Background: Assessment of root development and apical closure is critical in dental disciplines, including endodontics, trauma management, and age estimation. This study aims to leverage advances in deep learning Convolutional Neural Networks (CNNs) to automatically evaluate the apical region status of permanent first molars, highlighting a digital health application of AI in dentistry. Methods: In this retrospective study, 262 Cone Beam Computed Tomography (CBCT) scans were reviewed, and 147 anonymized dental images were cropped from pseudopanoramic radiographs, including standard measurements. Tooth regions were resized to 471 × 1075 pixels and split into training (80%) and test (20%) sets. CNN performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves with area under the curve (AUC), demonstrating AI-based image analysis in a dental context. Results: Precision, recall, and F1-scores were 0.79 for open roots and 0.81 for closed roots, with a macro average of 0.80 across all metrics. The overall accuracy and AUC were also 0.80. Conclusions: These results suggest that CNNs can be effectively used to assess apical patency from ROI images derived from pseudopanoramic radiographs.

Artificial intelligence (AI) is computer systems that can perform tasks that require human intelligence. It consists of data based on machine learning, deep learning and artificial neural networks. AI; with the increase in data collection and the ability to store large numbers of data, its use in the field of health has increased. It has been increasing rapidly recently. AI is being used more and more frequently with its features that help physicians in diagnosis, treatment planning, prognosis prediction and application of treatments. In this review, it is aimed to specify AI and its areas of use in the healthcare system.