cognitive impairment in Alzheimer's disease (AD) and related dementias (ADRD).
  • The development of open-source tools like FreeSurfer for brain MRI processing has accelerated AD/ADRD research.
  • This work introduces a toolkit to similarly advance the use of digital voice for AD/ADRD scientific discovery.

Purpose of the Study:

  • To develop and release an open-access toolkit for digital voice processing to aid in the scientific study of cognitive impairment.
  • To provide researchers with accessible, non-proprietary tools for analyzing voice data relevant to AD/ADRD.

Main Methods:

  • The Global Research and Imaging Platform (GRIP) is developing an open-access platform, with the first version of the toolkit published on GitHub.
  • The toolkit integrates various open-source packages for voice processing tasks, tested on over 33,000 Framingham Heart Study (FHS) recordings and other public datasets.
  • Detailed documentation and Docker containerization are provided for ease of use and deployment.

Main Results:

  • The toolkit includes pipelines for language identification, speaker diarization (achieving 9.0% DER with pyannote.audio), and automatic speech recognition (ASR) on datasets like MinDS-14 and DisfluencySpeech.
  • Evaluation pipelines for ASR provide metrics such as word error rate and character error rate.
  • Acoustic features (openSMILE, Data2vec, Wav2Vec2) and NLP features (pauses, lexical diversity) have been extracted from FHS recordings.

Conclusions:

  • Digital voice analysis presents an ideal, scalable method for collecting cognitively relevant data in real-world settings.
  • The ongoing development of this modular toolkit facilitates efficient, non-proprietary digital voice processing.
  • The GRIP platform aims to empower global users, irrespective of technical expertise, to utilize robust voice processing pipelines.