Documentation:FIB book/Use of Virtual Models to Predict Ocular Injury from Blunt Force Trauma

Overview

Introduction

Severe eye injuries are often very life-altering, and an important cause of vision impairment disability. Eye injuries are resultant of direct force to the eye, but can also be caused indirectly through trauma to the head.

As it is composed of layers of various soft cell tissue, understanding the biomechanical response of an eye is an important but difficult task. Particularly, identifying thresholds resulting in failure is difficult to achieve. This literature review aims to explore the existing work on predicting ocular damage from blunt force trauma, non-penetrating injuries caused by impact without skin laceration.

The current work, strengths, and drawbacks of the following are discussed:

• Test methods used to generate data for validating virtual models

• Virtual modelling techniques, including multivariate analysis, finite element analysis (FEA), and other mathematical modelling

To ensure the credibility and relevance of the literature included in this review, sources were selected based on a combination of factors. These include publication in peer reviewed journals with established impact factors, citation counts reflecting influence within the field, and alignment with the topic of ocular biomechanics. For instance, key studies cited in this review such as those by Bisplinghoff et al. (2009), Rossi et al. (2011), and Weaver et al. (2011) have been cited over 100 times. These quantitative measures support the selection of each paper as academically reputable and relevant to the discussion.

Background

Diagram of critical eye structure. Outer eye wall is labelled outside the eye.

This section seeks to clarify the different categories of blunt-force eye injuries. Additionally, some key terms are introduced.

Blunt-force injuries to the eye (often referred to as the “globe”, the eyeball as a whole, excluding surrounding tissues) can be categorized as open or closed, depending on the depth of injury. Open-globe injuries are full-thickness wounds of any part of the outer eye wall, comprising the cornea, limbus, and sclera. Closed-globe injuries are anything less, including partial-thickness wounds of the eyewall or contusions.

Open-globe injuries are generally far more serious, although both types of injuries motivate a better understanding of eye biomechanics. This demonstrates a need for modelling techniques to have high fidelity, as closed-globe injuries still have potential to cause significant vision complications.

While open-globe injuries are more commonly caused by sharp objects, blunt force trauma can create enough pressure to rupture the eye.

Injury mechanisms

From blunt trauma, the eye is typically injured through a compression of the eye within its socket. The eye is subject to pressure, and can cause structural failures of sensitive eye tissue leading to globe contusions or rupture.

Blunt force can affect the eye from two primary mechanisms:

1. Direct Injury: The eye is struck directly, either from accidental or intentional injury. The contact occurs on or near the cornea. Corneal abrasions or lacerations can often occur from direct injury.
2. Indirect Injury: Blunt force trauma on the skull can also indirectly injure the eye. If the skull is impacted, the eye can experience a compressive force due to the sudden acceleration of the orbit.

The severity of injury to the eye is also affected by the biomechanical response of the eye orbit. A blowout fracture refers to a fracture or break in one of the bones comprising the eye orbit, and is the most common type of orbital fracture. As the facial bones within that region are relatively thin, a blowout fracture may often be an outcome of blunt force trauma.

One recent epidemiology study assesses patients with old blowout fractures, and examines the likelihood of further ocular injuries. Statistical analysis was performed to show that, with an old blowout fracture, patients were less likely to have intraocular complications.

This study exposes a challenge modelling the biomechanical response of an eye: the ocular injury outcome is highly dependent on the orbit in which it sits. Extensive study is required to understand this interaction between the eye orbit and the injury to the eye.

Measuring biomechanical response

A diverse amount of work has been done to characterize the response of an eye. Normal eye response is measured in vivo, to assess the health of an eye and be understood for medical reasons.

There also have been efforts to characterize the structural failure of a post-mortem human subject (PMHS, human cadaver specimens used for biomechanical testing) or porcine surrogate eye. These studies are important for creating a good understanding of the overall mechanical response of an eye. This data can be further used to validate injury prediction models.

In vivo measurement of normal eye response

In vivo studies have been performed to characterize the biomechanical response of a healthy, living eye. Typical tonometry methods (measurements of intraocular pressure) include indentation-based (contact) or air puff (non-contact) techniques.

Studies using healthy volunteers are critical for understanding the biomechanical response of a human eye. Various strengths and drawbacks present themselves in this approach.

• Strengths: Living eyes have some intraocular pressure, around 10-21mmHg. Injury biomechanics studies using PMHS or alternative surrogates will need to recreate this pressure, and cannot do so without some intrusion to the eye.
• Drawbacks: There is very limited access to the eye, thus limiting most biomechanical response studies to the cornea.

One interesting recent study uses acoustic waves to further explore novel methods of non-contact tonometry. In particular, this looks to be a potential way of delivering more comfortable eye exams. Further work is required, as the sample size taken was not large enough for statistical analysis. However, it is still worthy to note the current efforts to advance tonometry methods for characterizing eyes.

Injury measurement using PMHS

It is unethical to attempt to identify injury thresholds in a living eye. Research efforts have been focused on determining the ocular rupture pressure - a structural failure threshold - through post-mortem testing.

