Close Menu
Healthtost
  • News
  • Mental Health
  • Men’s Health
  • Women’s Health
  • Skin Care
  • Sexual Health
  • Pregnancy
  • Nutrition
  • Fitness
What's Hot

15 easy ways to get 20 grams of protein (Personal Trainer Guide)

June 29, 2025

Organ chip technology accurately predicts chemotherapy response to patients with esophageal adenocarcinoma

June 29, 2025

How Barefoot Workout can make you stronger, more athletic and stunning in injuries

June 29, 2025
Facebook X (Twitter) Instagram
  • About Us
  • Contact Us
  • Privacy Policy
  • Terms and Conditions
  • Disclaimer
Facebook X (Twitter) Instagram
Healthtost
SUBSCRIBE
  • News

    Organ chip technology accurately predicts chemotherapy response to patients with esophageal adenocarcinoma

    June 29, 2025

    Expansion of genetic code to mammalian cells using pseuduridine -modified codons

    June 29, 2025

    Discover a Dimmer Genetic switch that controls fetal growth

    June 28, 2025

    Who Scientific Advisory Group for the origin of new pathogenic reports for Sars-Cov-2 Origins

    June 28, 2025

    Exploring nervous reactions to mental exhaustion in healthy adults

    June 27, 2025
  • Mental Health

    Which one is right for you? – Talkspace

    June 27, 2025

    Do alternative treatments for bipolar disorder work? Guide based on evidence (2025)

    June 26, 2025

    Data reveals both challenges and positive trends

    June 16, 2025

    How to choose the best yoga teacher training in Rishikesh

    June 14, 2025

    Stress is the most common mental health problem – here is how technology could help manage

    June 11, 2025
  • Men’s Health

    How Barefoot Workout can make you stronger, more athletic and stunning in injuries

    June 29, 2025

    How I turned the chatgpt to my personal nutrition coach and you can also

    June 29, 2025

    Total human care is here: Help men look and feel great now and forever

    June 28, 2025

    Why men ignore sleep apnea (and what they really cost them) – talking about men’s health

    June 28, 2025

    Lessons from a survivor for prostate cancer

    June 26, 2025
  • Women’s Health

    Books I have recently read – The Fitnessista

    June 29, 2025

    Does it support your aesthetic travel your body and mind? Guide

    June 28, 2025

    Eating for real immune support this winter

    June 27, 2025

    What does public health really mean

    June 27, 2025

    How long do you have to expand after MTF? A complete driver to expand – Vuvatech

    June 25, 2025
  • Skin Care

    Sunburn First Aid -7 common mistakes you will regret later

    June 29, 2025

    What is happening first? The step by step guide to build a routine of skin care

    June 28, 2025

    DIY Vitamin C Cucumber The Eye Serum

    June 27, 2025

    Tips for Summer skin care for your best skin

    June 26, 2025

    How a crisis of ingredients led to the best physical form of our deodorant stick

    June 24, 2025
  • Sexual Health

    Can Koles really get chlamydia?

    June 28, 2025

    Overward Visitor and Student Health Insurance in Australia for visa holders

    June 27, 2025

    Disassociation of the latest testosterone treatment lines

    June 27, 2025

    We always know that orgasms were good for you. Now there is proof.

    June 26, 2025

    Josh Duhamel gets testosterone replacement treatment at 52

    June 25, 2025
  • Pregnancy

    AI helps the couple capture after 19 years and 15 IVF attempts

    June 29, 2025

    7 signs your gut can be out of balance

    June 29, 2025

    Helping parents prepare for birth with calm and trust

    June 28, 2025

    Better screen limits for kids: Expert driver for parents

    June 28, 2025

    What is prenatal ability?

    June 27, 2025
  • Nutrition

    25 best vegan taco recipes that are healthy, easy and full of flavor

    June 29, 2025

    Episode 004: Trust your truth against all logic with Angela de la Agua

    June 28, 2025

    Benefits for the health of CoQ10 you should be aware

    June 27, 2025

    Creatine Completion in Menopause: What does science say?

