The worldwide mammography equipment market is projected to reach $1.1 billion by 2015, according to a report from market research firm Global Industry Analysts (GIA).
A major driving force behind the market is the rising incidence of breast cancer; one out of every eight women in industrialized countries faces the risk of being affected by breast cancer, according to the San Jose, CA-based firm.
The U.S. and Europe contribute 80% of global sales volume of mammography equipment, according to the report. GIA said that technological innovations such as computer-aided detection (CAD) and digital mammography, as well as thermal mapping and optical imaging, are expected to offer positive growth prospects.
The report estimates that in 2008 the worldwide digital mammography equipment market will produce $610 million in revenues, according to GIA. In other report findings, the U.S. full-field digital mammography market is projected to reach $677 million by 2015.
The growth and renewed interest in new technologies has eroded demand for analog mammography equipment, however. For example, the European analog mammography equipment market is projected to dip to $52 million by 2010, GIA said.
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![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&dpr=2&fit=crop&h=167&q=70&w=250)
