A radiographer in Malaysia has played a central role in designing a bed that allows trauma patients to undergo imaging more comfortably, according to a report published on 31 March in New Strait Times.
Shamsul Kamal Mohamad Khair, who works at Selama Hospital, was inspired to design the SmartBED after seeing accident victims experience pain when being moved from a stretcher to a bed for x-rays.
"On numerous occasions, I had witnessed the difficulties faced by road accident victims who would complain of pain when they were being transferred from the stretcher to another bed for x-ray screening," said Shamsul Kamal. "Since that day, the initial design of the SmartBED has gone through improvements and changes for about 80 times to ensure that the medical equipment could help speed up matters and lessen the pain usually experienced among patients when transferring beds to undergo x-ray imaging."
The device was launched recently during Ministry of Health Malaysia's Innovation Day. The vendor is Medical Apparatus Supplies.
![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)
