Bone quality prediction using dental diagnostic images has become an important goal for improving treatment planning, implant stability assessment, and understanding overall bone health. There have been many studies on predicting bone condition by measuring clear and objective features from routine images, instead of relying only on simple visual inspection. These measurements include structural patterns, cortical thickness, radiographic density (or Hounsfield units in CBCT or CT), and texture features such as fractal dimension, which can provide useful clues about both local bone structure and general bone strength. Various methods have been used so far, and each has strengths and weaknesses in accuracy, reproducibility, and sensitivity to imaging conditions. Many reports show that these image-based features can reflect changes in trabecular organization and cortical quality, allowing earlier detection of weak bone and more reliable preoperative assessment. This lecture will review current evidence on predicting bone quality from diagnostic images, compare the advantages and limitations of commonly used approaches, and discuss how these techniques may be applied in clinical practice, along with future directions for more stable and practical prediction.