Cranial Navigation, Navigation Software Cranial, Navigation Software Craniofacial, Cranial EM System, Automatic Registration iMRI

This document details Brainlab's advanced stereotaxic navigation systems, Cranial Navigation and Cranial EM System, which leverage optical and electromagnetic tracking technologies respectively, combined with patient-specific imaging data to guide complex cranial, craniofacial, and neurosurgical procedures. The systems integrate a novel AI/Machine Learning algorithm to improve pre-operative planning through abnormity detection and enhance intraoperative registration via landmark identification, aiming for greater surgical precision and workflow efficiency.

Brainlab AG's suite of stereotaxic instruments, encompassing Cranial Navigation, Cranial EM System, and the Automatic Registration iMRI accessory, functions as an image-guided planning and navigation platform for intricate cranial and neurosurgical interventions. Cranial Navigation employs optical tracking for procedures like cranial resections and craniofacial reconstructions, while the Cranial EM System utilizes electromagnetic tracking for neurosurgical applications such as tumor resections and intracranial catheter placement. Both systems integrate surgical instruments with virtual models derived from diverse patient imaging modalities (CT, MR, ultrasound), displayed on Image Guided Surgery (IGS) platforms. The Automatic Registration iMRI accessory aids the Cranial Navigation system by enabling automatic image registration using intraoperatively acquired MR images. A significant technological enhancement in both Cranial Navigation and Cranial EM System is the incorporation of a static, supervised learning-based Convolutional Neural Network (CNN). This AI/Machine Learning algorithm assists in identifying intra-cranial abnormalities for optimized view centering and offers pre-registration support by detecting anatomical landmarks from T1-Weighted Contrast-Enhanced MR images, with an atlas-based method as a fallback. These systems are indicated for various conditions demanding precise anatomical localization, including tumor and AVM resections, craniofacial defect reconstructions, and removal of foreign objects. Performance evaluations, including software validation, usability assessments for the newly added craniofacial indication, and system accuracy tests (demonstrating mean positional error ≤ 2 mm and mean angular error ≤ 2°), validated the device's capabilities without necessitating clinical trials.

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• Q1. How does the performance of the AI/ML-based landmark detection for pre-registration compare to traditional manual or atlas-based methods in terms of surgical time and registration accuracy in diverse patient populations?; What are the specific failure modes of the AI/ML algorithm for abnormity detection, and how robust is the fallback atlas-based system in ensuring continued navigational accuracy when such failures occur?; For the Craniofacial procedures, what is the system's quantified accuracy and reliability in complex reconstructions involving significant bone trauma or congenital defects, especially when pre-operative imaging quality varies?; How does the workflow integration of the Automatic Registration iMRI impact overall surgical procedure time and the ease of use for the surgical team during intraoperative MR updates?; What is the learning curve for surgeons and operating room staff to become proficient with the new AI/ML features and the different tracking technologies (optical vs. electromagnetic) offered across the system components?