| Summary: | Feedback systems have been utilized to reduce the possible thermal side effects of lasers for surgery by means of temperature monitoring to control irrigation systems. In this study, we investigated the potential application of optical coherence tomography as a means of detecting bone dehydration status. We investigated the penetration depth of the OCT laser and its respective relation to the hydration status of bone. A deep-learning method was utilized to differentiate between different levels of water content in bone tissue (fresh/hydrated, dehydrated, and carbonized) based on the OCT images. The proposed model achieved an accuracy of 0.912 on an independent test set, demonstrating its ability to accurately predict the state of the bone considering these three conditions. We believe this method can potentially accelerate the detection of dehydration during laser surgery, improving the safety of using lasers with real-time feedback.
|
|---|