Spine Planning (2.0), Elements Spine Planning, Elements Planning Spine

Spine Planning 2.0 is a software solution intended for pre- and intraoperative planning of spinal procedures, utilizing digital patient images and AI-driven algorithms for implant placement. Its methodology incorporates Convolutional Neural Networks for automated vertebrae labeling and landmark detection to assist surgeons in planning screw and rod implantation across various spinal regions, with its performance validated through comprehensive testing.

Spine Planning 2.0 by Brainlab AG is a software tool intended for the pre-operative and intra-operative planning of both open and minimally invasive spinal procedures. It enables clinicians to display digital patient images from various modalities (CT, Cone Beam CT, MR, X-ray) and facilitates the measurement and planning for spinal implants like screws and rods; the software can be used in conjunction with spinal navigation systems during surgery or as a standalone pre-operative tool to prepare for the surgical intervention. The system offers features such as automated labeling of vertebrae (cervical, thoracic, lumbar, sacral) with user override capabilities, and proposals for screw and rod implants. It leverages AI/ML algorithms, specifically Convolutional Neural Networks developed through supervised learning, to detect landmarks on 2D images for vertebral labeling and measurement, and to identify vertebrae on Digitally Reconstructed Radiographs for atlas registration. Enhanced features include adjustments for anatomical variations, AI-generated spinal parameter proposals on X-rays, manual measurement tools, and expanded automatic screw proposals to additional spinal regions (cervical, sacral, iliac) using a new algorithm with options to modify screw types; validation of these functionalities was established through software verification, AI/ML landmark detection assessment, screw proposal algorithm evaluation, and usability testing which confirmed the device performs as intended without critical use-related issues.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the AI/ML algorithm's performance in landmark detection and vertebrae labeling vary across different image modalities (CT, CBCT, MR, X-ray) and in patients with significant spinal deformities or prior hardware?; What is the quantitative accuracy and variability of the automated screw and rod placement proposals compared to experienced surgeons' manual planning, particularly in complex cervical and sacral cases?; Can the software integrate or learn from post-operative outcomes to refine its AI/ML algorithms and improve future planning suggestions for implant sizing and trajectory?; How does the system handle inconsistencies or poor image quality, and what are the fallback mechanisms or user alerts when AI/ML confidence is low for automated proposals?; What specific training data (diversity, size, annotation quality) was used for the supervised learning of the Convolutional Neural Network, and how does this impact its generalizability to diverse patient populations and rare pathologies?