GE Healthcare has joined forces with Santa Clara University in a partnership aimed at sparking innovation in maternal and child healthcare in sub-Saharan Africa.
GE is partnering with the university's Miller Center for Social Entrepreneurship to try to reduce the global maternal mortality ratio and end preventable deaths of newborns and children younger than 5 years. The agreement will focus on a training and mentoring program for social entrepreneurs working on maternal and child health innovations in the region.
The healthymagination Mother and Child program will help these social enterprises strengthen their business models, refine business plans, reinforce organizational development, manage talent, and learn how to scale sustainably, according to GE and Santa Clara University.
The program is being offered to 15 to 20 selected participants and will utilize the university's Global Social Benefit Institute methodology. Following a three-day, in-person workshop in Nairobi, Kenya, participants will have access to a six-month online program as well as weekly, in-depth mentoring from Silicon Valley-based executives. By introducing participants to GE's portfolio of products, organizations will also gain specialized support and training on technologies and resources for the maternal and child health sector, GE and Santa Clara University said.
Specifically, the program is aimed at social enterprises focused on the following areas:
- Delivery of health services to mothers and children
- Medical equipment distribution, training, use, or maintenance
- Development of products or technologies that improve knowledge and/or access to care, such as telemedicine, mobile technologies, data analysis, or image interpretation
- Infrastructure services or facilities associated with needs from pregnancy to pediatric care
Qualified leaders of for-profit, nonprofit, or hybrid enterprises need to apply online by May 18; finalists will be announced after a formal review and interview process by a panel of judges from GE and Santa Clara University.
The initiative will end in Nairobi in February 2017 with an investor showcase event, in which the program finalists will have the opportunity to pitch their enterprises and healthcare innovations to a large group of active investors in early-stage social enterprises.
![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.auntminnie.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)