Two such studies have been selected and are examined here for their use of distinct test methods and instrumentation in characterizing the eye’s response to blunt trauma. The goal of both studies was to quantify intraocular rupture thresholds under various loading conditions to enable model validation and improved injury prediction.

Study 1 - High-Rate Internal Pressurization with Drop Tower

This study employed a drop tower setup in conjunction with a hydraulic pressurization system to simulate high-rate loading on human cadaver eyes. A hydraulic line was connected directly to the eye, and a falling weight generated internal pressurization through a piston mechanism. This method is intended to provide precise control over the pressurization rate, enabling accurate measurement of the rupture threshold under repeatable, high-speed conditions.

Some of the instrumentation included a miniature pressure transducer inserted through the optic nerve, and high-speed video was used to visually correlate the rupture event with the pressure data.

• Purpose: Determine the rupture thresholds at high pressurization rates.
• Strength: The setup allows for high experimental control and direct pressure measurement.
• Limitations: The test method is non-physiological, as internal hydraulic pressurization does not reflect real-life injury mechanisms.

The average rupture pressure measured across human eye specimens was reported as approximately 0.91 ± 0.29 MPa (137.7 ± 44.1 psi). This represents a threshold pressure above which globe rupture is likely to occur in high-rate loading scenarios.

Study 2 - Pneumatic Cannon Impact Simulation

This study used a custom pneumatic cannon to launch projectiles at porcine eye specimens. The objective was to replicate real-world blunt trauma, such as that from sports or accidents, by simulating external impacts to the eye. The test did not follow a specific standardized method but was designed to mimic the kinematics of blunt trauma scenarios.

Like the previous study, intraocular pressure sensors were inserted via the optic nerve to monitor internal pressure dynamics during impact with high-speed imaging to record the deformation and rupture in real time.

• Purpose: Evaluate rupture risk under impact-relevant loading conditions.
• Strengths: The test method is more representative of real-world blunt impacts.
• Limitations: Variability in projectile size and speed introduces uncertainty in pressure estimates. Furthermore, porcine eyes may differ structurally from human eyes.

Duma et al. reported rupture thresholds occurring at intraocular pressures ranging from 0.69 to 1.03 MPa, depending on impact conditions. The corresponding projectile velocities were in the range of 45 to 60 m/s. These values help establish a baseline for assessing risk in high-speed impact environments.

These two studies provide complementary insights - one prioritizing control and repeatability, and the other focusing on realism. The data generated from these tests serves as a critical benchmark for validating finite element models and establishing thresholds for globe rupture due to blunt trauma.

Injury prediction and modeling

Multivariate analysis

Multivariate analysis refers to statistical techniques used to evaluate the influence of multiple variables on a particular outcome. In this case, multivariate analysis is used to evaluate variables contributing to heightened risk of serious eye injury. Predictive factors available from ER assessments can be used to identify patients that require specialized eye evaluations.

A study conducted by Joseph et al. studied the records of over 6700 patients admitted to a level I trauma center over a 29-month period. 94 patients, excluding individuals with direct eye injuries, penetrating ocular injuries, and gunshot wounds, were identified with sustaining eye damage leading to ophthalmologic consultations. This data was organized based on various factors such as classification of eye injury, pupil examination at the time of admission, age, gender, mechanism of injury, etc. Forward stepwise logistic regression was used to determine the probability of blindness or serious eye injury based on factors available at the time of initial examinations.

The main predictors of serious eye injuries were afferent pupillary defect (AFD), facial fractures, and Glasgow Coma Scale (GCS) eye response. AFD is a clinical sign of damage to the optic nerve or severe retinal injury, and its presence typically indicates a compromised visual pathway. It is detected when the pupil reacts less briskly to direct light in the affected eye compared to the unaffected eye. GCS eye response, a component of the Glasgow Coma Scale used in trauma assessment, ranges from spontaneous eye opening to no response. A low GCS eye score suggests impaired neurological function, which may correlate with risk of serious ocular involvement, especially in the context of head trauma. The study found these physiological indicators to be stronger predictors of injury severity than external variables such as trauma mechanism or injury type.

Limitations of multivariate analysis

This study has taken the necessary steps in the development of predictive models for serious eye injuries. One of the biggest limitations of this study is the small sample size of just 94 patients. Furthermore, the data for this study was taken from a single level I trauma center which may not generalize to all other hospital settings.

Another major limitation and point for controversy about this study is the lack of imaging-based verification. The study mainly relied on clinical examinations which may miss less obvious injuries (retinal tears). Furthermore, many of these examinations, such as for AFD, require subjective judgement by the clinician which may introduce an observer's bias. Additionally, many of these examinations took place in high-pressure trauma settings which may also have an influence on the assessment. Although a great first step, additional work is required to develop accurate predictive models.

Mathematical models

Finite element modelling (FEM), a numerical method used to simulate how materials deform under stress, is a powerful tool for studying eye injuries, however, comes with its own significant challenges. The geometry of a generic human eye is very easy to model but the mechanical properties and impact response of different parts of the eye are not as trivial to determine. Rossi et al. reversed engineered a gold standard study in ocular traumatology to characterize these properties.

