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Review on Mechanobiological Analysis and Computational Study of Human Tissue (Soft and Hard) Using Machine Learning Techniques: A Mechanical Perspective

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Abstract

This article reviewed current advancements in mechanobiology (MB) and its applications to investigate human tissue (soft and hard) using machine learning (ML) techniques. The study explores the use of ML for diagnosing tissue disorders and injuries and highlights the challenges and limitations of applying ML to MB. In addition, a detailed assessment of the many distinct experimental methodologies, computational studies and computer models may be utilized for MB analysis. The initial section introduces MB, their generation-wise developments, and the broad classification of human tissues and their disorders. This study also focussed on the computational studies of the different numerical models of human tissues. The final part examined various studies to classify and early detection of human tissue disorders with the help of ML techniques. Overall, the paper offers insights into the potential of ML for understanding human tissue’s complex behaviour and advancing the biomechanics field.

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  1. Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India

    Ashish Kumar Meher, Erukala Kalyan Kumar, Ankit Gangwar & Subrata Kumar Panda

  2. UCRD, Chandigarh University, NH-95 Chandigarh-Ludhiana Highway, Mohali, Punjab, India

    Subrata Kumar Panda

  3. Department of Food Processing Engineering, National Institute of Technology, Rourkela, 769008, India

    Rama Chandra Pradhan

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Meher, A.K., Kumar, E.K., Gangwar, A. et al. Review on Mechanobiological Analysis and Computational Study of Human Tissue (Soft and Hard) Using Machine Learning Techniques: A Mechanical Perspective. Arch Computat Methods Eng 31, 957–972 (2024). https://doi.org/10.1007/s11831-023-10003-4

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