ScanNav Anatomy Peripheral Nerve Block

The ScanNav Anatomy Peripheral Nerve Block is a software accessory designed to aid clinicians by using deep learning artificial intelligence to analyze live ultrasound images and highlight key anatomical structures relevant to regional anesthesia. Its purpose is to enhance anatomical identification accuracy for multiple nerve block procedures, including the femoral block, prior to needle insertion, thereby supporting safer and more effective anesthetic interventions.

ScanNav Anatomy Peripheral Nerve Block is a software as a medical device (SaMD) developed to assist qualified healthcare professionals in identifying and labeling anatomical structures within live ultrasound images. This process is intended for use prior to needle insertion during ultrasound-guided regional anesthesia procedures on adult patients (18 years and older). The system supports visualization for several anatomical regions, including the axillary, interscalene, and supraclavicular levels of the brachial plexus, erector spinae plane, popliteal sciatic nerve, rectus sheath plane, sub-sartorial femoral triangle/adductor canal, superior trunk of the brachial plexus, longitudinal suprainguinal fascia iliaca plane, and the newly added femoral block. The device operates by receiving real-time ultrasound images from a compatible general-purpose diagnostic ultrasound system. These images are processed using deep learning artificial intelligence algorithms, which then highlight relevant anatomical structures. The augmented image, showing the highlighted anatomy, is displayed on a dedicated general-purpose panel PC provided with the device, while the original ultrasound machine's display remains unaltered. Non-clinical testing for the addition of the femoral nerve block demonstrated strong performance, with a 1.1% false positive rate for mis-identification of safety-critical structures, a 96.7% accuracy rate for correct highlighting, and a 2.2% false negative rate for non-identification, all meeting predefined acceptance criteria. Software verification and validation tests further confirmed the system's integrity and functionality.

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 deep learning algorithm's performance for the Femoral block compare with its performance for the previously supported anatomical regions, particularly in terms of accuracy, false positive, and false negative rates across diverse patient populations (e.g., varying BMI, age, or anatomical anomalies)?; What specific image quality parameters from the host ultrasound system are critical for the optimal functioning of the ScanNav Anatomy Peripheral Nerve Block, and how does the system alert users to suboptimal image input?; Could the system be adapted or expanded in the future to provide guidance during needle insertion, rather than only prior to it, and what would be the key technological and validation challenges?; What were the specific criteria for defining 'safety-critical anatomical structures' during the validation of the Femoral block and other supported procedures, and how were these definitions established?; Are there any known limitations regarding the types of compatible general-purpose ultrasound systems, or specific ultrasound probe characteristics (e.g., frequency, footprint) that might affect the device's performance?