Cone-beam computed tomography (CBCT) images suffer from artifacts due to factors such as photon starvation, beam hardening, scatter radiation, and patient movement, which degrade image quality. These artifacts manifest as streaks, cupping effects, or distortions, particularly in regions with high-density structures like dental fillings or bone. In radiation therapy for the head and neck, such artifacts can obscure anatomical details and reduce the accuracy of dose calculations and target delineation. By removing or reducing these artifacts, CBCT images become more reliable for treatment planning, ensuring precise tumor targeting while minimizing radiation exposure to healthy tissues, ultimately improving therapeutic outcomes.
Developing frameworks for reporting artificial intelligence (AI) outputs in oral health care is crucial to ensure fairness, standardization, and transparency in clinical decision-making. AI models can be influenced by biases in training data, leading to disparities in diagnostic accuracy across different patient populations. Without standardized reporting, variations in AI performance and decision-making criteria may go unrecognized, potentially compromising patient safety and equity in care. A well-defined framework ensures that AI outputs are interpretable, reproducible, and aligned with clinical standards, enabling practitioners to trust and effectively integrate AI into practice while minimizing biases and improving patient outcomes.
Research in anatomical sciences is essential for developing image-guided surgical protocols that leverage new digital technologies in oral surgery and other dentistry-related procedures. A deep understanding of anatomical structures, variations, and spatial relationships enables the creation of precise, minimally invasive techniques that enhance surgical accuracy and patient safety. Advances in imaging modalities, such as cone-beam computed tomography (CBCT) and intraoperative navigation, combined with digital planning tools like 3D printing and artificial intelligence, allow for personalized treatment approaches. By integrating these innovations, research ensures that surgical protocols evolve to improve clinical outcomes, reduce complications, and optimize patient-centered care.
