The primary objective of the study is to develop and validate a machine learning model for the automatic identification of periodontal vertical bone defects, improving diagnostic accuracy and efficiency. The study comprises three phases: 1. Public dataset annotation: Approximately 7,000 intraoral radiographs will be manually annotated by experts to classify periodontal bone defects (1-wall, 2+ walls, craters, furcation involvement). 2. Model training: A deep learning algorithm will be trained on the annotated images to learn automatic recognition of the defects. 3. Clinical validation: The model will be tested on a dataset of 150 anonymized radiographs from 20-30 patients treated at AOU (Azienda Ospedaliero Universitaria) Cagliari, comparing its performance to expert dental evaluations.
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Intersection over Union (IoU)
Timeframe: Baseline
Precision (P)
Timeframe: Baseline
Recall (R)
Timeframe: Baseline