1a76f118b54e21852e966c43a1d3269c nutrients-18-01477.pdf d34227cc6e5d4b2964b8151cb3a3a5d1520cfbf3 nutrients-18-01477.pdf 3dd380eb0e3f6417e93a9634661a925e02b59f0ee286c4c12cf00d13d0de9d14 nutrients-18-01477.pdf Title: FocusedON-BC: A Robust Deep Learning Framework for Automated Body Composition Assessment Subject: Background: Computed tomography-based body composition assessment enables the quantification of clinically relevant prognostic conditions such as sarcopenia, myosteatosis, and visceral adiposity; the manual segmentation process limits its routine implementation in clinical practice. We developed FocusedON-BC, an automated deep learning tool for opportunistic screening of skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) across the T12–L5 range; Methods: Validated on a multicenter cohort of 518 patients (3280 slices) with diverse body mass index (12.7–47.7 kg/m2) from different computed tomography manufacturers. Performance was benchmarked against expert segmentation using the Dice coefficient score (DSC) and the mean absolute error (MAE); Results: FocusedON-BC achieved expert-level accuracy: mean DSC was 0.974 0.010 (SM), 0.959 0.032 (VAT), and 0.986 0.014 (SAT). Clinical MAE remained <5% for all compartments. Performance was robust, independent of body mass index and computed tomography scanner model. Qualitative assessment confirmed the tool's capability to isolate intermuscular adipose tissue for radiodensity analysis; Conclusions: FocusedON-BC provides accurate, vendor-agnostic body composition and muscle quality analysis. Its reliability across diverse phenotypes supports implementation for routine nutritional screening. Keywords: body composition; computed tomography; deep learning; opportunistic screening Author: Jano Manuel Rubio-García, Andrés Jiménez-Sánchez, Fiorella Palmas, Cora Oliver-Vila, Aitor Rodriguez-Martinez, Nuria Roson-Gradaille, Selenia Maria Medina-Hernandez, Gabriel Santana-Quintana, Eduardo J. Carrasco, Raul Guerra, Rosa Burgos and Andreea Ciudin Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Wed May 6 15:08:48 2026 CEST ModDate: Wed May 6 15:12:59 2026 CEST Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 23 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 5840931 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HGVYKS+URWPalladioL-Bold Type 1 Custom yes yes yes 180 0 BPMOZR+CMSY10 Type 1 Builtin yes yes yes 181 0 RESPMO+URWPalladioL-Roma Type 1 Custom yes yes yes 182 0 CGHAOU+URWPalladioL-Ital Type 1 Custom yes yes yes 183 0 SYFPBV+CMMI10 Type 1 Builtin yes yes yes 230 0 RFLZJB+CMR10 Type 1 Builtin yes yes yes 263 0 MUFUZY+Arial-BoldMT CID TrueType Identity-H yes yes yes 314 0 MUFUZY+ArialMT CID TrueType Identity-H yes yes yes 315 0 QQXUPP+PazoMath Type 1 Builtin yes yes yes 348 0 GGZKKH+CMBX10 Type 1 Builtin yes yes yes 360 0 ICBUDW+URWPalladioL-BoldItal Type 1 Custom yes yes yes 361 0 MDIKPC+CMBSY10 Type 1 Builtin yes yes yes 377 0 BMQQDV+DejaVuSans Type 3 Custom yes yes no 386 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2026-05-08 04:24:56 CEST RepresentationInformation: nutrients-18-01477.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2026-05-07 14:07:01 CEST Size: 5840931 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 706 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: Materials and Methods Children: Item: Title: Study Design Item: Title: Manual Segmentation and Ground Truth Generation Children: Item: Title: Muscle Tissue Item: Title: Adipose Tissue Item: Title: Body Region Item: Title: FocusedON-BC Architecture Children: Item: Title: Data Pre-Processing Item: Title: Network Architecture Item: Title: Data Post-Processing Item: Title: Training and Evaluation Strategy Item: Title: Statistical Analysis and Performance Metrics Item: Title: Results Children: Item: Title: Study Population Item: Title: Segmentation Performance Item: Title: Clinical Measurement Precision Item: Title: Algorithm Robustness and Technical Interoperability Children: Item: Title: Impact of Sex Item: Title: Impact of Body Mass Index Item: Title: Impact of Age Item: Title: Impact of CT Contrast Item: Title: Technical Interoperability (Scanner Manufacturer) Item: Title: Independent Hold-Out Test Item: Title: Qualitative Assessment Item: Title: Discussion Item: Title: Conclusions Item: Title: References Info: Title: FocusedON-BC: A Robust Deep Learning Framework for Automated Body Composition Assessment Author: Jano Manuel Rubio-García, Andrés Jiménez-Sánchez, Fiorella Palmas, Cora Oliver-Vila, Aitor Rodriguez-Martinez, Nuria Roson-Gradaille, Selenia Maria Medina-Hernandez, Gabriel Santana-Quintana, Eduardo J. Carrasco, Raul Guerra, Rosa Burgos and Andreea Ciudin Subject: Background: Computed tomography-based body composition assessment enables the quantification of clinically relevant prognostic conditions such as sarcopenia, myosteatosis, and visceral adiposity; the manual segmentation process limits its routine implementation in clinical practice. We developed FocusedON-BC, an automated deep learning tool for opportunistic screening of skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) across the T12–L5 range; Methods: Validated on a multicenter cohort of 518 patients (3280 slices) with diverse body mass index (12.7–47.7 kg/m2) from different computed tomography manufacturers. Performance was benchmarked against expert segmentation using the Dice coefficient score (DSC) and the mean absolute error (MAE); Results: FocusedON-BC achieved expert-level accuracy: mean DSC was 0.974 0.010 (SM), 0.959 0.032 (VAT), and 0.986 0.014 (SAT). Clinical MAE remained <5% for all compartments. Performance was robust, independent of body mass index and computed tomography scanner model. Qualitative assessment confirmed the tool's capability to isolate intermuscular adipose tissue for radiodensity analysis; Conclusions: FocusedON-BC provides accurate, vendor-agnostic body composition and muscle quality analysis. Its reliability across diverse phenotypes supports implementation for routine nutritional screening. 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