Emergency surgery specialists have developed a deep neural network for triaging patients presenting with gunshot wounds.
In a tryout trial, the technique quickly and accurately predicted shock as well as the need for early massive transfusion and major surgery.
The research was conducted at Massachusetts General Hospital and is running in the June edition of the Journal of Trauma and Acute Care Surgery.
Senior author Noelle Saillant, MD, and colleagues trained their field-capable AI tool on data from close to 30,000 patients with gunshot wounds to the trunk or appendage junctions thereof.
Along with wound locations and apparent extents, the training data included vital signs and other basic information that’s usually readily available in the field.
In testing, the tool had a high degree of accuracy predicting shock, transfusion need and surgery urgency, Saillant and co-authors report.
“The presented model is an important proof-of-concept,” they write. “Future iterations will employ an expansion of databases to refine and validate the model, further adding to its potential to improve triage in the field, both in civilian and military settings.”
Study posted here (behind paywall).
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.