Intellectual Property India Publishes Patent Application for 'Multi Chronic Kidney Disease Using Deep Learning' Filed by P A Nageswara Rao; Ommi Dinesh; Rayudu Navya Keerthana; Pondala Maniraj; Arji Mahesh; Raju Egala; and Venkatesh Seerapu

MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641048987 A) filed by P A Nageswara Rao; Ommi Dinesh; Rayudu Navya Keerthana; Pondala Maniraj; Arji Mahesh; Raju Egala; and Venkatesh Seerapu, Visakhapatnam, Andhra Pradesh, on April 17, for 'multi chronic kidney disease using deep learning.'

Inventor(s) include P A Nageswara Rao; P A Nageswara Rao; Ommi Dinesh; Rayudu Navya Keerthana; Pondala Maniraj; Arji Mahesh; Raju Egala; and Venkatesh Seerapu.

The application for the patent was published on May 1, under issue no. 18/2026.

According to the abstract released by the Intellectual Property India: "Chronic kidney disease (CKD) is a major global health concern that often occurs alongside multiple chronic conditions such as diabetes, hypertension, and cardiovascular diseases. These coexisting conditions complicate early diagnosis and increase the risk of disease progression and adverse outcomes. Traditional diagnostic approaches may not effectively capture the complex relationships among multiple diseases. This study focuses on the application of deep learning techniques to improve the prediction and early detection of CKD using multi-morbidity data. A comprehensive dataset consisting of patient demographics, clinical records, laboratory results, and medical histories was utilized. Data preprocessing techniques such as handling missing values, normalization, and feature encoding were applied to ensure data quality. Various deep learning models, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were developed and evaluated using performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The experimental results demonstrate that deep learning models outperform traditional machine learning methods in predicting CKD, achieving high accuracy and improved reliability. These models effectively identify patterns and interactions among multiple chronic conditions, enabling better risk assessment and early diagnosis. The proposed approach provides a promising solution for assisting healthcare professionals in timely decision-making, personalized treatment planning, and improving patient outcomes."

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