Orthopaedics Online
30 May 2024

Integrating artificial intelligence into trauma and orthopaedics: History, current state of AI in T&O and future perspectives

By Andrew Coppolaa, Caroline Hingb, and Vipin Asopaa,b

a South West London Elective Orthopaedic Centre, Epsom and St. Helier University Hospitals NHS Trust
b St George's University Hospital, London.

 

Introduction

Artificial Intelligence (AI) aims to develop systems capable of executing tasks traditionally associated with human intelligence. Since its inception in the mid-20th century, AI has undergone significant evolution, transitioning from basic algorithms to complex artificial neural networks. The Dartmouth Conference of 1956 is widely recognised as a seminal event that marked the beginning of organised research efforts in AI1. Recent advancements in AI have been accelerated by innovations in cloud computing, which provide scalable computational resources, and improvements in processor technology, enhancing the speed and efficiency of complex algorithms and real-time analysis2,3.

In the field of orthopaedics, AI demonstrates significant potential by leveraging extensive visual data, including X-rays, MRI, and CT scans, alongside registry data from repositories such as the National Joint Registry and the American Joint Replacement Registry. This strategic integration of AI presents a significant opportunity to transform orthopaedic practice through enhanced diagnostic accuracy, refined surgical planning processes, and overall improvements in patient outcomes4-6. Moreover, AI facilitates the development of patient specific predictive models, representing a paradigm shift toward personalised medicine7,8. These insights underscore AI's emergent role as a transformative tool set to reshape orthopaedic care.

The purpose of this article is to discuss the history of AI, orthopaedic applications and future perspectives. The review will explore the implications of utilising extensive data sets, regulatory challenges, and the necessity for dynamic data solutions and continuous learning within the field.

History of artificial intelligence

The conceptual foundation of AI began in 1943 with McCulloch and Pitts introducing the idea of artificial neurons, inspired by earlier theoretical work by Alan Turing9,10. The field was formally established in 1956 at the Dartmouth Conference, recognised as the starting point for organised AI research1. Throughout the late 1950s, significant advancements such as the development of the ADALINE and MADALINE neural networks by Widrow and Hoff in 1959 laid the groundwork for later innovations in machine learning11. The introduction of the Von Neumann architecture in the 1960s dominated computing approaches and temporarily overshadowed neural network research12.

The 1970s marked the first 'AI winter', a period of reduced funding and interest due to unrealistically high expectations and subsequent disillusionment. Despite this, research continued, leading to key developments like the backpropagation technique in 1975, which improved the training efficiency of multi-layer neural networks13. AI experienced a resurgence in the 1980s with significant research contributions and commercial interest, rejuvenating the field. This period saw the development of cooperative-competitive neural networks and the popularisation of LISP machines, although another AI winter occurred late in the decade due to market saturation and a shift back to cheaper personal computing options14.

The 1990s ushered in a new era of practical AI applications, highlighted by IBM's Deep Blue defeating world chess champion Garry Kasparov in 199715. The following decade saw rapid advancements in GPU technology, enhancing the training of neural networks, and the introduction of user-interactive AI applications like Microsoft's Kinect and Apple’s Siri, broadening the public’s interaction with AI technologies. The 2010s were marked by breakthroughs in deep learning, notably through Geoffrey Hinton's work, which significantly advanced image and text analysis capabilities. His contribution to the development of convolutional neural networks culminated in winning the prestigious Turing award in 2018 (figure 1)16,17.

Figure 1.jpg 1

Today, AI is a diverse field that spans numerical analysis, text analysis through large language models, and image analysis, with applications affecting nearly every sector of society.

Current state of AI in trauma and orthopaedics

Recent advancements in AI in the field of trauma and orthopaedics have been propelled by breakthroughs in cloud computing and enhanced processor capabilities. These innovations enable scalable, virtual computing resources that significantly improve the processing speed and efficiency of complex algorithms, facilitating near real-time analysis of data. Although current clinical applications of AI in orthopaedics are limited, the extensive and diverse data available from orthopaedic settings — such as imaging and registries — hold the potential for significant developments.

