What is it about?
Modern endodontic treatments are highly effective in saving teeth that might otherwise need to be extracted. However, like any medical procedure, there is always a chance of failure. The aim of this study was to develop and evaluate non-surgical endodontic treatment outcome prediction model using deep learning technology.
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Why is it important?
The proposed model is expected to provide AI second opinions for preoperative endodontic treatment planning to ensure that patients receive the most appropriate treatment.
Perspectives
Integrating AI into clinical applications can be difficult due to clinicians’ distrust of computer predictions and the potential risks associated with erroneous results. Future work should be designed to use AI models to trigger a second opinion in cases of disagreement between the clinician and the algorithm.
Prof. Siriwan Suebnukarn
Thammasat University
Read the Original
This page is a summary of: Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: A retrospective study, PLOS One, December 2024, PLOS,
DOI: 10.1371/journal.pone.0310925.
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