Exploring the Vitreoretinal Interface: a Key Instigator of Unique Retinal Hemorrhage Patterns in Pediatric Head Trauma

An Update to Biomechanical and Biochemical Principles of Retinal Injury in Child Abuse

Abusive head trauma (AHT) is an extreme form of physical child abuse, a subset of which is shaken baby syndrome (SBS). While traumatic injury in children is most readily observed as marks of contusion on the body, AHT/SBS may result in internal injuries that can put the life of the child in danger. One pivotal sign associated with AHT/SBS that cannot be spotted with the naked eye is retinal injury (RI), an early sign of which is retinal hemorrhage (RH) in cases with rupture of the retinal vasculature. If not addressed, RI can lead to irreversible outcomes, such as visual loss. It is widely assumed that the major cause of RI is acceleration-deceleration forces that are repeatedly imposed on the patient during abusive shaking. Still, due to the controversial nature of this type of injury, few investigations have ever sought to delve into its biomechanical and/or biochemical features using realistic models. As such, our knowledge regarding AHT-/SBS-induced RI is significantly lacking. In this mini-review, we aim to provide an up-to-date account of the traumatology of AHT-/SBS-induced RI, as well as its biomechanical and biochemical features, while focusing on some of the experimental models that have been developed in recent years for studying retinal hemorrhage in the context of AHT/SBS.

Modeling of inflicted head injury by shaking trauma in children: what can we learn? : Update to parts I&II: A systematic review of animal, mathematical and physical models

Inflicted shaking trauma can cause injury in infants, but exact injury mechanisms remain unclear. Controversy exists, particularly in courts, whether additional causes such as impact are required to produce injuries found in cases of (suspected) shaking. Publication rates of studies on animal and biomechanical models of inflicted head injury by shaking trauma (IHI-ST) in infants continue rising. Dissention on the topic, combined with its legal relevance, makes maintaining an up-to-date, clear and accessible overview of the current knowledge-base on IHI-ST essential. The current work reviews recent (2017-2023) studies using models of IHI-ST, serving as an update to two previously published reviews. A systematic review was conducted in Scopus and PubMed for articles using animal, physical and mathematical models for IHI-ST. Using the PRISMA methodology, two researchers independently screened the publications. Two, five, and ten publications were included on animal, physical, and mathematical models of IHI-ST, respectively. Both animal model studies used rodents. It is unknown to what degree these can accurately represent IHI-ST. Physical models were used mostly to investigate gross head-kinematics during shaking. Most mathematical models were used to study local effects on the eye and the head's internal structures. All injury thresholds and material properties used were based on scaled adult or animal data. Shaking motions used as inputs for animal, physical and mathematical models were mostly greatly simplified. Future research should focus on using more accurate shaking inputs for models, and on developing or and validating accurate injury thresholds applicable for shaking.

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.

Quantifying the efficacy of protective eyewear in pediatric soccer-induced retinal injury

BACKGROUND

Ocular injury is common in children playing sports. Sports-related eye injuries, if severe enough, can lead to permanent vision impairment. Soccer, the most popular sport in the world, is a sport in which players rarely use protective eyewear. The purpose of this study was to determine how eye injuries are induced by a soccer ball impact and to evaluate whether eye protection influences the effects of impact.

METHODS

A finite element (FE) computer simulation was used to simulate soccer ball trauma on a model of the eye with and without eye protection. Protective eyewear of different materials (polycarbonate and acrylic) was modeled to investigate the optimal medium for eye protection. Stress and strain experienced by the eyeball was quantified by the FE computer simulation in each model.

RESULTS

Protective eyewear was found to be effective in lowering ocular stress and strain by absorbing and redirecting energy from the ball. Compared to the unprotected eye model, polycarbonate eyewear reduced the average stress the retina experienced by 61%, whereas the acrylic model reduced the average stress by 40%. Polycarbonate and acrylic eyewear also reduced the maximum strain experienced by the retina by 69% and 47%, respectively, reducing the severity of deformations of the eye on impact.

CONCLUSIONS

These findings suggest that wearing protective eyewear, especially when made of polycarbonate, can be an effective means of reducing injury-inducing retinal stress. The use of eye protection is thus recommended for pediatric patients participating in soccer.

Rapid Prediction of Retina Stress and Strain Patterns in Soccer-Related Ocular Injury: Integrating Finite Element Analysis with Machine Learning Approach

Soccer-related ocular injuries, especially retinal injuries, have attracted increasing attention. The mechanics of a flying soccer ball have induced abnormally higher retinal stresses and strains, and their correlation with retinal injuries has been characterized using the finite element (FE) method. However, FE simulations demand solid mechanical expertise and extensive computational time, both of which are difficult to adopt in clinical settings. This study proposes a framework that combines FE analysis with a machine learning (ML) approach for the fast prediction of retina mechanics. Different impact scenarios were simulated using the FE method to obtain the von Mises stress map and the maximum principal strain map in the posterior retina. These stress and strain patterns, along with their input parameters, were used to train and test a partial least squares regression (PLSR) model to predict the soccer-induced retina stress and strain in terms of distributions and peak magnitudes. The peak von Mises stress and maximum principal strain prediction errors were 3.03% and 9.94% for the frontal impact and were 9.08% and 16.40% for the diagonal impact, respectively. The average prediction error of von Mises stress and the maximum principal strain were 15.62% and 21.15% for frontal impacts and were 10.77% and 21.78% for diagonal impacts, respectively. This work provides a surrogate model of FE analysis for the fast prediction of the dynamic mechanics of the retina in response to the soccer impact, which could be further utilized for developing a diagnostic tool for soccer-related ocular trauma.