Automated Dental Image Annotation via Machine Learning Models

The patent introduces machine learning models as an intermediary between the X-ray images and the diagnostic conclusion. The ML model processes the images and provides annotated detections of pathologies, anatomies, and anomalies, serving as a mediator that enhances diagnostic accuracy without completely replacing the healthcare provider's role. This intermediary approach allows the system to maintain high reliability while managing complexity through automated processing.

The patent replaces the mechanical process of manual visual analysis by healthcare providers with an automated machine learning-based system. Instead of relying on human eyes and experience to detect abnormalities, the system uses trained neural networks to automatically analyze X-ray images, providing consistent and reliable diagnostic support. This substitution of mechanical human analysis with automated computational processes directly improves diagnostic accuracy.

The machine learning model provides self-service analysis by automatically reviewing X-ray images without requiring multiple healthcare providers. The system independently detects and annotates pathologies, anatomies, and anomalies, providing consistent diagnostic insights in a single automated process. This self-service capability eliminates the need for repeated manual reviews while maintaining diagnostic consistency, thereby significantly improving productivity.

The system incorporates feedback mechanisms where the machine learning model continuously learns from annotated data and refines its detection capabilities. The feedback loop allows the model to improve its accuracy over time based on ground truth labels and expert annotations, ensuring consistent and reliable diagnostic results without requiring multiple human reviewers. This feedback-driven improvement maintains diagnostic consistency while reducing time consumption.

The patent segments the diagnostic system into distinct functional components: image processing modules, machine learning detection models, annotation generation components, and user interface interfaces. This segmentation allows each component to be developed, tested, and optimized independently, reducing the overall implementation complexity. The modular architecture makes it easier to deploy and maintain the system while achieving high detection accuracy through specialized algorithms.

The system performs preliminary actions by pre-training machine learning models on extensive datasets of annotated medical images before actual diagnostic use. This preliminary training phase prepares the models to accurately detect pathologies, anatomies, and anomalies in new X-ray images. By completing the complex model training and validation beforehand, the system reduces the complexity of deployment and operation, allowing for straightforward integration into clinical workflows while maintaining high measurement precision.