ReviewDiagnostics (Basel, Switzerland)2023
A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 87 citations in OpenAlex.
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
- Article
- A comprehensive bedside chest radiography dataset with structured, itemized and graded radiologic reports.Scientific data · 2026Article
- K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction.Scientific reports · 2026Article
- Decision-Aware Vision Mamba with Context-Guided Slot Mixing for Chest X-Ray Screening and Culture-Based Hierarchical Tuberculosis Classification.Sensors (Basel, Switzerland) · 2026Article
- Hybrid ConvNeXtV2-ViT Architecture with Ontology-Driven Explainability and Out-of-Distribution Awareness for Transparent Chest X-Ray Diagnosis.Diagnostics (Basel, Switzerland) · 2026Article
- Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data.Research square · 2025Article
- Quantitative assessment of impact of technical and population-based factors on fairness of AI models for chest X-ray scans.Computers in biology and medicine · 2025Article
- Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images.Journal of clinical medicine · 2025Article
- Comprehensive Evaluation of Facet Joints Osteoarthritis Radiological Features on Lumbar CT: A Multitask Deep Learning Approach.JOR spine · 2025Article
- Deep learning approach for screening neonatal cerebral lesions on ultrasound in China.Nature communications · 2025Article
- Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.Tomography (Ann Arbor, Mich.) · 2025Review
- Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review.EClinicalMedicine · 2025Review
- Computer-based quantitative image texture analysis using multi-collinearity diagnosis in chest X-ray images.PloS one · 2025Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Ensemble fusion model for improved lung abnormality classification: Leveraging pre-trained models.MethodsX · 2024Article
- Review
- Explainable AI to identify radiographic features of pulmonary edema.Radiology advances · 2024Article
- Ultrasound Image Analysis with Vision Transformers-Review.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models' detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases.
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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.