AI MODELS ANALYSE DATA FROM IMPLANTED SENSORS TO PREDICT INFECTION, REJECTION, OR MECHANICAL FAILURE

Background:Implantable biomedical devices are playing an increasingly vital role in modern healthcare.
However, their long-term success is often threatened. Early detection of complications is crucial for patient
safety and implant longevity.
Objective:This study investigated the potential of artificial intelligence (AI) models to interpret real-time data
from sensors embedded in implants. With the goal of predicting and preventing common post-implantation
complications.
Methods:Our dataset representing 500 cases of implanted devices, capturing sensor data relevant to three
major complication domains: infection, immunologic rejection, and mechanical failure. A total of 15 AI
models—including traditional machine learning algorithms and advanced deep learning approaches—were
evaluated for their effectiveness.
Results:Deep learning techniques such as Long Short-Term Memory (LSTM) networks and autoencoders
showed superior performance in detecting temporal anomalies within continuous sensor data.
Conclusion:The findings support the integration of AI, particularly deep learning frameworks, into nextgeneration implantable systems that could provide continuous, intelligent monitoring to anticipate
complications before they become critical.