Evidence map›Paper›PMID 29270150›Full record

ArticleFrontiers in neurology2017

A Novel Sonification Approach to Support the Diagnosis of Alzheimer's Dementia.

Letizia Gionfrida, Agnieszka Roginska

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.4field-weighted citation impact, top 17% of its field
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

7 citing papers in PubMed, 13 citations in OpenAlex.

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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

2 authors at 2 institutions in 2 countries.

Letizia GionfridaCentre of Performance Science, Royal College of Music, London, United Kingdom.
Agnieszka RoginskaMusic and Audio Research Laboratory, Music Technology, New York University, New York, NY, United States.
New York University · USRoyal College of Music · GB

Funding

Project-005UL1TR001445 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BREDELLA, MIRIAM ANTOINETTE, HOCHMAN, JUDITH S · 2015 to 2025
$103.5M
Institutional Clinical and Translational Science AwardUL1TR000038 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI CRONSTEIN, BRUCE NEIL · 2012 to 2014
$16.8M
NCATS NIH HHS UL1 TR000038NCATS NIH HHS UL1 TR001445
6 · The paper itself

Abstract

Alzheimer's disease is the most common neurodegenerative form of dementia that steadily worsens and eventually leads to death. Its set of symptoms include loss of cognitive function and memory decline. Structural and functional imaging methods such as CT, MRI, and PET scans play an essential role in the diagnosis process, being able to identify specific areas of cerebral damages. While the accuracy of these imaging techniques increases over time, the severity assessment of dementia remains challenging and susceptible to cognitive and perceptual errors due to intra-reader variability among physicians. Doctors have not agreed upon standardized measurement of cell loss used to specifically diagnose dementia among individuals. These limitations have led researchers to look for supportive diagnosis tools to enhance the spectrum of diseases characteristics and peculiarities. Here is presented a supportive auditory tool to aid in diagnosing patients with different levels of Alzheimer's. This tool introduces an audible parameter mapped upon three different brain's lobes. The motivating force behind this supportive auditory technique arise from the fact that AD is distinguished by a decrease of the metabolic activity (hypometabolism) in the parietal and temporal lobes of the brain. The diagnosis is then performed by comparing metabolic activity of the affected lobes to the metabolic activity of other lobes that are not generally affected by AD (i.e., sensorimotor cortex). Results from the diagnosis process compared with the ground truth show that physicians were able to categorize different levels of AD using the sonification generated in this study with higher accuracy than using a standard diagnosis procedure, based on the visualization alone.

Indexed as

Alzheimer’s dementiacomputer-aided diagnosismedical imagingPET scansonification

Identifiers

PMID29270150
PMCPMC5725407
OpenAlexW2773024834

What OpenQuestion holds

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LicenceCC BY
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.