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Personalized Surgical Planning in Liver Surgery Using Virtual 3D-Models

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Computational Intelligence in Pattern Recognition (CIPR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 725))

Abstract

Liver surgery requires personalized preoperative planning, especially in case of major liver resections. In this chapter, we explore how AI-based technologies can help hepatopancreatobiliary surgeons to plan the best surgical procedure for each patient. In particular, we analyze the role of virtual 3D-models in surgical planning, describing the current practical applications, future perspectives, and challenges in this field. Virtual 3D-models allow us to establish the correct surgical indication for each patient, plan the amount of liver to be resected, and perform a tailored surgery.

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Author information

Authors and Affiliations

  1. Azienda Ospedaliero Universitaria di Sassari, Sassari, Italy

    Teresa Perra & Alberto Porcu

Authors
  1. Teresa Perra
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  2. Alberto Porcu
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Corresponding author

Correspondence to Teresa Perra .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  2. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University (MSCB), Baripada, Odisha, India

    Janmenjoy Nayak

  3. Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, Odisha, India

    Bighnaraj Naik

  4. Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India

    S. Vimal

  5. Department of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Perra, T., Porcu, A. (2023). Personalized Surgical Planning in Liver Surgery Using Virtual 3D-Models. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_56

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