ReviewSensors (Basel, Switzerland)2020
3D Deep Learning on Medical Images: A Review.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 175 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
175 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Multidimensional Dual Encoding Network For Liver Lesion Classification From Multi-Phase Magnetic Resonance Imaging.Journal of imaging informatics in medicine · 2026Article
- Automatic Segmentation and Classification of Glioblastoma and Solitary Brain Metastasis Using a Deep Learning Model on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Reliability-Aware View-Adaptive Consensus for 3D Cephalometric Landmark Identification.Journal of imaging informatics in medicine · 2026Article
- A CT-based deep learning model for the automated risk stratification of refractory Mycoplasma pneumoniae pneumonia in children.BMC medical imaging · 2026Article
- Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.Brain informatics · 2026Article
- Micro-CT-based analysis of the murine osteochondral unit: a review and recommendations for standardized subchondral bone assessment.JBMR plus · 2026Review
- Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future.Neurocomputing · 2026Article
- Local and global patterns support medical imaging as a biomarker of ageing.Communications medicine · 2026Article
- Segmentation-Free Preoperative 3D MRI Classification of Low-Grade Versus High-Grade Glioma Using Task-Oriented Neural Architecture Search.Journal of imaging · 2026Article
- Interpretable Deep Learning Radiomics Model for Preoperative Prediction of High-Grade Soft Tissue Sarcomas: A Multicenter MRI Study.Journal of imaging informatics in medicine · 2026Article
- Applications and prospects of artificial intelligence and digital medicine in pediatric nasal skull base tumors.Pediatric investigation · 2026Review
- Dual-Phase Computed Tomography-Based Deep Learning Architecture for Three-Year Survival Prediction in Hepatocellular Carcinoma.Journal of imaging informatics in medicine · 2026Article
- Dual-stage 3D medical image segmentation integrating learnable prompt generation and memory attention.Scientific reports · 2026Article
- Few-Shot Learning for Prostate Cancer Detection on MRI: Comparative Analysis with Radiologists' Performance.Journal of imaging informatics in medicine · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Smartphone 3D Scanning Technology and 3D Semi-Synthetic Data for Processing Infant Head Deformities Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2026Article
- Multimodal deep learning using preoperative CT and ultrasound for recurrence risk prediction in high-grade serous ovarian carcinoma.BMC medical imaging · 2026Article
115 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
6 authors at 1 institution in 2 countries.
Funding
Abstract
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.
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.