    June 27, 2025

    GLP-1 Enhance the Smoothie recipes push for weight loss

    June 26, 2025
  • Fitness

    15 easy ways to get 20 grams of protein (Personal Trainer Guide)

    June 29, 2025

    Review of the Heat Index: an approach based on evidence

    June 28, 2025

    Bodybuilding Legend Charles Glass’ 5 Favorite Movements Hamstring

    June 27, 2025

    7 Best energy gels 2025, per runners and dieticians

    June 26, 2025

    Different types of training and fitness courses

    June 25, 2025
Healthtost
Home»News»AI predicts mortality with whole-body MRI for personalized health insights
News

AI predicts mortality with whole-body MRI for personalized health insights

healthtostBy healthtostDecember 6, 2024No Comments4 Mins Read
Facebook Twitter Pinterest LinkedIn Tumblr Reddit WhatsApp Email
Ai Predicts Mortality With Whole Body Mri For Personalized Health Insights
Share
Facebook Twitter LinkedIn Pinterest WhatsApp Email

Harnessing the power of artificial intelligence, researchers are unlocking the potential of whole-body MRI to predict health risks, paving the way for smarter, personalized prevention strategies.

Study: Body composition analysis based on deep learning from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population. Image credit: Juice Flair / Shutterstock

In a recent study published in the journal eBioMedicineresearchers in Germany and the United States developed and validated a deep learning framework for automated volumetric body composition analysis from whole-body Magnetic Resonance Imaging (MRI) and evaluated its prognostic value for predicting all-cause mortality in a large Western population.

Background

Body composition measures, including adipose tissue compartments and skeletal muscle, have shown strong associations with clinical outcomes and are emerging as important imaging biomarkers to improve personalized risk assessment. However, their routine quantification by imaging modalities such as MRI remains limited in clinical workflows due to time and resource constraints. With its superior ability to differentiate tissue types and assess their distribution, MRI offers significant potential for comprehensive analysis of body composition.

The study highlights that manual quantification is labor intensive, while automated approaches could overcome these obstacles. Fully automated volumetric approaches based on Artificial Intelligence (AI) could overcome current limitations, enabling more accurate and scalable assessments. These findings highlight the importance of developing standardized tools to ensure clinical applicability in diverse populations.

About the Study

The study used data from two large population-based cohort studies: the UK Biobank (UKBB), which included participants aged 45-84, and the German National Cohort (NAKO), with participants aged 40-75. Both studies collected comprehensive clinical data and used a detailed MRI protocol, including whole-body T1-weighted Dixon 3D Volumetric Interference Breath-Keeping Interference (3D VIBE) sequences, used to analyze body composition. Ethical approvals were obtained and informed consent was obtained from all participants.

The primary objective was to develop a deep learning framework for the automated quantification of volumetric body composition measures such as subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle (SM), skeletal muscle fat fraction (SMFF) and intramuscular adipose tissue (IMAT), using whole-body MRI. The performance of the framework was evaluated in the UKBB, focusing on its predictive value for all-cause mortality. The study also aimed to assess correlations between whole-body volumetric measurements and traditional single-slice body composition assessment at the L3 vertebra.

The deep learning model used Dixon sequence imaging inputs to generate segmentation masks, allowing quantification of the volumetric and somatic composition of a slice. Experienced radiologists performed manual annotations for model training and independently validated them. Statistical analyzes included survival modeling and association assessments, using harmonized data sets to minimize allocation differences.

Study Results

The UKBB cohort included 36,317 participants (18,777 women and 17,540 men) with a mean age of 65.1 ± 7.8 years and a mean body mass index (BMI) of 25.9 ± 4.3 kg/m². Body composition analysis revealed higher volumetric subcutaneous adipose tissue (VSAT), skeletal muscle fat fraction (VSMFF) and intramuscular adipose tissue (VIMAT) in females, while males showed greater visceral adipose tissue volume (VVAT) and skeletal muscle volume (VSM). (all p < 0.0001). Similar trends were seen among the 23,725 NAKO participants, whose mean age was 53.9 ± 8.3 years with a mean BMI of 27 ± 4.7 kg/m², as well as body composition measures of the single-incision area at the L3 vertebra for both cohorts.

