The ITAG Spinal Simulator (ISS): A New Simulator for Difficult Lumbar Punctures.

Lumbar punctures present a specific challenge in various medical specialties; appropriate simulators need to take into account the specific technical difficulties related to a realistic patient population, but currently fail to address the variety of anatomical differences seen in practice. We interviewed several leaders in the field of anesthesiology with extensive experience in lumbar puncture procedures, subsequently developing a more realistic training simulator. This novel simulator was built using silicone-based materials and advanced 3D-printing techniques, specifically tailored to be capable of mimicking a variety of patient populations without having to dispose of essential components after each use. Two Anesthesiologists with at least 20 years of experience were asked to perform several spinal tap procedures. Following testing, experts rated the simulator based on its procedural realism, usefulness in improving skill set, and overall simulation efficacy.The gathered validation outcomes based on the questionnaire evaluations completed by experts show excellent preliminary results, with an overall mean score of 4.8 out of 5 (96%). These preliminary results highlight the potential for the simulator's application as a tool to improve medical simulation education and future patient outcomes.

Utilizing Deep Learning to Identify an Ultrasound-guided Nerve Block Target Zone.

Ultrasound guided nerve blocks are increasingly being used in perioperative care as a means of safely delivering analgesia. Unfortunately, identifying nerves in ultrasound images presents a challenging task for novice anesthesiologists. Drawing from online resources, here we attempted to address this issue by developing a deep learning algorithm capable of automatically identifying the transversus abdominis plane region in ultrasound images. Training of our dataset was done using the U-Net architecture and artificial augmentation was done to optimize our training dataset. The Dice score coefficient was used to evaluate our model, with further evaluation against a test set composed of manually drawn labels from a pool of (n=10) expert anesthesiologists.Across all labelers the model achieved a global Dice score of 73.31% over the entire test set. These preliminary results highlight the potential effectiveness of this model as a future ultrasound decision support system in the field of anesthesia.