Evidence map›Paper›PMID 33168888›Full record

ArticleScientific reports2020

Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning.

Daisuke Nagasato, Hitoshi Tabuchi, Hiroki Masumoto, Takanori Kusuyama, Yu Kawai, Naofumi Ishitobi, Hiroki Furukawa, Shouto Adachi, Fumiko Murao, Yoshinori Mitamura

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images.Translational vision science & technology · 2024
    Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Deep Learning Model for Predicting Airway Organoid Differentiation.Tissue engineering and regenerative medicine · 2023
    Article
  10. Article
  11. Developing an iOS application that uses machine learning for the automated diagnosis of blepharoptosis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022
    Observational
  12. 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

10 authors.

Daisuke NagasatoDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan. d.nagasato@tsukazaki-eye.net.
Hitoshi TabuchiDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Hiroki MasumotoDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Takanori KusuyamaDepartment of Cardiology, Tsukazaki Hospital, Himeji, Japan.
Yu KawaiDepartment of Cardiology, Tsukazaki Hospital, Himeji, Japan.
Naofumi IshitobiDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Hiroki FurukawaDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Shouto AdachiDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Fumiko MuraoDepartment of Ophthalmology, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan.
Yoshinori MitamuraDepartment of Ophthalmology, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examined whether age and brachial-ankle pulse-wave velocity (baPWV) can be predicted with ultra-wide-field pseudo-color (UWPC) images using deep learning (DL). We examined 170 UWPC images of both eyes of 85 participants (40 men and 45 women, mean age: 57.5 ± 20.9 years). Three types of images were included (total, central, and peripheral) and analyzed by k-fold cross-validation (k = 5) using Visual Geometry Group-16. After bias was eliminated using the generalized linear mixed model, the standard regression coefficients (SRCs) between actual age and baPWV and predicted age and baPWV from the UWPC images by the neural network were calculated, and the prediction accuracies of the DL model for age and baPWV were examined. The SRC between actual age and predicted age by the neural network was 0.833 for all images, 0.818 for central images, and 0.649 for peripheral images (all P < 0.001) and between the actual baPWV and the predicted baPWV was 0.390 for total images, 0.419 for central images, and 0.312 for peripheral images (all P < 0.001). These results show the potential prediction capability of DL for age and vascular aging and could be useful for disease prevention and early treatment.

Indexed as

Ankle Brachial IndexColor PerceptionDeep LearningPulse Wave AnalysisAdultAgedFemaleHumansHypertensionMaleMiddle Aged

Identifiers

PMID33168888
PMCPMC7652944

What OpenQuestion holds

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LicenceCC BY
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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.