AI Spots Hidden Signs of Consciousness in Comatose Patients before Doctors Do
A machine-learning algorithm spotted signs of “covert consciousness” in coma patients—in some cases, days before doctors could do so
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Imagine lying in a hospital bed, awake but unable to move your body to communicate with the people around you. This experience of “covert consciousness” is a reality for many people who have sustained traumatic brain injuries. In a new study published in Communications Medicine, researchers found that they could detect signs of consciousness in comatose patients by using artificial intelligence to analyze facial movements that were too small to be noticed by clinicians.
Covert consciousness was first detected in 2006, when researchers asked an unresponsive woman and healthy volunteers to imagine doing specific tasks while in a brain scanner. The team found that the woman showed brain activity in the same regions as the volunteers. Just last year researchers using similar brain imaging methods found that one in four behaviorally unresponsive patients was covertly conscious. Such tests aren’t routinely performed on people in an unresponsive state because this type of neuroimaging is time-consuming and its operation requires specialized skills. Instead, doctors typically rely on more subjective visual examinations to gauge a person’s level of consciousness, testing whether they open their eyes, respond to commands or startle at a loud noise.
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Mofakham and her team recorded videos of 37 patients with recent brain injuries who outwardly appeared to be in a coma. They tracked the participants’ facial movements in extraordinary detail—on the level of individual pores—after they were given commands such as “open your eyes” or “stick out your tongue.” The researchers’ AI tracking tool, which they call SeeMe, detected any facial movements and then analyzed whether the movements were specific to the command given. SeeMe documented eye-opening responses in 30 of 36 patients and mouth movements in 16 of 17 patients with analyzable videos. Five of these patients did not go on to produce larger signs of movement that were visible to doctors, though most of the others did. On average, SeeMee detected participants trying to open their eyes and moving their mouths respectively 4.1 and 8.3 days before clinicians spotted these signs.
Next, Mofakham plans to examine whether patients can answer yes-or-no questions using specific facial movements. “This has a big ethical implication” because people who cannot communicate “cannot participate in their care,” she says. “This study opens a way to communicate with these patients.”
Andrew Chapman is a Truckee, Calif.–based freelance science writer who covers life sciences and the environment.
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