dxa

SDL Digital Experience Accelerator Java Spring MVC web application

SDL Digital Experience Accelerator .NET MVC web application

Generative medical imaging model that produces real DXA scan from 3D body surface scan inputs. Predicting interior fat, lean, and bone distribution fom exterior body shape.

SDL Web E-Commerce Framework

Tridion Digital Experience Accelerator Modules

SDL Tridion Sites Instant Campaign (aka DXA Flexible Campaign Content)

SDL Digital Experience Accelerator CM-side framework (.NET Template Building Blocks)

SDL Digital Experience Accelerator Whitelabel HTML Design

Modular DXA-implementation. Supports multiple lattice types and structure identification methods including PTM, CNA, and Diamond structure analysis. Identifies dislocation lines, computes Burgers vectors, constructs dislocation networks, and exports results for visualization.

SDL Digital Experience Accelerator Model Service

Data and R code for the meta-analysis of DXA-measured lean and fat mass with GLP-1 and dual GIP/GLP-1 receptor agonists (phase 3 RCTs; PROSPERO CRD420261323497). v2.0: corrected data, REML-HKSJ analysis.

The next-generation platform designed for high-performance research teams and enthusiastic materials scientists!

External preprocessing and analysis code for evaluating a muscle-bone imbalance phenotype in relation to fracture and osteoporosis risk across NHANES, KNHANES, and HRS.

SI4T = Search Integration 4 Tridion, a framework to help you get SDL Tridion CMS managed content into your web app search index

A desktop GUI tool for batch-extracting body composition and bone density data from Hologic DXA PDF summary reports into a single CSV file. Built for longitudinal research studies with pre/post or multi-timepoint scan designs. Developed at the Cardiovascular and Applied Physiology (CAP) Lab, Florida State University.

Body fat and lean mass from two phone photos — 2.8% MAE against clinical DXA scans. Two-stage gradient-boosted pipeline trained on 11,701 NHANES DXA scans and 2,018 scanned bodies, compiled to 1.4 MB and running entirely in the browser. Nothing is uploaded.

OrthoCare_AI is an AI-powered orthopaedics and musculoskeletal health research platform. Content is compiled from published medical literature, clinical guidelines (AAOS, BOA, NICE, WHO), and research databases. It enables upload and analysis of reports. This open, collaborative system grows stronger with every user. Share it widely.

DD4T content model (.NET)

DD4T Tridion Templates