ReviewEJNMMI physics2021
Artificial intelligence with deep learning in nuclear medicine and radiology.
Review in EJNMMI physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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.
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.
Who cites it
25 citing papers in PubMed.
- Review
- A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.EJNMMI physics · 2026Article
- Psychometric properties and detectability of GPT-4o-generated multiple-choice questions compared with human-authored items across imaging specialties.NPJ digital medicine · 2026Article
- Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.Journal of medical Internet research · 2025Article
- Global output of clinical application research on artificial intelligence in the past decade: a scientometric study and science mapping.Systematic reviews · 2025Article
- Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.Radiological physics and technology · 2025Article
- Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms.Scientific reports · 2025Article
- Sustainable Nanotechnology and Artificial Intelligence to Empower Image-Guided Therapy for Precision Healthcare.BME frontiers · 2025Review
- Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT.EJNMMI physics · 2024Article
- Comparison of the Accuracy of a Deep Learning Method for Lesion Detection in PET/CT and PET/MRI Images.Molecular imaging and biology · 2024Article
- Diagnostic performance of a deep-learning model usingAnnals of nuclear medicine · 2024Article
- Verification of image quality improvement of low-count bone scintigraphy using deep learning.Radiological physics and technology · 2024Article
- Deep learning-based attenuation correction method inRadiological physics and technology · 2024Article
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability.Molecular imaging and biology · 2024Article
- A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification.Scientific reports · 2024Article
- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
- Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.Schizophrenia bulletin · 2023Article
- Geometry-invariant abnormality detection.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
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What OpenQuestion holds
Registered trials
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.