The 42nd edition of Arab Health Exhibition and Congress was officially opened on 30 January by his Highness Sheikh Hamdan Bin Rashid Al Maktoum, deputy ruler of Dubai, United Arab Emirates (UAE) minister of finance, and the president of the Dubai Health Authority.
His Highness Sheikh Hamdan Bin Rashid Al Maktoum.Arab Health 2017 will feature 4,400 exhibitors, an increase of over 400 companies over last year's meeting. More than 110,000 attendees from over 70 countries are expected to take part in the conference, which runs from 30 January to 2 February at the Dubai International Convention and Exhibition Center.
This year's event also includes the debut of new hands-on-training sessions, which allow physicians, surgeons, and technicians from the region to learn and practice new techniques using the latest equipment in areas such as cardiology, neurology, surgery, gastroenterology, urology, oncology, and radiology, according to the conference. In addition, the 3D Medical Printing Zone has returned after a successful debut at Arab Health 2016. It has been expanded to reflect the growing use of 3D printing technology in healthcare, and will include items such as 3D-printed bionic limbs and real-life models of 3D-printed organ models, according to Arab Health.
A total of 14 conferences -- including the Total Radiology Conference -- will also be held over the four days of the meeting.
![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)
