Image management technology developer Mach7 Technologies and 3D Medical have signed a definitive agreement to merge.
Primarily, the merger allows Mach7 access to new capital via public markets in Australia, where 3D Medical is based.
Under the terms of the agreement, 3D Medical will issue of 459.5 million shares to the owners of Mach7 in return for 100% of Mach7 assets and intellectual property. Shares will also be made available to the owners of Mach7 subject to the achievement of agreed-upon financial milestones.
In addition, 3D Medical intends to raise new funding of up to $10 million Australian ($7.3 million U.S.) to provide working capital to support further research and development, customer support, sales, and marketing activities for Mach7, as well as to build out 3D Medical's data offerings, absolve Mach7's debt (about $2 million U.S.), and pay for the cost of the merger.
Both firms plan to maintain their respective workforces in engineering, customer sales, support, marketing, and service relationships without alteration, they said. Subject to shareholder approval and upon completion of the merger, 3D Medical will change the name of the company to Mach7 Technologies and appoint Albert Liong as managing director of the group.

















![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)
