Getting a proper sleep diagnosis often means spending hours reviewing complex data from overnight monitors. This process is expensive and relies on human experts who can get tired or disagree with each other. A new study asked if smart computer models could do this job just as well without the cost and delay. The team tested these models on data from healthy people and those suspected of having sleep-disordered breathing. They compared the computer's results against four certified human experts who manually scored the same data. The goal was to see if the machines could be trusted to help doctors make faster, more consistent decisions.
When it came to identifying sleep stages and detecting moments of waking up, the computer models performed very well. Their accuracy was comparable to the agreement seen between different human experts reviewing the same data. This is a big deal because it means the technology is reliable for these specific tasks. However, the results for detecting breathing pauses, known as respiratory events, were different. While the models still performed better than previous studies, they did not match the agreement seen between human experts for this specific job.
This study shows that computers are excellent at some parts of sleep analysis but not all. For breathing events, the computer's limits of agreement were wider than those of human experts. This means the technology is not yet ready to fully replace human review for counting breathing pauses. Until this gap is closed, doctors will likely need to keep using their expertise to ensure accurate diagnosis. The findings offer hope for faster care, but they also remind us that current technology has clear limits.