Researchers at Johns Hopkins University have made a groundbreaking discovery using artificial intelligence to analyze routine heart tests, also known as electrocardiograms (ECGs). They found that AI can detect previously undetected signals in ECGs that strongly predict which patients will suffer potentially deadly complications after surgery.
The AI model, trained on ECG data from 37,000 patients, can predict post-surgical complications with 85% accuracy when combined with patient medical records. This is a significant improvement over current risk scores used by doctors, which are only accurate in about 60% of cases. The researchers believe that this technology could revolutionize decision-making and risk calculation for patients and surgeons, potentially saving lives.
By analyzing a routine ECG, doctors could identify patients at high risk of complications and take steps to mitigate those risks. This could provide personalized risk assessments for patients undergoing surgery, allowing them to make more informed decisions about their care. The team plans to further test the model on larger datasets and explore what other information can be extracted from ECGs using AI.
The potential impact of this technology is significant, and it could lead to improved patient outcomes and more effective care. As the researchers continue to refine and develop their model, it's likely that AI will play an increasingly important role in predicting and preventing post-surgical complications.