Currently, AI applications in orthopaedics can be categorised into three main areas (figure 2):

  • Predictive analytics: Utilising machine learning to forecast patient outcomes and optimise treatment plans.
  • Computer vision: Enhancing diagnostics and surgical planning through advanced image analysis.
  • Natural language processing (NLP): Streamlining documentation and extracting meaningful insights from clinical texts.
Figure 2.jpg 1

Predictive analytics

Predictive analytics harnesses machine learning and deep learning technologies to enable computers to learn from vast amounts of data and make accurate predictions. Machine learning focuses on identifying patterns and relationships in data, while its subset, deep learning, utilises complex neural networks that mirror the human brain's architecture to analyse data at an even deeper level18. These technologies have been transformative, especially in predicting patient outcomes such as treatment effectiveness, patient satisfaction, potential postoperative complications, duration of hospital stays, and the likelihood of readmission19-25.

In the field of orthopaedics, predictive analytics has proven particularly effective. Machine learning models analyse detailed surgical procedure information and patient-specific data, capturing complex and nonlinear relationships that traditional analytical methods may misinterpret26. This nuanced understanding allows healthcare providers to tailor care protocols more precisely, thus enhancing the quality of outcomes and overall patient experiences.

A study by Huber et al. effectively predicted patient-reported outcomes from hip and knee replacement surgeries by analysing over 130,000 observations from the NHS PROMs dataset27. The research utilised machine learning models, including techniques like extreme gradient boosting and random forests, which demonstrated high predictive accuracy with AUC scores of 0.87 and 0.86. Similarly, research by Chen et al. using over 246,000 patient records from the American College of Surgeons National Surgical Quality Improvement Program, showed AI's efficacy in predicting the length of hospital stay (LOS) for Total Hip Arthroplasty patients19. Further, a review by Spence et al. indicates that deep learning methods are highly accurate in predicting surgical case durations, which is crucial for optimising hospital workflows, resource allocation, and reducing surgical backlogs28. These advancements in AI-driven predictive analytics are poised to significantly improve efficiency, patient care, and hospital management in orthopaedics.

As predictive analytics continues to evolve, its use in orthopaedic research and practice is expected to become more widespread, leading to increasingly data-centric and personalised approaches to patient care. This evolution marks a significant shift towards leveraging data-driven insights to improve all aspects of orthopaedic care, from pre-surgical planning to post-operative recovery and follow-up.

Computer vision

Computer vision allows computers to 'see' and interpret images and videos, much like human vision. This area of AI relies on technologies such as Deep Convolutional Neural Networks (DCNNs), which mimic the way humans process visual information, and transformer models, advanced algorithms that understand the context and relationships within visual data29.

In orthopaedics, the application of computer vision has potential to revolutionise the analysis of medical imaging data, such as X-rays, MRI, and CT scans. This technology enables a computer to diagnose various conditions, detect fractures, identify soft tissue injuries, and classify injuries in accordance with established medical standards30. Moreover, it can evaluate the positioning and alignment of arthroplasty implants in postoperative images31.

The study by Yoon et al. assesses the application of DCNNs in the detection of scaphoid fractures, analysing a dataset of 11,838 radiographs32. The research demonstrated the DCNN's effectiveness in enhancing diagnostic accuracy for both apparent and occult fractures, with the model achieving a sensitivity of 87.1%, specificity of 92.1%, and an area under the receiver operating curve (AUROC) of 0.955. These findings indicate the capability of DCNNs to significantly improve the detection of scaphoid fractures, including those that might not be identified through conventional examination methods.

