ReviewQuantitative imaging in medicine and surgery2021
Artificial intelligence and abdominal adipose tissue analysis: a literature review.
Review in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis.Journal of cachexia, sarcopenia and muscle · 2023Pooled it
- Adaptive deep learning for quantification and spatial distribution of abdominal adipose tissue from magnetic resonance imaging proton density fat fraction in adults with a body mass index ≥24 kg/mQuantitative imaging in medicine and surgery · 2026Article
- Automated L3 Skeletal Muscle Segmentation for the Evaluation of Sarcopenia: Development and Independent Validation of an Ensemble-Based 2D nnU-Net Pipeline in a Complex Liver Disease Cohort.Muscles (Basel, Switzerland) · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Association Between Metabolic Scores for Visceral Fat and Arterial Stiffness.Vascular health and risk management · 2026Article
- AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.Current atherosclerosis reports · 2025Review
- Stand-alone MRI tool for semiautomatic volumetry of abdominal adipose compartments in patients with obesity.Scientific reports · 2025Article
- Reproducibility of automatic adipose tissue segmentation using proton density fat fraction images between 1.5 and 3.0 T magnetic resonance.Quantitative imaging in medicine and surgery · 2025Article
- Cardiometabolic risk assessment by anthropometric and biochemical indices in mexican population.Frontiers in endocrinology · 2025Article
- Association of liver multi-parameter quantitative metrics determined by dual-layer spectral detector computed tomography (SDCT) with coronary plaque scores.Quantitative imaging in medicine and surgery · 2024Article
- Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.Magma (New York, N.Y.) · 2024Article
- Evaluating Sex-specific Differences in Abdominal Fat Volume and Proton Density Fat Fraction at MRI Using Automated nnU-Net-based Segmentation.Radiology. Artificial intelligence · 2024Article
- Making the invisible visible: imaging techniques for assessing muscle mass and muscle quality in chronic kidney disease.Clinical kidney journal · 2024Review
- Visceral adiposity in patients with lipomatous hypertrophy of the interatrial septum.Heart and vessels · 2024Article
- ChatGPT in radiology structured reporting: analysis of ChatGPT-3.5 Turbo and GPT-4 in reducing word count and recalling findings.Quantitative imaging in medicine and surgery · 2024Article
- Validation of an established TW3 artificial intelligence bone age assessment system: a prospective, multicenter, confirmatory study.Quantitative imaging in medicine and surgery · 2024Article
- Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.Global challenges (Hoboken, NJ) · 2024Review
- Abdominal fat quantification using convolutional networks.European radiology · 2023Article
- Emerging technologies in adipose tissue research.Adipocyte · 2023Review
- Relationship between chronic kidney disease and cerebral white matter hyperintensities: a systematic review.Quantitative imaging in medicine and surgery · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Body composition imaging relies on assessment of tissues composition and distribution. Quantitative data provided by body composition imaging analysis have been linked to pathogenesis, risk, and clinical outcomes of a wide spectrum of diseases, including cardiovascular and oncologic. Manual segmentation of imaging data allows to obtain information on abdominal adipose tissue; however, this procedure can be cumbersome and time-consuming. On the other hand, quantitative imaging analysis based on artificial intelligence (AI) has been proposed as a fast and reliable automatic technique for segmentation of abdominal adipose tissue compartments, possibly improving the current standard of care. AI holds the potential to extract quantitative data from computed tomography (CT) and magnetic resonance (MR) images, which in most of the cases are acquired for other purposes. This information is of great importance for physicians dealing with a wide spectrum of diseases, including cardiovascular and oncologic, for the assessment of risk, pathogenesis, clinical outcomes, response to treatments, and complications. In this review we summarize the available evidence on AI algorithms aimed to the segmentation of visceral and subcutaneous adipose tissue compartments on CT and MR images.
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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.