Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_41_библиотеки_им_акад_М_И_Перельмана

AI’s involvement in sleep medicine may also extend to other disorders, such as narcolepsy. Multiple sleep latency testing (MSLT) has limited specicity (73.3% at the <8minute mean sleep onset latency cutoff), due at least in part to suboptimal interrater reliability among epochs scored [83]. Researchers have leveraged ML to stage as little as 5seconds of sleep, a level of precision much greater than the con­ventional 30-second epoch scoring. They demonstrated 96% sensitivity and 91% specicity for narcolepsy Type 1, a specicity that rises to 99% when adding HLA­DQB1*06:02 typing to their model [84]. As the authors state, AI-guided diagnosis should not supplant BCSMP review and judgment. On the contrary, AI can be uti­lized as another type of twenty-rst century “physician extender,” allowing them to focus more efciently on the most complex patient management issues while reach­ing a larger population. Reviewing Fig.4.1, one can foresee AI assisting both sleep clinicians and non-clinicians at each step in the initial symptom presentation and evaluation process. Indeed, even non-sleep clinicians could be guided to order home sleep apnea testing (HSAT) in the higher risk patients for OSA (Fig.4.1, Point D). AI could then assist BCSMPs in interpreting the studies, prescribing the most effec­tive therapies, and assisting clinicians with ongoing follow up (e.g., anticipate needed changes in PAP settings or to other modes of treatment).