Developing neural network-based models for image analysis can be time-consuming, requiring dataset design and model training. No-code AI platforms allow users to annotate object features without coding. Corrective annotation, a "human-in-the-loop" approach, refines AI segmentations iteratively. Dentistry has seen success with no-code AI for segmenting dental restorations. This study aims to assess radiographic features related to root canal treatment quality using a "human-in-the-loop" approach.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Accuracy
Timeframe: through data collection, an average of 6 months
Sensitivity
Timeframe: through data collection, an average of 6 months
Specificity
Timeframe: through data collection, an average of 6 months