Deep Learning-Based Kidney Stone Analysis within an IoT-Integrated Healthcare Ecosystem

Author(s)	Ms. Poonam Ghanghas
Country	India
Abstract	The sphere of healthcare is shifting with the development of AI and IoT, which bring efficient, effective, and precise diagnostics in real-time. The proposed study is an image classification model with deep learning to classify and detect kidney stones on stone surgery healthcare data, grounded on the CNNs. To give clinical trust, the system provides low-latency inference on IoT systems with the help of edge computing, and model interpretability with the help of explainable AI (XAI). The accuracy, precision, recall, and F1-score of the proposed model are high and proven by many experiments as in comparison with other machine learning solutions that are traditional. The data security, privacy, and interoperability issues discussed in the paper and provide a holistic structure of the AI-IoT based smart healthcare systems. The findings indicate the potential of AI IoT integration to improve the efficiency, patient outcome, and workflow of clinical practice.
Keywords	AI, IoT, Stone Surgery, Medical Image Classification, DL, CNN, Edge Computing, Explainable AI (XAI), Healthcare Informatics, Real-Time Diagnostics
Field	Computer Applications
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-16

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International Journal For Multidisciplinary Research