Traditional periodontal disease diagnosis relies heavily on subjective clinical examination and manual interpretation of X-rays, creating opportunities for missed early-stage disease when intervention would be most effective. This comprehensive analysis of 80 studies reveals how computational approaches are transforming dental diagnostics with remarkable precision.
The research examined two distinct technological approaches across studies from 2020-2025: fractal dimension analysis, which measures the complex geometric patterns of bone structure, and artificial intelligence models that can classify disease severity, segment affected areas, and detect bone loss automatically. AI systems demonstrated striking performance ranges, with some achieving perfect sensitivity and accuracy scores of 1.00, while others showed more modest results. The box-counting method emerged as the preferred fractal analysis technique, primarily applied to panoramic and periapical radiographs.
This represents a significant leap toward objective, standardized periodontal assessment that could revolutionize preventive dentistry. Current clinical practice depends on practitioner experience and subjective interpretation, leading to diagnostic variability between providers. Computational methods eliminate human bias while potentially identifying subtle bone changes invisible to conventional examination. However, the wide performance ranges highlight a critical limitation: these technologies remain inconsistently reliable across different populations and imaging conditions. The absence of standardized evaluation protocols also prevents direct comparison between systems. While promising for enhancing diagnostic accuracy and enabling earlier intervention, these tools require further validation and standardization before widespread clinical adoption can ensure consistent patient outcomes.