ReviewMedical physics2019
Deep learning in medical imaging and radiation therapy.
Review in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03980470 (Usefulness of Deep-Learning Image Reconstruction for Cardiac Computed Tomography Angiography - a Prospective, Non-randomized Observational Trial), which is not on this map. Cited by 253 papers, 6 of them syntheses 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.
Usefulness of Deep-Learning Image Reconstruction for Cardiac Computed Tomography Angiography - a Prospective, Non-randomized Observational Trial
Who cites it
253 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis.The British journal of radiology · 2024Pooled it
- Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.Journal of cancer research and clinical oncology · 2023Pooled it
- Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.Radiation oncology (London, England) · 2022Pooled it
- Deep learning for coronary arteries identification and measurement in pediatric echocardiography.Pediatric research · 2026Article
- Quantifying the spatial information loss of Dose-Volume Histograms in Gamma Knife radiosurgery via unsupervised 3D dose geometry embedding.Radiological physics and technology · 2026Article
- LiT-WSAG: high-precision 3D liver tumor segmentation via 2D training and 3D reconstruction.La Radiologia medica · 2026Article
- A Comprehensive Review of Multimodal Medical Image Fusion: Techniques, Evaluation, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- Development of an Integrated Radiotherapy Simulation Platform with AI-Driven Segmentation and Ray-Casting-Based Dosimetric Evaluation.Bioengineering (Basel, Switzerland) · 2026Article
- Deep learning prediction of contrast extravasation versus intracranial hemorrhage after thrombectomy in patients with acute stroke.BMC medical imaging · 2026Article
- Investigation of multimodal deep learning models for predicting ovarian tumor malignancy based on ultrasound images and clinical information - a comprehensive comparative study against readers and O-RADS.BMC medical imaging · 2026Article
- Automated segmentation and quantification of peripapillary hyperreflective ovoid mass-like structures using swept-source optical coherence tomography.BMC ophthalmology · 2026Article
- Machine and deep learning-based forecast of Tehran's accelerating nighttime surface heat island crisis.Scientific reports · 2026Article
- Brain-SAM: a general automatic SAM-based segmentation model for brain science images.Biomedical optics express · 2026Article
- A Deep Network Incorporating Depthwise Separable Convolutions for Pathological Diagnosis of Chest X-Ray Images: A Model Development and Validation Study.Health science reports · 2026Article
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- Generation of deep learning based virtual contrast-enhanced CT images from noncontrast CT images for target volume delineation in cervical cancer radiotherapy.Frontiers in oncology · 2026Article
- AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy.Frontiers in oncology · 2026Review
193 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.
Indexed as
Identifiers
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.