FP-00031

Section 1 - Basic information about you and your application:

Title of research project
Development of Artificial Intelligence to diagnose ligament health and injury potential.

Grant Type
The ORUK Inspiration Fund

Research area
Diagnostic

Duration
12

Start date
March 4, 2024

Have you previously received funding from ORUK?
No.

Profession
Academic scientist

Your current job title/position
Senior Lecturer

Are you an early-career researcher (ECR)? (definition of ECR)
no

Section 2 - Lay summary

Lay summary:

Ankle sprain is one of the most common injuries across the general population and particularly in sport. Globally, an ankle sprain accounts for 5-10% of all accident and emergency visits per year. This equates to approximately 2 million reported incidences in the UK alone and has been estimated to cost the economy more than £1bn. Over 50% of first-time sprains involve chronic symptoms, including pain and instability, leading to a high risk of re-injury and development of longer-term ankle instability. In most sprain cases, the outer side of the ankle is affected usually by a tear in one particular ligament – the anterior-talofibular ligament (ATFL).

Ankle ligaments are crucial for daily activities such as standing, walking and running because their main function is to stabilise the ankle joint. With increasing load, the ligament structure gets longer and stiffer, but will continue to function and absorb energy until loading reaches the tissue’s elastic limit. After this limit, strain in the ligament increases at the expense of stiffness and the likelihood of tissue damage is heightened. Expensive diagnostic technologies such as magnetic resonance imaging and computed tomography can diagnose ligament injury and give some indication of the tissue’s health during rehabilitation. However, both techniques fall short as they cannot quantify a ligament’s capacity to withstand physical stress (e.g., mechanical loading), which is an important consideration for injury prevention and effective rehabilitation.

A tissue’s tolerance to stress can be assessed by measuring its stiffness. An increasingly popular technique being used in musculoskeletal research for estimating tissue stiffness is shear-wave elastography via ultrasound imaging. Ultrasound beams generate tiny forces inside the body, which spread sideways to the beam and displace tissue. The shear-wave elastography technique detects the velocities (speed and direction) of the resulting displacements and tissue elasticity can be measured. For example, if a ligament has a low shear-wave velocity, then the tissue is lax. This is a threat for injury.

The ambition of this research project is to provide the Orthopaedic community with a cost-effective and practical solution to predict and manage ankle sprain injury. The longer-term aim is to reduce the incidence rate. The proposed project will recruit a broad cross-section of the population differing in age, gender, ethnicity, health and physical activity. It will specifically recruit members of the public and athletes suffering from current or previous ankle sprain injury to participate in the study and assist in disseminating the study’s aims and its findings; as well as advising future research. Participants will have their ATFL ligaments imaged, and the measures of stiffness will be entered into a database. We will then use artificial intelligence to produce an innovative diagnostic tool for the prevention and management of ankle sprain injury.

Section 3 - Purpose of research

Purpose of research:

This project will be part of on-going PhD research. The overall aim is to deliver a ligament screening test that objectively measures the capacity of ATFL to tolerate mechanical loading, which can be used in practice for acute and chronic assessment of the ligament. To achieve this, a database of ATFL stiffness values will be created and subsequently managed using artificial intelligence methods to provide a score on ligament health and injury potential.

The project will have three deliverables as the result of following work packages:

• W-P 1: Data collection – the objective is to collect data on ATFL health representing a broad cross-section of the population. This will be an ongoing work package throughout the length of the project, where participants will visit the Musculoskeletal Biomechanics laboratory at LSBU and undergo an experimental protocol (section 5) that images the ATFL of both extremities with shear-wave ultrasound elastography. The deliverable will be a minimum (minimum n=300) number of ATFL stiffness records indexed according to demographics, health and physical activity status and entered to a normative database.
• W-P 2: Building of database – the objective is to create a population database of ATFL health. Each measure of ligament stiffness will be entered into the database, and cumulatively, used for the identification of ‘norms’ and ‘outliers’ for a given ATFL based on demographics, health and physical activity status. The deliverable will be a relational database system created in Postgre SQL.
• W-P 3: Programming of artificial intelligence – the objective is to develop a learning algorithm that returns a visual score on ligament health. The algorithm will be written in Python and the deliverable will be a software that acts as a front end in accepting location specific ATFL stiffnesses, cross-references a normative database, and returns an individual’s potential for injury.

Section 4 - Background to investigation

Background to investigation:

The prevalence rate of ankle sprain injury continues to rise [1] and still, a comprehensive understanding of the risk factors underlying the injury is not clear [2], particularly factors associated with joint anatomy [2]. In most ankle sprain cases, the ATFL is the affected ligament, and this is because it has the poorest strain behaviour of all the lateral ankle ligaments [3].

