Computer Vision and Machine-Learning Techniques for Automatic 3D Virtual Images Overlapping During Augmented Reality Guided Robotic Partial Nephrectomy

Abstract

Objectives The research's purpose is to develop a software that automatically integrates and overlay 3D virtual models of kidneys harboring renal masses into the Da Vinci robotic console, assisting surgeon during the intervention. Introduction Precision medicine, especially in the field of minimally-invasive partial nephrectomy, aims to use 3D virtual models as a guidance for augmented reality robotic procedures. However, the co-registration process of the virtual images over the real operative field is performed manually. Methods In this prospective study, two strategies for the automatic overlapping of the model over the real kidney were explored: the computer vision technology, leveraging the super-enhancement of the kidney allowed by the intraoperative injection of Indocyanine green for superimposition and the convolutional neural network technology, based on the processing of live images from the endoscope, after a training of the software on frames from prerecorded videos of the same surgery. The work-team, comprising a bioengineer, a software-developer and a surgeon, collaborated to create hyper-accuracy 3D models for automatic 3D-AR-guided RAPN. For each patient, demographic and clinical data were collected. Results Two groups (group A for the first technology with 12 patients and group B for the second technology with 8 patients) were defined. They showed comparable preoperative and post-operative characteristics. Concerning the first technology the average co-registration time was 7 (3–11) seconds while in the case of the second technology 11 (6–13) seconds. No major intraoperative or postoperative complications were recorded. There were no differences in terms of functional outcomes between the groups at every time-point considered. Conclusion The first technology allowed a successful anchoring of the 3D model to the kidney, despite minimal manual refinements. The second technology improved kidney automatic detection without relying on indocyanine injection, resulting in better organ boundaries identification during tests. Further studies are needed to confirm this preliminary evidence.

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“…The 3D model was securely attached to the kidney with minimal manual adjustments. A novel method was employed to enhance the detection of kidneys without the need for indocyanine injection, resulting in improved recognition of organ boundaries during tests [38].…”

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“…The precise diagnostics, tailored treatment protocols, and improved patient care facilitated by these technologies could usher in a new era of urology defined by enhanced precision and individualized care [25]. Harnessing AI and AR for the benefit of patients means not only adopting these technologies but also training clinicians to use them effectively [26]. Education and training will be critical to maximize the advantages that AI and AR offer, ensuring that clinicians are equipped to incorporate these tools into practice.…”