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<p>Advanced precision oncology has the potential to revolutionize the current infrastructure of precision oncology, especially in cancer diagnosis and ongoing monitoring, owing to the strong development and advancement of the Internet of Medical Things (IoMT) and Blockchain Distributed Ledger Technology (BDLT). In order to improve cancer diagnosis accuracy and real-time patient monitoring, this paper introduces a novel Blockchain-enabled secure IoMT architecture that incorporates state-of-the-art multi-modal data fusion algorithms, such as weighted fusion. A comprehensive picture of patient health is made possible by this proposed architecture, which presents a novel mechanism for the safe, dynamic aggregation of various datasets, including as genetic portfolios, medical imaging, and wearable sensory-enabled data, as we assess the existing solutions. However, a BDLT-enabled immutable distributed ledger that uses cutting-edge encryption techniques to protect patient privacy while guaranteeing data immutability, decentralized access controls, fine-grained data availability, and traceability are among the main goals. This proposed architecture's unique context-aware data fusion algorithm greatly outperforms traditional techniques, achieving a diagnostic accuracy of 97.10%, precision of 98.25%, F1-scroe of 0.97, and sensitivity of 96.85%. Furthermore, the incorporation of BDLT enhanced security and privacy protection by eliminating single points of failure and attaining 100% immutability. When handling, organizing, and processing dynamic data, a latency of less than 250&#xa0;ms is calculated. Through simulations using real-world case studies, the proposed work is tested, enhancing the system's reliability while also showcasing its scalability, energy efficiency, and robustness. Based on the examination of the simulation findings, we are able to reach parameters such as data integrity, throughput exceeding 300 transactions per second, and resource utilization efficiency optimized up to 85% in comparison to other state-of-the-art methodologies. It guarantees dependable functioning even with fluctuating computational loads. The findings demonstrate its ability to provide precise, secure, and useful insights instantly, revolutionizing real-time monitoring and cancer diagnosis.</p>

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Blockchain-enabled secure Internet of Medical Things (IoMT) architecture for multi-modal data fusion in precision cancer diagnosis and continuous monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月16日

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  • Abdullah Ayub Khan,
  • Abdul Khalique Shaikh,
  • Roobaea Alroobaea,
  • Abdullah M. Baqasah,
  • Majed Alsafyani,
  • Hamed Alsufyani,
  • Asif Ali Laghari

摘要

Advanced precision oncology has the potential to revolutionize the current infrastructure of precision oncology, especially in cancer diagnosis and ongoing monitoring, owing to the strong development and advancement of the Internet of Medical Things (IoMT) and Blockchain Distributed Ledger Technology (BDLT). In order to improve cancer diagnosis accuracy and real-time patient monitoring, this paper introduces a novel Blockchain-enabled secure IoMT architecture that incorporates state-of-the-art multi-modal data fusion algorithms, such as weighted fusion. A comprehensive picture of patient health is made possible by this proposed architecture, which presents a novel mechanism for the safe, dynamic aggregation of various datasets, including as genetic portfolios, medical imaging, and wearable sensory-enabled data, as we assess the existing solutions. However, a BDLT-enabled immutable distributed ledger that uses cutting-edge encryption techniques to protect patient privacy while guaranteeing data immutability, decentralized access controls, fine-grained data availability, and traceability are among the main goals. This proposed architecture's unique context-aware data fusion algorithm greatly outperforms traditional techniques, achieving a diagnostic accuracy of 97.10%, precision of 98.25%, F1-scroe of 0.97, and sensitivity of 96.85%. Furthermore, the incorporation of BDLT enhanced security and privacy protection by eliminating single points of failure and attaining 100% immutability. When handling, organizing, and processing dynamic data, a latency of less than 250 ms is calculated. Through simulations using real-world case studies, the proposed work is tested, enhancing the system's reliability while also showcasing its scalability, energy efficiency, and robustness. Based on the examination of the simulation findings, we are able to reach parameters such as data integrity, throughput exceeding 300 transactions per second, and resource utilization efficiency optimized up to 85% in comparison to other state-of-the-art methodologies. It guarantees dependable functioning even with fluctuating computational loads. The findings demonstrate its ability to provide precise, secure, and useful insights instantly, revolutionizing real-time monitoring and cancer diagnosis.