Computational surgery in the management of patients with abdominal aortic aneurysms: Opportunities, challenges, and future directions.

Computational surgery (CS) is an interdisciplinary field that uses mathematical models and algorithms to focus specifically on operative planning, simulation, and outcomes analysis to improve surgical care provision. As the digital revolution transforms the surgical work environment through broader adoption of artificial intelligence and machine learning, close collaboration between surgeons and computational scientists is not only unavoidable, but will become essential. In this review, the authors summarize the main advances, as well as ongoing challenges and prospects, that surround the implementation of CS techniques in vascular surgery, with a particular focus on the care of patients affected by abdominal aortic aneurysms (AAAs).

Registration Sanity Check for AR-guided Surgical Interventions: Experience From Head and Face Surgery.

Achieving and maintaining proper image registration accuracy is an open challenge of image-guided surgery. This work explores and assesses the efficacy of a registration sanity check method for augmented reality-guided navigation (AR-RSC), based on the visual inspection of virtual 3D models of landmarks. We analyze the AR-RSC sensitivity and specificity by recruiting 36 subjects to assess the registration accuracy of a set of 114 AR images generated from camera images acquired during an AR-guided orthognathic intervention.

Design, Fabrication, and Preliminary Validation of Patient-Specific Spine Section Phantoms for Use in Training Spine Surgeons Outside the Operating Room/Theatre.

Pedicle screw fixation (PSF) demands rigorous training to mitigate the risk of severe neurovascular complications arising from screw misplacement. This paper introduces a patient-specific phantom designed for PSF training, extending a portion of the learning process beyond the confines of the surgical room. Six phantoms of the thoracolumbar region were fabricated from radiological datasets, combining 3D printing and casting techniques.

Preliminary Results of Preoperative Planning Using 3D Printing and Augmented Reality in Cryotherapy Treatment of Giant Cell Tumor of Bone-CRIO2AR Project.

Giant Cell Tumor of Bone is a benign tumor with high local aggressive expansion, which, in rare cases, spreads metastasis. Surgical treatment, which often consists of wide curettage to reduce recurrence risk, can lower the quality of life for those affected. Along with aggressive surgery, adjuvant intraoperative techniques have been implemented such as PMMA and cryotherapy.

Analysis of the Robotic-Based In Situ Bioprinting Workflow for the Regeneration of Damaged Tissues through a Case Study.

This study aims to critically analyse the workflow of the in situ bioprinting procedure, presenting a simulated neurosurgical case study, based on a real traumatic event, for collecting quantitative data in support of this innovative approach. After a traumatic event involving the head, bone fragments may have to be removed and a replacement implant placed through a highly demanding surgical procedure in terms of surgeon dexterity. A promising alternative to the current surgical technique is the use of a robotic arm to deposit the biomaterials directly onto the damaged site of the patient following a planned curved surface, which can be designed pre-operatively.