Applying artificial intelligence to the use of ultrasound as an educational tool: A focus on ultrasound-guided regional anesthesia

Anatomy has long been a keystone of medical and surgical education. Applied anatomical knowledge can explain the phenomenon of referred pain during myocardial infarction, guide surgical intervention, and inform the interpretation of diagnostic imaging.

Ultrasound (US) is a rapid, cheap, painless, non-invasive, and non-ionizing imaging modality.² Its use is spreading beyond radiology in secondary care, to multiple other in-hospital specialties, pre-hospital emergency medicine, primary care, and even global public health and disaster medicine.^3-5 It is a versatile technique, which can be used diagnostically and therapeutically.⁵ Modern cheaper and portable machines allow use at the point of care.^{6, 7} For these reasons, and due to its ability to reveal structures in their living context, it is being rapidly adopted within anatomy education.⁸

Though advances in US technology continue to provide enhanced image resolution,⁹ there can be identification errors of soft tissue structures.¹⁰ Progress in image acquisition and interpretation may not have matched developments in image generation.¹¹ Recent technological advances in artificial intelligence (AI) may enhance the skill of interpreting US images.¹² This article explores the role of AI in clinical anatomical education, through the medium of US. A specific example will be discussed, of which the authors have in-depth knowledge; the application to US-guided regional anesthesia (UGRA).

A sound understanding of the relevant anatomy is important to both skills, though available evidence does not always provide reassurance with respect to anesthesiologists' anatomical knowledge.³⁸ Despite endeavors to promote anatomical knowledge amongst those training in the specialty,³⁹ innovative educational approaches are needed when knowledge and performance are limited by the frailties discussed above.

One such educational innovation in anatomy learning may be through the use of AI. AI is “the ability of a computer programme to perform processes associated with human intelligence”.⁴⁰ It is a novel and rapidly evolving field that already surrounds us in everyday life - from guiding internet searches, and companies checking your credit rating, to self-driving cars. The terms machine learning and deep learning are often used interchangeably with AI: machine learning is a technique within the AI field, and deep learning is a subset of machine learning.

Machine learning uses algorithms, which are rule-based problem-solving instructions implemented by a computer,¹² to enable computers to perform specific tasks. The algorithm is exposed to training data, such as a bank of medical US images, and can improve task performance by making statistical correlations within the input training data and the desired output. In supervised machine learning, the algorithm is instructed what the desired output is (e.g., labeling sections of an US image as “nerve” or “muscle”). In unsupervised machine learning, the algorithm makes correlations autonomously by grouping regions of the data with common characteristics.

Deep learning is particularly suited to image analysis.¹² This approach uses artificial neurons, arranged in a network of layers, called a convolutional neural network.^{41, 42} Typically, each neuron is connected to the neurons of the layer below and above. There is an input layer, followed by multiple processing layers, and a final output layer. Neurons in each layer analyze the input data and draw out specific features—early layers identify simple features (e.g., straight lines), whilst deeper layers assess more complex features. From each layer of neurons, a map of the features is produced, resulting in the production of the overall analysis.⁴¹ By presenting the convolutional neural network with large volumes of input data, the network will learn to produce a fine-grained analysis of the original image.⁴³ For example, the input data may be a basic US image, and the desired outcome could be correct labeling of a nerve. When trained, the algorithm can subsequently generate the desired outcome on new, previously unseen, input data. This domain of AI is often referred to as computer vision as it utilizes deep learning to allow computers to interpret the visual world.

This is one example of a complex field. It is not yet clear when or how AI can be incorporated into all medical curricula, or even when the use of ultrasound will become routine, despite the increasing prevalence of the modality. However, it has been suggested that clinicians should be digitally literate and trained in the basics of AI.^45-47 It is anticipated that AI will become entwined with future medical practice in a way that may change professional identities,^{45, 46, 48} and there are considerations around AI that should be understood. For example, there are fears that AI could influence medical care in becoming reductive, with AI not necessarily able to recognize the nuances of complex tasks.^{48, 50} AI can be something of a black box, so how a given system makes its prediction is unknown,⁵¹ and there should, therefore, be caution around any biases inherent within the system.⁵²

Recent advances in AI technology have been noted within medical education, as it may support individualized learning with access to a large body of information.⁵² AI applications typically give rapid feedback,⁵³ and would not suffer from fatigue or distraction.⁴⁸ Proposed ideas include interactive training materials, such as a chat program,⁴⁹ intelligent tutoring,⁵³ and image bank algorithms.⁵⁴ AI could become involved in student assessments to provide impartiality.⁵¹ Such innovative ideas have been welcomed in the context of distance learning,⁵⁵ though are yet to be widely implemented within medical courses.⁴⁷

Artificial intelligence, primarily in the form of deep learning, has been rapidly adopted in medical imaging. Examples include chest x-rays and optical coherence tomography.^{56, 57} Within US, assessment of the musculoskeletal system is common,⁵⁸ such as assessing metacarpal cartilages⁵⁹ and determining the diameter of the median nerve, with a future target of diagnosing carpal tunnel syndrome.⁶⁰ Cardiology applications include assessing cardiac valves⁶¹ and detecting coronary artery abnormalities.⁶² One aim is to reduce repetitive tasks associated with analyzing echocardiograms.⁶³ Obstetrics is also recognized as an area of potential application,⁴⁰ with uses so far including obstetric measurements of the fetal head.⁶⁴ Automated deep vein thrombosis assessment has also been developed.⁶⁵ AI has been used in elucidating spinal anatomy where it can be challenging to accurately identify specific intervertebral spaces from surface landmarks, particularly when obesity complicates the surface anatomy.^{66, 67} This is of interest to anesthetists performing neuraxial anesthesia, as attempting a lumbar spinal injection superior to the cauda equina risks damage to the spinal cord.⁶⁷ Ultrasound can assist with intervertebral space identification, and several AI algorithms have been developed to begin to address this area.^{68, 69}

Anatomical knowledge underpins medical practice, although it is challenging to acquire this basic science due to the learning pressures applied by an expanding body of medical knowledge. Interest in using ultrasound to augment the teaching of this fundamental knowledge base is rapidly increasing, although barriers remain. The use of innovative AI technology alongside ultrasound offers the potential to supplement learning, with many potential applications. Here, we have provided one example, of ultrasound-guided regional anesthesia, where radiological and anatomical learning is integrated with the development of a clinical skill. Structured goals may be met by incorporating AI into US education and practice, with numerous future clinical applications.