Evidence map›Paper›PMID 33829706›Full record

Trial reportSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2021

[A Preliminary Study of Applying Geometric Deep Learning in Brain Morphometry for Diagnosis of Alzheimer's Disease].

Wei Xie, Huai-Qiang Sun, Jia-Wei Chen, Yi Zeng, Xu Xu, Zhen-Lin Li, Chun-Chao Xia

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Wei XieDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Huai-Qiang SunHuaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Jia-Wei ChenDepartment of Ultrasound Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
Yi ZengDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Xu XuDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Zhen-Lin LiDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Chun-Chao XiaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveA predictive model of Alzheimer's disease (AD) was established based on brain surface meshes and geometric deep learning, and its performance was evaluated.

methodsSeventy-six clinically diagnosed AD patients and 83 healthy older adults were enrolled and randomly assigned to the training set and the test set according to a 4-to-1 ratio. Brain surface mesh was constructed from 3-D T1-weighted high-resolution structural MR volumes of each participant. After applying a series of simplification to the surface meshes, the training set was fed into the geometric deep neural network for training. The performance of the prediction model was evaluated with the test set, and the evaluation metrics included accuracy, sensitivity and specificity.

resultsThe prediction model trained on the right brain surface meshes with 6 000 faces achieved the best performance, with accuracy reaching 93.8%, sensitivity, 91.7%, and specificity, 94.1%. The evolution of the brain surface meshes during convolution and pooling revealed that AD patients had diffuse brain tissue loss compared with healthy older adults.

conclusionMorphological brain analysis based on mesh data and geometric deep learning has great potential in the differential diagnosis of AD.

Indexed as

Alzheimer DiseaseDeep LearningAgedBrainHumansMagnetic Resonance ImagingNeural Networks, ComputerAlzheimer's diseaseBrain morphometryGeometric deep learningMagnetic resonance brain imaging

Identifiers

PMID33829706
PMCPMC10408926

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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.