Intellectual Property India Publishes Patent Application for 'A New Deep Neural Network Model For Wrist Fracture Detection And Localization' Filed by Kailasapathi T; Prasanna R; Santhosh S; Vishnu R; Sundar Raj. M; K Kaaviyakanth; Karuppanasamy. C; Dhivya Parameswari A; and Keerthana N

MUMBAI, India, Nov. 21 -- Intellectual Property India has published a patent application (202441037893 A) filed by Kailasapathi T; Prasanna R; Santhosh S; Vishnu R; Sundar Raj. M; K Kaaviyakanth; Karuppanasamy. C; Dhivya Parameswari A; and Keerthana N, Coimbatore,Tamil Nadu, on May 14, 2024, for 'a new deep neural network model for wrist fracture detection and localization.'

Inventor(s) include Kailasapathi T; Prasanna R; Santhosh S; Vishnu R; Sundar Raj. M; K Kaaviyakanth; Karuppanasamy. C; Dhivya Parameswari A; and Keerthana N.

The application for the patent was published on Nov. 21, under issue no. 47/2025.

According to the abstract released by the Intellectual Property India: "Our study presents a pioneering approach for the detection and localization of wrist fractures through the development of a specialized deep neural network model. By harnessing a diverse dataset comprising wrist X-ray images containing both fractured and non-fractured cases, our model achieves remarkable accuracy in identifying fractures and precisely localizing them within the wrist region. Through meticulous preprocessing techniques and the selection of a suitable deep learning architecture, such as a convolutional neural network (CNN), we ensure standardized image quality and optimize model performance. Furthermore, leveraging transfer learning and extensive training, our model exhibits robustness across varying datasets, demonstrating its effectiveness in real-world scenarios. In comparative analysis, our model surpasses existing methods by exhibiting superior performance in terms of sensitivity and specificity. This heightened accuracy, coupled with seamless integration into a user-friendly application, presents a significant advancement in the field of medical imaging. The deployment of our model in clinical settings streamlines the diagnostic process, facilitating prompt and accurate identification of wrist fractures. Ultimately, this innovation holds promise for enhancing patient outcomes and healthcare efficiency, signifying a substantial contribution to the realm of diagnostic imaging in orthopedic care."

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