Automated Bone Age Determination Using Expert Systems: A New Approach

This paper proposes an expert system approach for automated bone age determination, utilizing rule-based and knowledge-based programming to mimic human expert decision-making. The researchers collected a large dataset of bone age images and corresponding clinical data, building an expert system to assist doctors in determining a patient's bone age.

The system incorporates image processing and pattern recognition techniques to analyze bone age images. Initial pre-processing steps, such as resizing and contrast adjustments, ensure image quality. The system then extracts features like bone shape and density, comparing them to a known bone age database to determine the closest match.

Beyond image features, the expert system also considers patient clinical data like gender, height, and weight, further refining bone age estimations.

Finally, the system was validated by comparing its bone age assessments to those made by expert physicians, evaluating its accuracy and reliability. The researchers also compared the system to traditional manual bone age assessment methods, highlighting its efficiency and advantages.

Overall, this paper demonstrates the use of expert systems and image processing techniques to automate bone age determination, offering doctors a fast, accurate, and reliable tool. This method has the potential for widespread clinical application, enhancing efficiency and precision in bone age assessment.