Abu Dhabi's SWAAC ELSO Conference Explores AI Applications In ECMO For Heart And Lung Failure Treatments

The second day of the 11th Annual SWAAC ELSO Conference in Abu Dhabi featured several important sessions. This event, held at the Conrad Hotel, has attracted over 1,000 international experts and clinicians specialising in extracorporeal life support (ECLS) from the GCC and worldwide. The conference's agenda focused on ECMO applications for heart and lung failure due to trauma, burns, cancer, and hypoxemia.

Today's sessions also explored recent advances in anticoagulation for ECMO patients and strategies for patient rehabilitation during ECMO treatment. A parallel track concentrated on ECMO use in children, drawing significant attendance. Attendees had the chance to examine over 60 research posters and oral presentations selected from nearly 100 submissions. These were chosen for their methodological quality or innovative uses of ECMO and AI.

Dr. Umar Khan, Chair of the Research Committee and Consultant Critical Care at Cleveland Clinic Abu Dhabi, announced that five exceptional research papers have been selected for conference awards. The exhibition area was bustling with interest as industry stakeholders displayed a wide range of new devices and technologies.

The Department of Health (DoH) Abu Dhabi has accredited the conference, allocating 22.55 hours for continuing medical education and 44 hours for workshops. The event is supported by DoH, the Department of Culture and Tourism Abu Dhabi, Cleveland Clinic Abu Dhabi, and Sheikh Khalifa Medical City.

The conference's engaging agenda ensured full attendance in lecture halls throughout all sessions. This gathering serves as a platform for sharing knowledge on ECMO's role in treating critical conditions like cancer and hypoxemia while highlighting innovations in patient care.

As the conference progresses, it continues to foster collaboration among experts in ECLS from diverse regions. The discussions aim to enhance understanding of ECMO's potential benefits across various medical scenarios.

With inputs from WAM