Artificial Intelligence and Robotics in Regional Anaesthesia: Do they have a role?

摘要

“I expect it [Artificial Intelligence - AI] to play a foundational role in pretty much every aspect of our lives” Sundar Pichai, CEO Google, 2021 We are living in the fourth industrial revolution, characterised by the dominance of computers and technological advances including artificial intelligence (AI) and robotics [1]. Such developments are likely to have a profound impact on humanity, reforming our work environment and daily life. Artificial intelligence refers to the simulation of human intelligence in machines [2]. Computers can be programmed to imitate neuronal activity and appear to think like humans or mimic their actions, with an attempt to find solutions to complex problems in a variety of scientific domains, including medicine. Such programs are able to make calculations with higher accuracy and speed, compared to humans, using large volumes of data. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning, planning, programming, creativity and problem–solving. AI renders machines capable of interpreting their environment to act for the purpose of achieving a specific goal. Importantly, AI systems can be capable of adapting their behaviour (up to a point) to solve problems with relative autonomy, via analysis of previous actions and outcomes. Robots are machines with an abundance of sensors, that are properly designed to perceive the outer environment and to interact with it, finally executing a series of programmed actions [3]. Artificial intelligence and robotics may provide extremely powerful advances with multiple applications in various medical fields. The emphasis, for the moment, is on surgery and radiology, and with the first related literature reports having appeared at the end of the previous century [3]. In anaesthesia, however, their development was slower, and the first attempt in automation was the introduction of computerised pharmacokinetic model – driven continuous infusion pumps. These attempts resulted in the first target–controlled infusion (TCI) device for administering propofol. More recently, research has demonstrated that AI may also be useful in Regional Anaesthesia (RA), by identifying key anatomical features and by facilitating Ultrasound–Guided Regional Anaesthesia (UGRA) [2, 4]. The initial challenge in UGRA is an understading of the sono–anatomy, to acquire and interpret ultrasound images [2, 5]. This remains an under–explored area of research and is known to be imperfect amongst anaesthesiologists. While improvements in ultrasound technology provide greater image resolution, developments in AI can be helpful and may be employed to support the application of this technology to identify the salient sono–anatomy. In this regard, a field of AI called “computer vision” has received particular attention as it enables computers to interpret the visual world, most commonly using a technique called deep learning. Artificial intelligence systems in RA are emerging [2, 6] Among them, the development of a deep learning–based system called ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) has recently received attention in literature [2–4]. This system uses deep learning to identify anatomical structures on B-mode ultrasound and applies a colour overlay to those structures in real time (as summarised below taken from Bowness et al, 2021) [5]. The labelling is achieved using a convolutional neural network, based on the U-Net architecture. Data (greyscale ultrasound images) that are entered, pass through a series of computational (neural) layers, with each layer extracting specific information. In the initial “contracting” path, each of the down–sampling layers applies a series of convolutional filters to extract image features, and then halves the resolution for the next layer. By this down–sampling, the AI machine can understand better what is present in the image, but it loses information about where some features a