Is there a place for ultrasound in neuraxial anesthesia?

Augmented reality in anesthesia, pain medicine and critical care: a narrative review

Augmented reality (AR) is the integration of computer-generated information with the user's environment in real time. AR is used in many industries, including healthcare, where it has gained significant popularity. Recent strides in hardware and software engineering have reduced the cost of AR, while significantly improving the experience for users and developers. One of the first applications of AR technology in perioperative medicine has been in the identification of anatomical structures for regional blocks and peripheral or central vascular access. AR has also been implemented in pediatric care to reduce periprocedural anxiety. In this narrative review, we summarize the current role of AR in anesthesiology, pain medicine, and critical care.

Ultrasound-Guided Neuraxial Anesthesia

PURPOSE OF REVIEW

There has been a recent surge of interest in clinical applications of ultrasound, which has revolutionized acute pain management. This review is to summarize the current status of ultrasound utilization in neuraxial anesthesia, the most common type of regional anesthesia.

RECENT FINDINGS

Ultrasound-assisted and ultrasound-guided neuraxial anesthesia has improved clinical accuracy and patient safety through landmark identification including proper vertebral level and midline, as well as via measurements on neuraxial space. Direct needle or catheter visualization during the entire procedure has not yet been achieved consistently. The recent introduction of ultrasound into neural anesthesia has clinical performance benefits and patient safety implications, with documented improvement on overall efficacy with higher first attempt success rate as well as less needle pass. More controlled studies are needed for the overall impact of ultrasonography in neuraxial anesthesia in obstetric and non-obstetric patients.

Development and Evaluation of an Augmented Reality Ultrasound Guidance System for Spinal Anesthesia: Preliminary Results

Applications of ultrasound guidance for epidural injections are hindered by poor needle and epidural space visualization. This work presents an augmented reality (AR) ultrasound guidance system that addresses challenges in both needle visualization during navigation and epidural space identification for needle positioning. In this system, (i) B-mode ultrasound and the needle are visualized in a 3-D AR environment for improved navigation, and (ii) A-mode ultrasound, obtained from a custom-made single-element transducer housed at the needle tip, is used to identify the epidural space for improved needle positioning. Performance of the system was evaluated against ultrasound-only guidance in a phantom study with novice operators and an expert anesthesiologist. The procedure success rate was higher with the AR system (100%) than ultrasound-only guidance (57%). The AR system has the potential to improve procedure outcomes in terms of success rate, time, needle path-length and usability.

SLIDE: automatic spine level identification system using a deep convolutional neural network

PURPOSE

Percutaneous spinal needle insertion procedures often require proper identification of the vertebral level to effectively and safely deliver analgesic agents. The current clinical method involves "blind" identification of the vertebral level through manual palpation of the spine, which has only 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion.

METHODS

A real-time system was developed to identify the vertebral level from a sequence of ultrasound images, following a clinical imaging protocol. The system uses a deep convolutional neural network (CNN) to classify transverse images of the lower spine. Several existing CNN architectures were implemented, utilizing transfer learning, and compared for adequacy in a real-time system. In the system, the CNN output is processed, using a novel state machine, to automatically identify vertebral levels as the transducer moves up the spine. Additionally, a graphical display was developed and integrated within 3D Slicer. Finally, an augmented reality display, projecting the level onto the patient's back, was also designed. A small feasibility study [Formula: see text] evaluated performance.

RESULTS

The proposed CNN successfully discriminates ultrasound images of the sacrum, intervertebral gaps, and vertebral bones, achieving 88% 20-fold cross-validation accuracy. Seventeen of 20 test ultrasound scans had successful identification of all vertebral levels, processed at real-time speed (40 frames/s).

CONCLUSION

A machine learning system is presented that successfully identifies lumbar vertebral levels. The small study on human subjects demonstrated real-time performance. A projection-based augmented reality display was used to show the vertebral level directly on the subject adjacent to the puncture site.