ArticleScientific reports2020
Large-scale biometry with interpretable neural network regression on UK Biobank body MRI.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 3 citations in OpenAlex.
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- Artifact-free fat-water separation in Dixon MRI using deep learning.Journal of big data · 2023Article
- A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans.Radiology. Artificial intelligence · 2022Article
- Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics.Yearbook of medical informatics · 2021Review
- Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R[Formula: see text]) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.
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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.