ReviewQuantitative imaging in medicine and surgery2020
Artificial intelligence, radiomics and other horizons in body composition assessment.
Review in Quantitative imaging in medicine and surgery, 2020. 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.
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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.
- Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Review
- Radiomics and Back Pain.Global spine journal · 2026Review
- Methodologies for establishing and validating cut-points and comparative standards in medical imaging-based body composition analysis: a scoping review protocol.Systematic reviews · 2026Article
- Preoperative prediction of serosal invasion in gastric cancer using multiphase contrast-enhanced CT radiomics.Frontiers in oncology · 2026Article
- Artificial intelligence-driven body composition analysis and its association with survival in patients with colorectal liver metastases.Contemporary oncology (Poznan, Poland) · 2026Article
- Artificial Intelligence-Assisted Muscular Ultrasonography for Assessing Inflammation and Muscle Mass in Patients at Risk of Malnutrition.Nutrients · 2025Observational
- The Critical Role of Body Composition Assessment in Advancing Research and Clinical Health Risk Assessment across the Lifespan.Journal of obesity & metabolic syndrome · 2025Review
- Sex-Specific Characteristics of Perivascular Fat in Aortic Aneurysms.Journal of clinical medicine · 2025Review
- A comparison between radiomic biological age and chronological age in estimating kidney function.Scientific reports · 2025Article
- Review
- Artificial intelligence-assisted rectus femoris ultrasound vs. L3 computed tomography for sarcopenia assessment in oncology patients: establishing diagnostic cut-offs for muscle mass and quality.Frontiers in nutrition · 2025Article
- Perivascular adipose tissue: a central player in the triad of diabetes, obesity, and cardiovascular health.Cardiovascular diabetology · 2024Review
- 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
- Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors.Journal of personalized medicine · 2023Article
- A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.Quantitative imaging in medicine and surgery · 2023Article
- Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease.European radiology · 2023Article
- PreoperativeQuantitative imaging in medicine and surgery · 2023Article
- The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer.Quantitative imaging in medicine and surgery · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper offers a brief overview of common non-invasive techniques for body composition assessment methods, and of the way images extracted by these methods can be processed with artificial intelligence (AI) and radiomic analysis. These new techniques are becoming more and more appealing in the field of health care, thanks to their ability to treat and process a huge amount of data, suggest new correlations between extracted imaging biomarkers and traits of several diseases as well as lead to the possibility to realise an increasingly personalized medicine. The idea is to suggest the use of AI applications and radiomic analysis to search for features that may be extracted from medical images [computed tomography (CT) and magnetic resonance imaging (MRI)], and that may turn out to be good predictors of metabolic disorder diseases and cancer. This could lead to patient-specific treatments and management of several diseases linked with excessive body fat.
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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.