Dental CBCT is a 3D imaging modality widely adopted to help dentists detect and diagnose jaw lesions. Due to minimum information loss (compared to conventional 2D radiography) and low radiation exposure (compared to conventional CT), it has become the “go-to” radiographic technique in various dental fields. Gaps: Accompanying the clear benefits of dental CBCT is an overwhelming amount of 3D data presented to clinicians. Clinician-based CBCT interpretation suffers from low inter-/intra-observer agreement and low accuracy. AI/Deep Learning (DL) holds great promise to automate CBCT image analysis and provide objective, accurate detection and diagnosis capabilities to support clinical decision. However, limited research has been done due to unique and significant challenges: (1) Dental CBCT provides 3D images composed of a complicated mix of different oral structures/contents, preventing the direct use of existing general-purse DL algorithms for image segmentation and calling for new DL designs. (2) AI/DL is known to be data-hungry. It is very difficult to obtain a large number of accurately-annotated CBCT images to train DL due to complex oral anatomy and inevitable human errors, which calls for efficient strategies to reduce annotation effort for DL training. (3) Due to these challenges, the current software systems used to assist clinicians in dental CBCT interpretation do not provide advanced AI- based lesion detection and diagnosis capabilities, which makes this STTR project timely and important. We recently developed a DL algorithm that integrates unique oral anatomy into the DL design, namely “Anatomically-Constrained dense UNet (AC-UNet)”. In addition to improving accuracy, AC-UNet is also annotation-efficient as it is not only trained using CBCT images but also constrained by anatomical domain knowledge through novel mathematical encoding and posterior regularization-based optimization. Applied to a preliminary dataset of CBCTs with periapical lesions indicative of Apical Periodontitis (AP), AC-UNet achieved high accuracy in segmentation and lesion detection on CBCT images and outperformed state-of-the-art DL algorithms. Our long-term goal is to develop the first-ever AI-based software system called “AIDen” to perform automatic segmentation, lesion detection, and differential diagnosis based on dental CBCT for a variety of jaw lesions/diseases with high accuracy, reliability, and reproducibility. AIDen will assist clinicians in providing optimal treatment decision for each patient. Our Phase-I goal is to develop and test the feasibility of AIDen for lesion detection and differential diagnosis focusing on AP, a highly-prevalent jaw lesion/disease. Three aims are: (1) Optimize design: to develop an extension of AC-UNet to integrate a broader range of different types of oral-anatomical knowledge into the DL design; (2) Optimize training: to develop an Active Learning strategy to further improve annotation efficiency of AC-UNet training; (3) Cli...