ReviewRadiological physics and technology2017
Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.
Review in Radiological physics and technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 182 citations in OpenAlex.
- Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research.Computational and mathematical methods in medicine · 2021Pooled it
- Measuring and managing pulmonary edema in ARDS: a narrative review.Critical care (London, England) · 2026Review
- Machine learning in critical care: Improving prediction of mortality and intensive care unit stay after cardiac surgery.JTCVS open · 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
- Emphysema progression risk in COPD using a localized foundational model of density evolution.NPJ digital medicine · 2025Article
- Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation.PLOS digital health · 2025Article
- Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.Thoracic cancer · 2024Article
- Lusca: FIJI (ImageJ) based tool for automated morphological analysis of cellular and subcellular structures.Scientific reports · 2024Article
- Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.Diagnostics (Basel, Switzerland) · 2024Article
- Advanced image generation for cancer using diffusion models.Biology methods & protocols · 2024Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- Quantification of pulmonary involvement in COVID-19 pneumonia: an upgrade of theEuropean physical journal plus · 2023Article
- ADS-Net: attention-awareness and deep supervision based network for automatic detection of retinopathy of prematurity.Biomedical optics express · 2022Article
- Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs.World journal of orthopedics · 2022Article
- Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.BMC medical imaging · 2022Article
- Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias.Diagnostics (Basel, Switzerland) · 2022Article
- Computational Approaches for Acute Traumatic Brain Injury Image Recognition.Frontiers in neurology · 2022Review
- Computer-assisted image-based risk analysis and planning in lung surgery - a review.Frontiers in surgery · 2022Review
- Isophotes, Scale Space, and Invariants in Lung CT for COPD Diagnosis.Radiology. Artificial intelligence · 2022Article
- Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.
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.