AI Enhanced Early Detection of Postoperative Complications: Comparing Traditional, Machine Learning, and Deep Learning Surveillance Models
Keywords:
postoperative complications, artificial intelligence, machine learning, deep learning, surgical site infection, remote monitoring, wearable sensors, risk predictionAbstract
Artificial intelligence (AI) is increasingly used not only to predict, but also to monitor and detect early complications after surgery, using diverse data streams such as electronic health records, vital signs, wearable sensors, and imaging,,. Machine learning (ML) and deep learning (DL) models can outperform traditional logistic regression and rule‑based systems in discriminating patients at risk of sepsis, pneumonia, surgical site infection (SSI), and mortality, while substantially reducing manual surveillance workload,,. Large, multi‑center neural network models that combine structured and unstructured data (e.g., 3.5 million surgical encounters) demonstrate rapid, multi‑outcome prediction, and specialized SSI‑prediction platforms show AUC values approaching 0.99 in focused cohorts,,. This article reviews AI models for early post‑surgical complication analysis, compares traditional, ML, and DL approaches, and visualizes relative performance and workload impact using a summary table and dual pie charts.
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