ReviewJournal of medical systems2018
Medical Image Analysis using Convolutional Neural Networks: A Review.
Review in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 402 papers, 7 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
402 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Image-Based Deep Learning for Cataract Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled it
- Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.Journal of imaging informatics in medicine · 2025Pooled it
- Convolutional neural network models of structural MRI for discriminating categories of cognitive impairment: a systematic review and meta-analysis.BMC neurology · 2025Pooled it
- Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.Neurosciences (Riyadh, Saudi Arabia) · 2024Pooled it
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.BMC oral health · 2023Pooled it
- Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis.Cancer medicine · 2023Trial
- Accelerating Focused Ultrasound Modeling in Heterogeneous Spinal Cord Anatomy With Vision Transformer Operator Networks.Ultrasound in medicine & biology · 2026Article
- Segmenting scaffolds with ingrown bone in micro-CT images: considerations of evaluation metrics and loss functions.Medical & biological engineering & computing · 2026Article
- Artificial intelligence for evaluation of magnetic resonance imaging-detected extramural vascular invasion in rectal cancer.PLOS digital health · 2026Article
- The Role of Artificial Intelligence in Optimizing Diagnosis in Prostate Cancer-A Narrative Review.Journal of clinical medicine · 2026Review
- Deep learning-based classification of interphalangeal finger joints in erosive hand osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- A similarity-aware network with contrastive optimization for biomedical image segmentation.BMC medical imaging · 2026Article
- Automated Prognostic Evaluation of First Permanent Molar Extractions Using YOLOv8 with Oriented Bounding Boxes on Pediatric Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- Article
- AI-Driven Image Analysis for Nanofiber Characterization: From Diameter Measurement to Multiparameter Assessment.ACS omega · 2026Review
- Impact of Imaging Protocols on Thermal Detection of Pressure Injuries: Threshold versus Deep Learning Across Skin Tones.medRxiv : the preprint server for health sciences · 2026Article
342 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.
Indexed as
Identifiers
30298337What 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.