SynthesisFrontiers in radiology2024
A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.
Synthesis in Frontiers in radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled 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.
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Automated quality assurance of rigid brain CT/MR image registration using a 3D convolutional neural network.Journal of applied clinical medical physics · 2026Article
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Improved synthetic CT generation using surface scan information integrated into a deformable registration algorithm for limited Field of View data.Physics and imaging in radiation oncology · 2026Article
- Multimodal Deep Learning Approaches for Lung Disease Detection: A Review.Medicina (Kaunas, Lithuania) · 2026Review
- Implementation of an AI-Driven Workflow for Daily Dose Reconstruction in Prostate Cancer Radiotherapy.Cancers · 2026Article
- Advanced detection of coronary artery disease using a GAN-transformer model on plasma cytokine profiles.Scientific reports · 2026Article
- Self-learning GAN based synthetic CT generation: unlocking CBCT-based adaptive radiotherapy.Frontiers in oncology · 2026Article
- Multiscale Segmentation-Guided Diffusion Model for CBCT-to-CT Synthesis.Life (Basel, Switzerland) · 2025Article
- 3D Latent Diffusion Model for MR-Only Radiotherapy: Accurate and Consistent Synthetic CT Generation.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Evaluation of model uncertainty in AI-based synthetic CT generation from CBCT for abdominal adaptive radiotherapy.Medical physics · 2025Article
- Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.Radiation oncology (London, England) · 2025Article
- The Impact of Diagnostic Imaging on Obstructive Sleep Apnea: Feedback from a Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Convolutional Framelets for Dose Reconstruction in Boron Neutron Capture Therapy with Compton Camera Detector.Cancers · 2025Article
- FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images.Frontiers in artificial intelligence · 2025Article
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
- Uncertainty estimation in female pelvic synthetic computed tomography generated from iterative reconstructed cone-beam computed tomography.Physics and imaging in radiation oncology · 2025Article
- A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer.Physics and imaging in radiation oncology · 2025Review
- Modeling dose uncertainty in cone-beam computed tomography: Predictive approach for deep learning-based synthetic computed tomography generation.Physics and imaging in radiation oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study:
Indexed as
Identifiers
What Socratic 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 Socratic graph.