The innovation just won first place in the Society for Technology in Anesthesia’s Engineering Challenge, and could improve post-surgical care for all anesthesia patients, according to a Feb. 17 press release.
The development team was led by medical students Michael Ma and Maharshi Pandya. They wanted to create an objective way to determine patient recovery status following sedation.
Anesthesiologists typically use patient speech patterns to determine recovery status, according to the report. The machine uses machine learning and deep neural networks to detect elements of slurred speech.
To demonstrate the effectiveness of the machine, the team used pre-recorded voice samples of intoxicated people.
With the engineering challenge win, the team took home $1,000 to further develop and improve the machine model.
At the Becker’s 32nd Annual Meeting: The Business and Operations of ASCs, taking place October 29-31 in Chicago, ASC leaders, surgeons and healthcare executives will explore strategies to drive growth, enhance operational performance, navigate reimbursement challenges and prepare for the future of ambulatory surgery. Apply for complimentary registration now.