Bulletin of Stomatology and Maxillofacial Surgery
ISSN 1829-006X
2025; 52–55
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AI MODELS ANALYSE DATA FROM IMPLANTED SENSORS TO PREDICT INFECTION, REJECTION, OR MECHANICAL FAILURE

Melwin Ebenezer*, Swarnapriya R., Sunanda Sudhakaran, Risvana S S., Priya Nagar, Anitha Subbappa

Sree Balaji Dental College and Hospital · KMCT Group of Educational Institutions · Institute of Dental Science , ROHTAK · JSS Dental College and Hospital

Received: 2026-04-08 · Published: 2025-09-21

© 2025 The Author(s) and the original publisher.

CC BY-NC 4.0 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Original title
AI MODELS ANALYSE DATA FROM IMPLANTED SENSORS TO PREDICT INFECTION, REJECTION, OR MECHANICAL FAILURE
Authors
Melwin Ebenezer, Swarnapriya R., Sunanda Sudhakaran, Risvana S S., Priya Nagar, Anitha Subbappa
Source journal
Bulletin of Stomatology and Maxillofacial Surgery
Published
2025-09-21
Licence
Creative Commons Attribution-NonCommercial 4.0 International
Original
https://doi.org/10.58240/1829006X-2025.21.9-52

Abstract

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.
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