MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202621056173 A) filed by Dr. Surabhi L. Kapgate; Stavan L. Kapgate; Dr. Akanksha Kumar; Dr. Johar Rajvinder Singh; Dr. Shreyas Gedam; Dr. Anjali Nandanwar; Dr. Prajakta Joshi; Dr. Sejul Jaiswal; and Dr. Sneha Bhaisare on May 03, 2026, for Novel Method For Ai-Based Automated Skeletal Maturity Assessment Using Mp3 Radiographs.
Inventors include Dr. Surabhi L. Kapgate; Stavan L. Kapgate; Dr. Akanksha Kumar; Dr. Johar Rajvinder Singh; Dr. Shreyas Gedam; Dr. Anjali Nandanwar; Dr. Prajakta Joshi; Dr. Sejul Jaiswal; and Dr. Sneha Bhaisare.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: [05] This study presents a novel artificial intelligence (AI)-based framework for automated skeletal maturity assessment using MP3 radiographs, combining morphometric analysis with machine learning and deep learning techniques. Conventional manual staging methods are often subjective and time-consuming; therefore, a data-driven system was developed to provide consistent and reliable predictions. A total of 1287 radiographs from patients aged 8–16 years were used, with 1029 samples for training and 258 for testing. Key anatomical features, including horizontal, vertical, angular measurements and epiphyseal contour, were extracted and used to train five AI models: Random Forest, KNN, SVM, ResNet, and EfficientNet-B7. Model performance was evaluated using accuracy, precision, recall, and F1-score, and compared with manual staging. The results demonstrate that AI models can accurately and reproducibly assess skeletal maturity, with deep learning models showing superior performance. Overall, the study highlights a significant advancement in orthodontic diagnostics by enabling efficient, objective, and automated MP3 staging. Accompanied Drawing [FIG. 1] [FIG. 2] [FIG. 3] [FIG. 4] [FIG. 5] [FIG. 6] [FIG. 7] [FIG. 8] [FIG. 9]
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