ArticleRadiology2022
Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning.
Article in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Artificial intelligence-based models for quantification of intra-pancreatic fat deposition and their clinical relevance: a systematic review of imaging studies.European radiology · 2026Pooled it
- Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.Diabetes, obesity & metabolism · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Landmark-Based Pancreas Sub-region Segmentation in CT.Journal of imaging informatics in medicine · 2026Article
- The adoption paradox for veterinary professionals in China: high use of artificial intelligence despite low familiarity.Frontiers in veterinary science · 2026Article
- CT-based radiomics nomogram of vertebral bodies and paravertebral muscles for differential diagnosis of type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Opportunistic AI-derived adiposity measures from coronary artery calcium scans predict new-onset type 2 diabetes in adults without obesity or hyperglycemia: insights from the AI-CVD study within MESA.Diabetology & metabolic syndrome · 2025Article
- Data-driven abdominal phenotypes of type 2 diabetes in lean, overweight, and obese cohorts from computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Establishment of normative spleen volume in a Japanese cohort using automated CT segmentation: diagnostic implications for liver fibrosis.BMC gastroenterology · 2025Article
- Increased depth of pancreas incisure on computed tomography as an independent predictor for T2DM.Scientific reports · 2025Article
- Assessing the Reliability of Pancreatic CT Imaging Biomarkers for Diabetes Prediction: A Dual Center Retrospective Study.Academic radiology · 2025Article
- Pancreatic changes in patients with visceral fat obesity: an evaluation with contrast-enhanced dual-energy computed tomography with automated three-dimensional volumetry.La Radiologia medica · 2025Article
- Can AI Be Useful in the Early Detection of Pancreatic Cancer in Patients with New-Onset Diabetes?Biomedicines · 2025Review
- A Comparison of CT-Based Pancreatic Segmentation Deep Learning Models.Academic radiology · 2024Article
- Sex-specific association between carbohydrate antigen 19-9 and incident type 2 diabetes.Scientific reports · 2024Article
- Correlation of HbA1c levels with CT-based body composition biomarkers in diabetes mellitus and metabolic syndrome.Scientific reports · 2024Article
- Fasting pancreatic polypeptide predicts incident microvascular and macrovascular complications of type 2 diabetes: An observational study.Diabetes/metabolism research and reviews · 2024Observational
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
- Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study.Insights into imaging · 2024Article
- Longitudinal follow-up of incidental renal calculi on computed tomography.Abdominal radiology (New York) · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Background CT biomarkers both inside and outside the pancreas can potentially be used to diagnose type 2 diabetes mellitus. Previous studies on this topic have shown significant results but were limited by manual methods and small study samples. Purpose To investigate abdominal CT biomarkers for type 2 diabetes mellitus in a large clinical data set using fully automated deep learning. Materials and Methods For external validation, noncontrast abdominal CT images were retrospectively collected from consecutive patients who underwent routine colorectal cancer screening with CT colonography from 2004 to 2016. The pancreas was segmented using a deep learning method that outputs measurements of interest, including CT attenuation, volume, fat content, and pancreas fractal dimension. Additional biomarkers assessed included visceral fat, atherosclerotic plaque, liver and muscle CT attenuation, and muscle volume. Univariable and multivariable analyses were performed, separating patients into groups based on time between type 2 diabetes diagnosis and CT date and including clinical factors such as sex, age, body mass index (BMI), BMI greater than 30 kg/m
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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 OpenQuestion graph.