ReviewAJR. American journal of roentgenology2016
Progress in Fully Automated Abdominal CT Interpretation.
Review in AJR. American journal of roentgenology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- The Association of FIT-Based Colorectal Cancer Screening with Earlier Disease Detection: The Romanian Experience.Cancers · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- Fully automated assessment of the future liver remnant in a blood-free setting via CT before major hepatectomy via deep learning.Insights into imaging · 2024Article
- Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022.European radiology · 2024Article
- Value-added Opportunistic CT Screening: State of the Art.Radiology · 2022Review
- Quantitative assessment of renal obstruction in multi-phase CTU using automatic 3D segmentation of the renal parenchyma and renal pelvis: A proof of concept.European journal of radiology open · 2022Article
- Article
- Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning.Diagnostics (Basel, Switzerland) · 2021Article
- [Application of deep learning assisted electronic laryngoscope in diagnosis of laryngeal leukoplakia].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2021Article
- Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.Pancreas · 2021Review
- Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study.Journal of multidisciplinary healthcare · 2021Article
- Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2020Article
- Evaluation of radiologist's knowledge about the Artificial Intelligence in diagnostic radiology: a survey-based study.Acta radiologica open · 2020Article
- Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department.Scientific reports · 2020Article
- Advanced imaging techniques for chronic pancreatitis.Abdominal radiology (New York) · 2020Review
- Deep learning workflow in radiology: a primer.Insights into imaging · 2020Review
- The Role of Hospital Transfer in Reexamination Computed Tomography Scans: A Nationwide Cohort Study of Gastric Cancer Patients Undergoing Surgery.Healthcare (Basel, Switzerland) · 2019Article
- Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.Academic radiology · 2019Article
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.
Funding
Abstract
objectiveAutomated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically.
conclusionThis article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.
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.