Rossi et al. used FEM to investigate the mechanism of retinal damage following blunt trauma by reverse-engineering a previous experiment conducted by Delori et al.. In the original Delori study (1969), human cadaver eyes were subjected to high-speed impacts using steel balls, while retinal damage patterns were documented post-mortem. The study reported that retinal contusions and lesions were primarily found in the posterior pole, away from the point of impact, suggesting that internal deformation and pressure wave propagation, rather than direct contact, were primary causes of injury.

Building on this, Rossi et al. constructed a simplified finite element model of a human eye, incorporating anatomical structures such as the cornea, sclera, lens, vitreous body, and retina. They tuned the material parameters and boundary conditions until their simulations reproduced the posterior lesions pattern described by Delori. The simulations predicted posterior eye stresses exceeding 0.10 to 0.15 MPa during deformation with tensile strains concentrated near the retina. Notably, their model protected negative pressure regions forming at the posterior retina, supporting a hypothesis that tensile stresses from rapid deformation contribute to retinal damage, rather than compressive impact alone. This contradicts the traditional view that vitreous traction is the main driver of blunt trauma-induced retinal lesions.

This reverse-engineering approach exemplifies how finite element modeling can be used not only to predict outcomes but also to explore injury mechanisms that are otherwise difficult to study directly. It underscores the importance of validating FEM with experimental data and shows how computational models can inform our understanding of the mechanical thresholds and internal dynamics that lead to ocular damage.

This FEM approach offers insight that is difficult to obtain experimentally, reinforcing the idea that tensile stresses, rather than vitreous traction alone, may be the primary cause of retinal damage in blunt trauma cases.

Another study by Weaver et al. looks into how eye shape and structure affects the risk of eye injury using FEA. A model simulated a blunt trauma event on a human eye and surrounding geometry. Factors such as aperture size, brow protrusion angle, eye protrusion, and eye position within the orbit were tested to see how they influence eye deformation and stress levels. The results of this study provide new insight into why some individuals are more at risk of eye injuries than others. This is another study showcasing the benefits of FEM in understanding eye injuries and the human body in general.

Limitations of mathematical models

While Rossi et al. provide important insight into the use of FEM for retinal injury mechanisms, the results are subject to numerous oversights and limitations. As mentioned in the paper, the material properties of the eye were overly simplified. While this was due to lack of experimental data on the mechanical response of various eye tissues, it still results in inaccuracies in the model. Furthermore, a generic eye geometry was used to model trauma for a 54-year-old patient. This is a significant oversight as an actual human eye differs heavily based on age and previous diseases.

Like with the study by Rossi et al., the FEA model used by Weaver et al. suffers from a lack of complete understanding of the material properties and trauma response of the eye. Furthermore, while geometry was the primary variable, age related changes were not taken into account in determining the risk of injury for certain individuals. While insightful, further development of such models is necessary before mathematical models become reliable enough to use in understanding eye injuries.

Discussion and future work

Multivariate analysis and mathematical models are promising tools for predicting eye injuries. Both tools, however, are still in their infancy and require significant work to be reliable predictors of eye injuries.

One of the biggest limitations for mathematical models is that they rely on simplified material properties for ocular tissues. These simplifications do not accurately represent the nonlinear, anisotropic, and viscoelastic behaviour of eye tissues. Further studies need to be conducted to address this lack of understanding of the material properties of the eye. More specifically, experimental data needs to be collected on the mechanical properties of the various tissues in the eye to address this shortcoming. Additionally, the mathematical models studied in this review simplify the eye as a solid whereas in reality, the eye is a fluid-filled structure. As such, computational fluid dynamics integrated with FEA studies should be investigated to account for the vitreous behaviour in the event of blunt trauma.

Once more refined models are developed, they should be validated against experimental and real-world trauma data. As with any mathematical model, the results from the model should be compared with experimental studies (cadaver or animal experimental data). This has been a major shortcoming of previous studies. With mathematical models improving overtime, this validation will be key to ensuring accuracy in predicting eye injuries.

In addition to mathematical modelling, multivariate analysis is another predictor for eye injuries. While promising, current work has significant drawbacks that need to be addressed before this analysis can be considered reliable. The small sample sizes and datasets is a major drawback to current studies on the topic. Larger studies need to take place that take larger datasets from numerous sources, such as injury reports from sports, military, and emergency care centers, to improve model robustness and make it applicable to the general population. Additionally, work needs to be done to reduce the bias in obtaining the data. Rather than relying on objective data, such as imaging results, traditional multivariate studies often rely on subjective clinical examinations. While these examinations are important, imaging-derived metrics should be incorporated to refine injury classification and reduce bias. Finally, more sophisticated machine learning algorithms can be incorporated, rather than relying on traditional regression, to improve injury prediction.

Mathematical models and multivariate analysis can be used in conjunction to further improve the prediction of eye injuries. While promising, significant work is still required to make these tools truly valuable in predicting injuries and getting treatment early.