Evidence map›Paper›PMID 37892850›Full record

ReviewBioengineering (Basel, Switzerland)2023

Application of Deep Learning for Prediction of Alzheimer's Disease in PET/MR Imaging.

Yan Zhao, Qianrui Guo, Yukun Zhang, Jia Zheng, Yang Yang, Xuemei Du, Hongbo Feng, Shuo Zhang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Counseling and disclosure practices in predictive Alzheimer's disease diagnostics: A scoping review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  12. Review
  13. Review
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

8 authors.

Yan ZhaoDepartment of Information Center, The First Affiliated Hospital, Dalian Medical University, Dalian 116011, China.
Qianrui GuoDepartment of Nuclear Medicine, Beijing Cancer Hospital, Beijing 100142, China.ORCID 0009-0005-4460-8231
Yukun ZhangDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian 116011, China.
Jia ZhengDepartment of Nuclear Medicine, The First Affiliated Hospital, Dalian Medical University, Dalian 116011, China.
Yang YangBeijing United Imaging Research Institute of Intelligent Imaging, Beijing 100094, China.ORCID 0000-0001-5668-6812
Xuemei DuDepartment of Nuclear Medicine, The First Affiliated Hospital, Dalian Medical University, Dalian 116011, China.
Hongbo FengDepartment of Nuclear Medicine, The First Affiliated Hospital, Dalian Medical University, Dalian 116011, China.
Shuo ZhangDepartment of Nuclear Medicine, The First Affiliated Hospital, Dalian Medical University, Dalian 116011, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is a promising technique that combines the advantages of PET and MR to provide both functional and structural information of the brain. Deep learning (DL) is a subfield of machine learning (ML) and artificial intelligence (AI) that focuses on developing algorithms and models inspired by the structure and function of the human brain's neural networks. DL has been applied to various aspects of PET/MR imaging in AD, such as image segmentation, image reconstruction, diagnosis and prediction, and visualization of pathological features. In this review, we introduce the basic concepts and types of DL algorithms, such as feed forward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. We then summarize the current applications and challenges of DL in PET/MR imaging in AD, and discuss the future directions and opportunities for automated diagnosis, predictions of models, and personalized medicine. We conclude that DL has great potential to improve the quality and efficiency of PET/MR imaging in AD, and to provide new insights into the pathophysiology and treatment of this devastating disease.

Indexed as

Alzheimer’s diseasedeep learningmagnetic resonancepositron emission tomography

Identifiers

PMID37892850
PMCPMC10604050

What OpenQuestion holds

Textmetadata
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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.