ArticleJournal of the American College of Radiology : JACR2018
Deep Learning in Radiology: Does One Size Fit All?
Article in Journal of the American College of Radiology : JACR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 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
45 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.BMC oral health · 2024Pooled it
- Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis.Clinical oral investigations · 2024Pooled it
- Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2021Pooled it
- Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.European radiology · 2021Pooled it
- Automated Femoral, Acetabular, and Global Offset Measurements on Pelvis Radiograph Using Deep Learning.Hip & pelvis · 2026Article
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- Deep Learning-Based Prediction of Hepatic Decompensation in Patients With Primary Sclerosing Cholangitis With Computed Tomography.Mayo Clinic proceedings. Digital health · 2024Article
- Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography.Korean journal of radiology · 2024Article
- Artificial Intelligence in pathology: current applications, limitations, and future directions.Irish journal of medical science · 2024Review
- The unintended consequences of artificial intelligence in paediatric radiology.Pediatric radiology · 2024Article
- Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection.Cureus · 2024Review
- The Relationship between Liver Volume, Clinicopathological Characteristics and Survival in Patients Undergoing Resection with Curative Intent for Non-Metastatic Colonic Cancer.Tomography (Ann Arbor, Mich.) · 2024Article
- Generative Artificial Intelligence Terminology: A Primer for Clinicians and Medical Researchers.Cureus · 2023Article
- Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning approach for differentiating indeterminate adrenal masses using CT imaging.Abdominal radiology (New York) · 2023Article
- A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG.Diagnostics (Basel, Switzerland) · 2023Article
- Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis-A Cross-Sectional Study.Journal of personalized medicine · 2023Article
- Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review.Frontiers in oncology · 2023Review
- Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential.SICOT-J · 2023Article
- Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends.Journal of clinical medicine · 2022Review
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
Abstract
Deep learning (DL) is a popular method that is used to perform many important tasks in radiology and medical imaging. Some forms of DL are able to accurately segment organs (essentially, trace the boundaries, enabling volume measurements or calculation of other properties). Other DL networks are able to predict important properties from regions of an image-for instance, whether something is malignant, molecular markers for tissue in a region, even prognostic markers. DL is easier to train than traditional machine learning methods, but requires more data and much more care in analyzing results. It will automatically find the features of importance, but understanding what those features are can be a challenge. This article describes the basic concepts of DL systems and some of the traps that exist in building DL systems and how to identify those traps.
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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.