Medical Imaging and AI: Advancements in Bone Age Prediction

Abstract

In the era of growing technology were predict every possible methods are done through computer software. AI employs algorithms designed for medical predictions making process fast and accurate, as in case of bone age assessment, the crucial in paediatric radiology for diagnosing growth disorders, has been revolutionized. Traditional methods like the Greulich-Pyle (GP) atlas and Tanner-Whitehouse (TW) system are laborious and prone to inter-observer discrepancies. On the other hand wrong and weak predictions leads to misappropriations and chaos. Bone age determination plays an essential role in diagnosing and managing paediatric growth disorders, such as short stature, precocious puberty, and growth hormone deficiency. The conventional methods, primarily the GP and TW atlas system, involve comparing radiographs with predefined standards. These methods are limited by subjective interpretations, leading to variability among radiologists.

In AI, specifically deep learning models, has emerged as a transformative tool in medical imaging. Algorithms namely as ConvNets (CNNs), Artificial Intelligence Networks (ANNs), Recurrent Neural Networks (RNN) are proficient in analyzing radiographic data with minimal human intervention. This development aligns with the demand for automated solutions to reduce diagnostic workloads and improve accuracy. The fusion of AI and radiology aims to bridge the gap between human expertise and computational precision.

This paper investigates the trajectory of AI applications in bone age detection, tracing the evolution from traditional approaches to cutting-edge methodologies.

This paper examines the transformative impact of AI-driven approaches, particularly DLL and ML algorithms, which offer automated, objective, and rapid bone age estimations from hand and wrist radiographs. We explore methodological innovations, benchmark AI performance against established techniques, discuss contemporary clinical applications, and analyse real-world implementations. Our findings underscore AI's potential to enhance diagnostic precision, address scalability challenges, and ultimately improve patient care in bone age analysis.