During a median follow-up period of 4.77 years in the UKBB, 634 deaths (1.7%) were recorded. Kaplan-Meier survival curves showed that participants in the lowest 10th percentile of VSM and the highest 10th percentile of VSMFF and VIMAT showed significantly higher mortality rates (log-rank p <0.0001). Adjusted Cox regression analyzes revealed that lower VSM (aHR: 0.86, 95% CI [0.81–0.91]p < 0.0001) was associated with a reduced risk of mortality, whereas higher VSMFF (aHR: 1.07, 95% CI [1.04–1.11]p < 0.0001) and VIMAT (aHR: 1.28, 95% CI [1.05–1.35]p < 0.0001) were associated with increased risk. In contrast, volumetric VSAT and VVAT measurements showed no substantial association with mortality after adjustment for traditional risk factors.

Analysis of single-slice area measurements at L3 yielded results consistent with volumetric measurements, with lower skeletal muscle area (ASM) and higher fat fraction (ASMFF) and intramuscular adipose tissue (AIMAT) associated with mortality. However, after full adjustment, these associations weakened for ASM and AIMAT. Reclassification analyzes showed that volumetric measurements were more effective in identifying high-risk individuals than single-slice measurements, as evidenced by a significant net improvement in reclassification for skeletal muscle (NRI = 0.053, 95% CI [0.016–0.089]).

Correlation analysis between whole-body and single-slice volumetric measurements showed strong agreement at specific vertebral levels, such as L3 for VAT (R = 0.892) and SM (R = 0.944). These findings were replicated in the NAKO cohort, although the association differed significantly by BMI and gender strata. The deep learning framework demonstrated high accuracy, with Dice coefficients exceeding 0.86 and strong agreement between manual and automated segmentation results (r > 0.97).

conclusions

This study developed an automated deep learning framework for whole-body MRI-based body composition analysis and evaluated its prognostic value for predicting mortality in more than 30,000 individuals. Volumetric measures, including SM, SMFF, and IMAT, were independent predictors of mortality, outperforming traditional single-section approaches, which showed variable associations influenced by gender and BMI. Despite these strengths, the study acknowledged limitations, such as cohort demographics representing predominantly Western populations and limited follow-up duration, which could affect generalizability.

Future research should investigate the clinical complementarity of volumetric analysis with MRI in various populations and imaging protocols.

health Insights mortality MRI personalized predicts wholebody
bhanuprakash.cg
healthtost
  • Website

Related Posts

Organ chip technology accurately predicts chemotherapy response to patients with esophageal adenocarcinoma

June 29, 2025

Expansion of genetic code to mammalian cells using pseuduridine -modified codons

June 29, 2025

Discover a Dimmer Genetic switch that controls fetal growth

June 28, 2025

Leave A Reply Cancel Reply

Don't Miss
Fitness

15 easy ways to get 20 grams of protein (Personal Trainer Guide)

By healthtostJune 29, 20250

The acquisition of several proteins in your diet is vital to muscle repair, satiety and…

Organ chip technology accurately predicts chemotherapy response to patients with esophageal adenocarcinoma

June 29, 2025

How Barefoot Workout can make you stronger, more athletic and stunning in injuries

June 29, 2025

Books I have recently read – The Fitnessista

June 29, 2025
Stay In Touch
  • Facebook
  • Twitter
  • Pinterest
  • Instagram
  • YouTube
  • Vimeo
TAGS
Baby benefits body brain cancer care Day Diet disease exercise finds Fitness food Guide health healthy heart Life Loss Men mental Natural Nutrition Patients Pregnancy protein research reveals Review risk routine sex sexual Skin study Therapy Tips Top Training Treatment Understanding ways weight women Workout
About Us
About Us

Welcome to HealthTost, your trusted source for breaking health news, expert insights, and wellness inspiration. At HealthTost, we are committed to delivering accurate, timely, and empowering information to help you make informed decisions about your health and well-being.

Latest Articles

15 easy ways to get 20 grams of protein (Personal Trainer Guide)

June 29, 2025

Organ chip technology accurately predicts chemotherapy response to patients with esophageal adenocarcinoma

June 29, 2025

How Barefoot Workout can make you stronger, more athletic and stunning in injuries

June 29, 2025
New Comments
    Facebook X (Twitter) Instagram Pinterest
    • About Us
    • Contact Us
    • Privacy Policy
    • Terms and Conditions
    • Disclaimer
    © 2025 HealthTost. All Rights Reserved.

    Type above and press Enter to search. Press Esc to cancel.