Abstract
The augmented reality (AR) is the latest technology in laparoscopy and minimally invasive surgery (MIS). This technology decreases post-operative pain, recovery time, difficulty rate, and infections. The main limitations of AR systems are system accuracy, the depth perception of organs, and real-time laparoscopy view. The aim of this work is to define the required components to implement an efficient AR visualization system. This work introduces Data, Visualization techniques, and View (DVV) classification system. The components of DVV should be considered and used as validation criteria for introducing any AR visualization system into the operating room. Well-designed DVV system can help the end user and surgeons with a clear view of anatomical details during abdominal surgery. This study validates the DVV taxonomy and considers system comparison, completeness, and acceptance as the primary criteria upon which the proposed DVV classification is based upon. This work also introduces a framework in which AR systems can be discussed, analyzed, validated and evaluated. State_of_the_art solutions are classified, evaluated, validated, and verified to describe how they worked in the domain of AR visualizing laparoscopy during abdominal surgery in the operating room. Finally, this paper states how the proposed system improves the system limitations.
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Bikram, K.R., Al-Dala’in, T., Alkhawaldeh, R.S., AlSallami, N., Al-Jerew, O., Ahmed, S. (2023). Taxonomy of AR to Visualize Laparoscopy During Abdominal Surgery. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_25
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DOI: https://doi.org/10.1007/978-3-031-35308-6_25
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