Observational studyScientific reports2019
Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury.
Observational study in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Spine muscle auto segmentation techniques in MRI imaging: a systematic review.BMC musculoskeletal disorders · 2024Pooled it
- Comparison between movement pattern training and strengthening on muscle volume, muscle fat, and strength in patients with hip-related groin pain: An exploratory analysis.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2022Trial
- A novel multimodal AI-based radiomics approach for precision assessment of pain intensity in chronic nonspecific low back pain.Journal of orthopaedic translation · 2026Article
- Trustworthy deep learning for the automated quantification of the fatty infiltration of the rotator cuff muscles using magnetic resonance imaging.JSES international · 2025Article
- Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach.Journal of cachexia, sarcopenia and muscle · 2024Article
- MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle.Journal of imaging · 2024Review
- MRI-based assessment paraspinal extensor muscle fatty infiltration in acute cervical spinal cord injury patients - a retrospective study.BMC musculoskeletal disorders · 2024Article
- Morphological Changes of Deep Extensor Neck Muscles in Relation to the Maximum Level of Cord Compression and Canal Compromise in Patients With Degenerative Cervical Myelopathy.Global spine journal · 2024Article
- Three-dimensional freehand ultrasonography to measure muscle volume of the lumbar multifidus: Reliability of processing technique and validity through comparison to magnetic resonance imaging.Journal of anatomy · 2024Article
- Artificial Intelligence in Spine and Paraspinal Muscle Analysis.Advances in experimental medicine and biology · 2024Review
- Biopsychosocial sequelae and recovery trajectories from whiplash injury following a motor vehicle collision.The spine journal : official journal of the North American Spine Society · 2023Article
- The association between morphological characteristics of paraspinal muscle and spinal disorders.Annals of medicine · 2023Review
- IMFSegNet: Cost-effective and objective quantification of intramuscular fat in histological sections by deep learning.Computational and structural biotechnology journal · 2023Article
- Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain.Scientific reports · 2022Article
- Towards defining muscular regions of interest from axial magnetic resonance imaging with anatomical cross-reference: a scoping review of lateral hip musculature.BMC musculoskeletal disorders · 2022Article
- Classification of rotator cuff tears in ultrasound images using deep learning models.Medical & biological engineering & computing · 2022Article
- Analysis of the paraspinal muscle morphology of the lumbar spine using a convolutional neural network (CNN).European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults.Frontiers in rehabilitation sciences · 2022Article
- Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.Scientific reports · 2021Article
- A deep learning model for detection of cervical spinal cord compression in MRI scans.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
Abstract
Muscle fat infiltration (MFI) of the deep cervical spine extensors has been observed in cervical spine conditions using time-consuming and rater-dependent manual techniques. Deep learning convolutional neural network (CNN) models have demonstrated state-of-the-art performance in segmentation tasks. Here, we train and test a CNN for muscle segmentation and automatic MFI calculation using high-resolution fat-water images from 39 participants (26 female, average = 31.7 ± 9.3 years) 3 months post whiplash injury. First, we demonstrate high test reliability and accuracy of the CNN compared to manual segmentation. Then we explore the relationships between CNN muscle volume, CNN MFI, and clinical measures of pain and neck-related disability. Across all participants, we demonstrate that CNN muscle volume was negatively correlated to pain (R = -0.415, p = 0.006) and disability (R = -0.286, p = 0.045), while CNN MFI tended to be positively correlated to disability (R = 0.214, p = 0.105). Additionally, CNN MFI was higher in participants with persisting pain and disability (p = 0.049). Overall, CNN's may improve the efficiency and objectivity of muscle measures allowing for the quantitative monitoring of muscle properties in disorders of and beyond the cervical spine.
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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.