Advancements in DCNNs have demonstrated potential in improving the diagnosis of meniscal tears. A recent meta-analysis indicates that AI models are capable of accurately detecting the presence of meniscal tears when analysing MRI images, with reported pooled sensitivity and specificity rates suggesting favourable diagnostic performance33. These outcomes suggest that AI could play a significant role in supporting the diagnostic capabilities of clinicians and radiologists. The adoption of AI for the diagnosis of meniscal tears suggests a shift towards more accurate, data-driven, and efficient approaches in orthopaedic care. Nevertheless, the full realisation of AI's potential in this area is contingent upon addressing existing challenges, including the need for standardised reporting, external validation, and comprehensive analyses of AI's diagnostic methodologies.

Natural language processing

NLP is a branch of AI that enables machines to understand, interpret, and respond to human language in a way that is both valuable and meaningful34. Patients are increasingly utilising technologies like ChatGPT for summarising information regarding healthcare issues, indicating a growing reliance on AI for health-related guidance. Despite its utility, there are concerns regarding the accuracy of the information provided and the potential for 'hallucinations', a term in AI that refers to instances where the model generates false or misleading information not supported by the input data. These challenges underscore the need for careful evaluation of AI-generated health information to ensure its reliability and safety for patient use.

In a study of 68 orthopaedic experts, ChatGPT's advice on post-surgical self-management was assessed for accuracy, applicability, and comprehensiveness35. Although it scored highly in accuracy and applicability, it was found lacking in depth, highlighting opportunities for enhancement to better address patient informational needs. Furthermore, NLP has potential to transform orthopaedic research by converting unstructured data — such as clinical notes, patient-reported narratives, and imaging reports — into structured formats, thereby facilitating the exploration of new research avenues. NLP can further support practitioners by efficiently summarising the latest research and treatment updates, and aid in clinical decision-making by extracting critical information from various health record sources, facilitating more informed and streamlined treatment planning.

While NLP holds promise for revolutionising orthopaedic medicine by streamlining tasks, enhancing information access, and supporting research efforts, it also faces limitations. These include potential inaccuracies in data interpretation, the need for extensive training datasets that accurately reflect clinical scenarios, and ensuring that the technology's decisions are both understandable and trusted by users

Current challenges and future perspectives

Implementing AI in orthopaedic surgery offers a significant opportunity to transform the practice, enhancing diagnostic accuracy, refining surgical planning, and potentially enabling semi- or fully autonomous procedures to improve outcomes. Beyond surgery, AI can streamline the entire care process — from primary care referrals to efficient clinic bookings and communications. Automated data gathering and reporting can revolutionise clinic assessments, real-time recording and transcription, appointment scheduling, and the allocation of operating slots and resources. Additionally, AI can tailor patient consents, rehabilitation protocols, and follow-up appointments to individual needs, boosting NHS efficiency and reducing costs.

Realising these benefits requires overcoming substantial challenges. Regulatory, ethical, and methodological hurdles, such as ensuring privacy, enhancing the consent process, and addressing the 'right to be forgotten' in data use, pose significant obstacles. Further, AI's 'black box' nature necessitates greater transparency and accountability to ensure that both healthcare providers and patients understand how decisions are made36. Addressing potential biases in AI systems is crucial to prevent care disparities, requiring diverse and representative training datasets. Initiatives like TRIPOD-AI and PROBAST-AI are crucial in establishing standards to ensure reliability and transparency in AI development37,38.

As we look to the future, interdisciplinary collaboration among computer scientists, data analysts, orthopaedic surgeons, and other healthcare professionals will be vital for advancing AI technologies within clinical settings. A key aspect of this advancement is the development of dynamic AI systems that not only adapt but also continuously learn from ongoing clinical data. The concept of 'data half-life' — the period after which data may become less relevant — underscores the need for AI models that can update their knowledge base in real-time to reflect the latest medical insights39. An adaptive methodology that incorporates various models and promotes continuous learning is crucial for enhancing the efficacy and reliability of AI applications. By embracing these dynamic models, we can significantly improve the flexibility and clinical utility of AI systems, paving the way for truly personalised medicine. This approach is a promising direction that aims to transform patient care by tailoring treatments to individual needs based on the most current and relevant data40.

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