A novel recognition of artificial intelligence in regional anaesthesia

Abstract

Mechanised analysis of algorithms for problem solving, object recognition and decision-making assistance defines the general concept underlining artificial intelligence (AI).[1] The former presents notable applications in perioperative medicine across diverse situations like depth of anaesthesia monitoring, closed-loop anaesthesia delivery, risk-predictive models, pain-management through objective analgo-scoring, operating-room planning logistics and, guidance of perioperative ultrasonography. The recent Food and Drug Administration (FDA) approval[2] of the Scan Nav™ Anatomy Peripheral Nerve Block (PNB) system (Intelligent Ultrasound Group, Cardiff, United Kingdom)[3] only emerges as a novel recognition of the potential of AI in regional anaesthesia. Worthwhile to mention, Hemmerling et al.[4] described an encouraging seminal use of the robotic ultrasound-guided nerve block system or the Magellan system, almost a decade ago. Employing the same system, Morse et al.[5] went on to compare the performance parameters (learning curve and success rates) and subjective performance variability of manual versus robot-assisted ultrasonographic nerve blocks in a phantom model. The authors found that assistance of nerve blocks, enabled significant faster needle guidance (1.8 [1.6] seconds vs. 0.3 [0.3] seconds; P = 0.007), than for manual blocks. Inter-subject performance variability was also found to be considerably attenuated. Specific to the context of the recently FDA approved software medical device, ScanNav™ Anatomy PNB system uses deep-learning AI technology. During performance of a peripheral nerve block the ScanNav™ system identifies and grades a live ultrasound image using this technology. A distinctive colour overlay of the key sono-anatomical structures during a live ultrasound scan is a characteristic valuable attribute of the system.[3] This enhances standardisation and accuracy of the ultrasound image interpretation. This device colour highlights anatomy of 10 vital regions relevant to ultrasound-guided regional anaesthesia namely Interscalene, Supraclavicular, Axillary and Superior Trunk Brachial plexus of the upper limb; Erector Spinae Plane, Rectus Sheath and Supra-inguinal Fascia Iliaca sheath of truncal structures; and Femoral Nerve, Adductor canal and Popliteal nerve sheath of the lower limb. Furthermore, a 3-dimension inbuilt reference material provides a review of the relevant anatomy, probe-positioning technique; and demonstrates a virtual scan of the selected anatomical region. The system enables adjustments in the intensity of coloured highlighting according to user preference and allows for viewing the unmodified and highlighted images side-by-side for better comprehension of the sono-anatomy. The use of ScanNav™ system thereby facilitates as a labelling device alongside real-time anatomy visualisation during regional anaesthesia. The system also has inbuilt virtual scans that assist the clinician functioning as a 3D reference material of the relevant PNB anatomy scanned. Notably so, the software is compatible with the general-purpose ultrasound systems in addition to being a stand-alone device. Talking of the literature relevant to the ScanNav™ Anatomy PNB system, Bowness et al.[6] compared 240 ultrasound scans performed across 15 expert versus non-expert regional anaesthesia providers on evaluating its' role in promoting the uptake and generalisability of ultrasound-guided regional anaesthesia. The authors outlined that non-experts were more likely to submit positive feedback and less likely to submit negative reviews than the experts (P = 0.001). Positive feedbacks were obtained on the role of the device in training (37/60, 61.7%) and utility for teaching (30/60, 50.0%). Meanwhile the non-experts appreciated the anatomic structure identification and learning, as the contextual benefits, some nonexperts and experts thought the device could decrease the scanning confidence. Additionally, expert providers reported a potentially increased, yet insignificant, risk in 12/254 (4.7%) vs. 8/254 (3.1%, P = 0.362) scans for needle trauma to “safety” structures. The discussion elaborates the tremendous opportunity AI has to offer in guiding regional anaesthesia, awaiting prospective validation outside the core 10 block areas. The utility of this device can be further extended to sympathetic blocks like the stellate-ganglion, celiac-plexus and other blocks for treatment of chronic pain syndromes like complex regional pain syndrome. The times might not be distant when perioperative physicians have routine access to ultrasound machines integrated with AI-assisted colour reframing for anatomical landmark “coding” side by side with the present B-mode, M-mode and colour/pulse Doppler profiling.