Towards Deep Learning-Based Sarcopenia Screening with Body Joint Composition Analysis

2021 ◽  
Author(s):  
Yung-Chih Chen ◽  
Jun-Wei Hsieh ◽  
Yao-Hong Yang ◽  
Chien-Hung Lee ◽  
Pei-Yi Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Composition Analysis ◽  
Body Joint

scholarly journals Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis

2021 ◽  
Vol 10 (2) ◽  
pp. 356
Author(s):  
Lennard Kroll ◽  
Kai Nassenstein ◽  
Markus Jochims ◽  
Sven Koitka ◽  
Felix Nensa
Keyword(s):  
Adipose Tissue ◽  
Deep Learning ◽  
Coronary Artery ◽  
Cardiac Ct ◽  
Coronary Calcification ◽  
Risk Scores ◽  
Agatston Score ◽  
Composition Analysis ◽  
Significance Level ◽  
Tissue Quantification

(1) Background: Epi- and Paracardial Adipose Tissue (EAT, PAT) have been spotlighted as important biomarkers in cardiological assessment in recent years. Since biomarker quantification is an increasingly important method for clinical use, we wanted to examine fully automated EAT and PAT quantification for possible use in cardiovascular risk stratification. (2) Methods: 966 patients with intermediate Framingham risk scores for Coronary Artery Disease referred for coronary calcium scans were included in clinical routine retrospectively. The Coronary Artery Calcium Score (CACS) was extracted and tissue quantification was performed by a deep learning network. (3) Results: The Computed Tomography (CT) segmentations predicted by the network indicated no significant correlation between EAT volume and EAT radiodensity when compared to Agatston score (r = 0.18, r = −0.09). CACS 0 category patients showed significantly lower levels of total EAT and PAT volumes and higher EAT and PAT densities than CACS 1–99 category patients (p < 0.01). Notably, this difference did not reach significance regarding EAT attenuation in male patients. Women older than 50 years, thus more likely to be postmenopausal, were shown to be at higher risk of coronary calcification (p < 0.01, OR = 4.59). CACS 1–99 vs. CACS ≥100 category patients remained below significance level (EAT volume: p = 0.087, EAT attenuation: p = 0.98). (4) Conclusions: Our study proves the feasibility of a fully automated adipose tissue analysis in clinical cardiac CT and confirms in a large clinical cohort that volume and attenuation of EAT and PAT are not correlated with CACS. Broadly available deep learning based rapid and reliable tissue quantification should thus be discussed as a method to assess this biomarker as a supplementary risk predictor in cardiac CT.

scholarly journals Deep-learning-based cell composition analysis from tissue expression profiles

2019 ◽  
Author(s):  
Kevin Menden ◽  
Mohamed Marouf ◽  
Sergio Oller ◽  
Anupriya Dalmia ◽  
Karin Kloiber ◽  
...  
Keyword(s):  
Deep Learning ◽  
Expression Profiles ◽  
Tissue Expression ◽  
Mouse Tissue ◽  
Composition Analysis ◽  
Complex Data ◽  
Rna Seq ◽  
Data Types ◽  
Multiple Data ◽  
Multiple Data Sets

AbstractWe present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple data sets. Due to this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning

Radiology ◽  
2019 ◽  
Vol 290 (3) ◽  
pp. 669-679 ◽  
Author(s):  
Alexander D. Weston ◽  
Panagiotis Korfiatis ◽  
Timothy L. Kline ◽  
Kenneth A. Philbrick ◽  
Petro Kostandy ◽  
...  
Keyword(s):  
Body Composition ◽  
Deep Learning ◽  
Ct Scans ◽  
Composition Analysis ◽  
Body Composition Analysis

scholarly journals Deep learning–based cell composition analysis from tissue expression profiles

2020 ◽  
Vol 6 (30) ◽  
pp. eaba2619 ◽  
Author(s):  
Kevin Menden ◽  
Mohamed Marouf ◽  
Sergio Oller ◽  
Anupriya Dalmia ◽  
Daniel Sumner Magruder ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Expression Profiles ◽  
Tissue Expression ◽  
Mouse Tissue ◽  
Composition Analysis ◽  
Complex Data ◽  
Rna Seq ◽  
Data Types ◽  
Multiple Datasets

We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

scholarly journals Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2314
Author(s):  
Anton Faron ◽  
Nikola S. Opheys ◽  
Sebastian Nowak ◽  
Alois M. Sprinkart ◽  
Alexander Isaak ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Composition ◽  
Adipose Tissue ◽  
Deep Learning ◽  
Immune Checkpoint ◽  
Composition Analysis ◽  
Skeletal Muscle Index ◽  
Checkpoint Inhibitor Therapy ◽  
Tissue Compartments ◽  
The Impact

Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945–0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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