There is sufficient evidence that an injured Achilles tendon can be reflected by its mechanical properties (e.g., its stiffness) [4,5,6,7,8]; therefore, the same should be true for the ATFL, given that tendon and ligament share a similar structure [9]. However, the typical approach for measuring Achilles tendon stiffness (via synchronized ultrasound and dynamometry) is problematic for ligament because load and displacement are difficult to measure. An alternative approach is shear-wave elastography; this is an emerging technology in musculoskeletal research, but not without its limitations. Primarily, these are artefactual challenges that influence the speed of the shear-wave such as tissue heterogeneity and depth, and underlying osseous structures [10]; although, these are more of a concern for skeletal muscle and deeper tissue elastography than shear-wave investigations of ATFL. Indeed, the ATFL is a passive and superficial tissue and correct positioning of the transducer can overcome the reflection of bone within an elastogram [10].

Notwithstanding these limitations, shear-wave elastography has demonstrated that the stiffness of the ulna collateral ligament in the throwing arm of collegiate baseball pitchers significantly reduces during the season compared to pre-season (p=0.001) [11,12] and non-throwing arm (p<0.001) measures [11]. This is indicative of ligament laxity and what should be a flag for therapeutic intervention. Similar observations have been shown in collegiate football players [12]. The technique has also been shown to be sensitive to changes in ankle position with shear-wave velocities being significantly greater (p<0.001) in ATFL when plantarflexed and under greater strain than a relaxed position [13]. In addition, inter- [14] and intra-observer [15] reliability of ATFL measurements has been demonstrated, but construct validity is still missing from the literature. In the tendon, there is equivocal opinion on this issue [16,17].

In summary, shear-wave elastography is sensitive enough to detect changes in the stiffness of ligament with training and competition, or when placed under different loads; and its output – either shear-wave velocity or shear modulus, is reliable and repeatable.

The investigatory team are internationally recognized for research on muscle-tendon biomechanics spanning over 20 years and including over 30 publications in high-ranking journals, invited keynote lectures, and knowledge transfer / income generating activities with private healthcare companies, Olympic organizations and professional football clubs. We are also internationally recognized for work in super-resolution ultrasound imaging techniques, sports injuries and foot and ankle biomechanics. Combined with a senior UK foot and ankle consultant orthopaedic surgeon, the investigatory team has the expertise and track record to deliver the proposed work packages and add significant knowledge to the area of ankle sprain injury.

Dr Darren James (PI): is a Senior Lecturer in Biomechanics. He has led external research and development consultancy projects for the footwear industry and has supervised PhD projects in the areas of clinical gait analysis and foot and ankle biomechanics. He has led on two projects funded by the British Orthopaedic Foot and Ankle Society and in total, has secured over £500k (as PI or Co-I) in research income, capital investment funding and enterprise activity. Darren has co-authored 22 scientific papers within the Biomechanics area and one book chapter on ankle sprain injury.

Miss. Fenja Deister (Co-I, doctoral researcher): completed her BSc in Biomedical Engineering at Hochschule Mannheim, MSc in Biomedical Engineering at University of Stuttgart, and in January 2023 accepted a fully funded PhD scholarship at LSBU to investigate the mechanical properties of ligaments.

Dr. Sevan Harput (Co-I, project adviser): is an Associate Professor and head of South Bank Applied BioEngineering Research (SABER). He has secured over £1.2m in research income as a PI or Co-I and has given 10 invited talks on ultrasound imaging, including the first clinical super-resolution ultrasound imaging study. He has authored 27 peer-reviewed journal articles; is an Associate Editor for IEEE T-UFFC and sits on the Royal Society panel for the International Exchanges scheme.

Prof. Kiros Karamanidis (Co-I, project supervisor): is a Professor of Ageing and Exercise Science and is head of the Musculoskeletal Biomechanics Research group at LSBU. He is an author and co-author of more than 100 peer reviewed journal publications and book chapters and has secured over €4.3m, as a PI or Co-I, in research grant income and enterprise projects.

Mr. Matthew Solan (Co-I, project recruitment and translation outside academia): is a Consultant Orthopaedic Surgeon at Royal Surrey County Hospital. He is past chairman of the BOFAS Scientific Committee and has co-authored over 100 peer-reviewed journal articles predominantly on foot and ankle surgery.

Prof. Wolfgang Potthast (Co-I, project collaboration in sport injury research): is a Professor of Clinical Biomechanics within the Institute of Orthopaedics and Biomechanics at the German Sports Institute, Cologne. He is currently president of the German Society of Biomechanics and has worked on several funded football-related projects with different research institutions (e.g., FIFA) and industry partners.

 

Preliminary work so far has revealed that B-mode ultrasound imaging of ATFL is feasible and reliable. Intra-observer reliability was assessed from multiple measurements within a day (n=3) and between days (n=5) in one healthy female (white American) participant aged 2