Evidence map›Paper›PMID 37840494›Full record

ArticleJournal of Alzheimer's disease : JAD2023

Fusion of Low-Level Descriptors of Digital Voice Recordings for Dementia Assessment.

Cody Karjadi, Chonghua Xue, Claire Cordella, Swathi Kiran, Ioannis Ch Paschalidis, Rhoda Au, Vijaya B Kolachalama

Abstract readLetter
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Obfuscation via pitch-shifting for balancing privacy and diagnostic utility in voice-based cognitive assessment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Cody KarjadiThe Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Chonghua XueDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Claire CordellaSargent College, Boston University, Boston, MA, USA.
Swathi KiranSargent College, Boston University, Boston, MA, USA.
Ioannis Ch PaschalidisFaculty of Computing & Data Sciences, Boston University, Boston, MA, USA.
Rhoda AuThe Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Vijaya B KolachalamaDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.

Funding

TBDU19AG068753 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Lindsay A. Farrer · 2020 to 2026
$42.4M
Utilizing Technology and AI Approaches to Facilitate Independence andResilience in Older AdultsP30AG073104 · NIA · JOHNS HOPKINS UNIVERSITY · PI Peter M. Abadir, Alexis Battle · 2021 to 2026
$31.2M
Neuropathology CoreP30AG013846 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI KOWALL, NEIL W. · 1996 to 2020
$29.1M
Precision Brain Health Monitoring for Alzheimer's Disease Risk Detection in the Framingham Study: Black & AA Recruitment SupplementRF1AG072654 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, GOLDSTEIN, LEE E. · 2021 to 2023
$2.4M
Mechanisms of drug-coated balloon therapyR01HL159620 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI KOLACHALAMA, VIJAYA B. · 2021 to 2024
$2.1M
Cognitive heterogeneity in those with high Alzheimer's Disease RiskR56AG062109 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA · 2018 to 2018
$517k
PRISTINE: Pre-cancer histology identification of Endobronchial biopsies using deep learningR21CA253498 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BEANE, JENNIFER ELLEN, KOLACHALAMA, VIJAYA B. · 2020 to 2020
$424k
Smartphone image analysis for real time adequacy assessment during kidney biopsyR43DK134273 · NIDDK · NEPHROPATHOLOGY ASSOCIATES · PI KOLACHALAMA, VIJAYA B., SHARMA, SHREE GOPAL · 2022 to 2022
$252k
Detecting and classifying non-fluent speech in aphasia using machine learningF32DC020342 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI CORDELLA, CLAIRE ELIZABETH · 2022 to 2023
$147k
NCI NIH HHS R21 CA253498NHLBI NIH HHS R01 HL159620NIA NIH HHS P30 AG013846NIA NIH HHS P30 AG073104NIA NIH HHS R56 AG062109NIA NIH HHS RF1 AG072654NIA NIH HHS U19 AG068753NIDCD NIH HHS F32 DC020342NIDDK NIH HHS R43 DK134273
6 · The paper itself

Abstract

Digital voice recordings can offer affordable, accessible ways to evaluate behavior and function. We assessed how combining different low-level voice descriptors can evaluate cognitive status. Using voice recordings from neuropsychological exams at the Framingham Heart Study, we developed a machine learning framework fusing spectral, prosodic, and sound quality measures early in the training cycle. The model's area under the receiver operating characteristic curve was 0.832 (±0.034) in differentiating persons with dementia from those who had normal cognition. This offers a data-driven framework for analyzing minimally processed voice recordings for cognitive assessment, highlighting the value of digital technologies in disease detection and intervention.

Indexed as

Alzheimer DiseaseCognitive DysfunctionDementiaVoiceCognitionHumansROC CurveAlzheimer’s diseasedementiadigital healthmachine learningneuropsychological testingvoice recording

Identifiers

PMID37840494
PMCPMC10657667

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.