An adaptive-remeshing framework to predict impact-induced skull fracture in infants

Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

Assessment System for Child Head Injury from Falls Based on Neural Network Learning

Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots' frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers' daily falling at home from their parents to evaluate the framework's performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven.

Age-related skull fracture patterns in infants after low-height falls

BACKGROUND

Prior research and experience has increased physician understanding of infant skull fracture prediction. However, patterns related to fracture length, nonlinearity, and features of complexity remain poorly understood, and differences across infant age groups have not been previously explored.

METHODS

To determine how infant and low-height fall characteristics influence fracture patterns, we collected data from 231 head CT 3D reconstructions and quantified length and nonlinearity using a custom image processing code. Regression analysis was used to determine the effects of age and fall characteristics on nonlinearity, length, and features of fracture complexity.

RESULTS

While impact surface had an important role in the number of cracks present in a fracture, younger infants and greater fall heights significantly affected most features of fracture complexity, including suture-to-suture spanning and biparietal involvement. In addition, increasing fracture length with increasing fall height supports trends identified by prior finite-element modeling. Finally, this study yielded results supporting the presence of soft tissue swelling as a function of fracture location rather than impact site.

CONCLUSIONS

Age-related properties of the infant skull confer unique fracture patterns following head impact. Further characterization of these properties, particularly in infants <4 months of age, will improve our understanding of the infant skull's response to trauma.

IMPACT

Younger infant age and greater fall heights have significant effects on many features of fracture complexity resulting from low-height falls. Incorporating multiple crack formation and multiple bone involvement into computational models of young infant skull fractures may result in increased biofidelity. Drivers of skull fracture complexity are not well understood, and skull fracture patterns in real-world data across infant age groups have not been previously described. Understanding fracture complexity relative to age in accidental falls will improve the understanding of accidental and abusive head trauma.

Statistical analysis of the thickness and biomechanical properties of Japanese children's skulls

OBJECTIVE

The structure and strength of a child's skull are important in accurately determining what and how external forces were applied when examining head injuries. The aims of this study were to measure skull thickness and strength in children, evaluate sex differences, and investigate the correlation between skull thickness and strength and age.

MATERIALS AND METHODS

Skulls were obtained from 42 Japanese dead bodies under 20 years of age. During the autopsies, bone samples were taken from each skull. The length, width, and central thickness of the skulls were measured using calipers. Three-point bending tests were conducted, and bending load and displacement were recorded. Bending stress and bending strain were calculated, and Young's modulus, 0.2% proof stress, and maximum stress were obtained.

RESULTS

In cases under 1.5 years old, 14 out of 46 male samples and 20 out of 40 female samples did not fracture during the three-point bending test, though no significant sex differences were detected. No significant differences in age, sample thickness, Young's modulus, 0.2% proof stress, or maximum stress were detected between the sexes. The sample thickness, Young's modulus, 0.2% proof stress, and maximum stress increased significantly and logarithmically with age (R² = 0.761-0.899). Although age correlated with thickness, Young's modulus, and maximum stress more in females than in males, 0.2% proof stress correlated slightly better in males than in females.

CONCLUSION

The skulls of preschool children, in particular, are thin, have low strength, and are at high risk of fracturing even with relatively small external forces. Unlike adults, no significant sex differences in skull thickness or strength were observed in children.

Head biomechanics of video recorded falls involving children in a childcare setting

The objective of this study was to characterize head biomechanics of video-recorded falls involving young children in a licensed childcare setting. Children 12 to < 36 months of age were observed using video monitoring during daily activities in a childcare setting (in classrooms and outdoor playground) to capture fall events. Sensors (SIM G) incorporated into headbands worn by the children were used to obtain head accelerations and velocities during falls. The SIM G device was activated when linear acceleration was ≥ 12 g. 174 video-recorded falls activated the SIM G device; these falls involved 31 children (mean age = 21.6 months ± 5.6 SD). Fall heights ranged from 0.1 to 1.2 m. Across falls, max linear head acceleration was 50.2 g, max rotational head acceleration was 5388 rad/s², max linear head velocity was 3.8 m/s and max rotational head velocity was 21.6 rad/s. Falls with head impact had significantly higher biomechanical measures. There was no correlation between head acceleration and fall height. No serious injuries resulted from falls—only 1 child had a minor injury. In conclusion, wearable sensors enabled characterization of head biomechanics during video-recorded falls involving young children in a childcare setting. Falls in this setting did not result in serious injury.

High-Rate Anisotropic Properties in Human Infant Parietal and Occipital Bone

Computational models of infant head impact are limited by the paucity of infant cranial bone material property data, particularly with regard to the anisotropic relationships created by the trabecular fibers in infant bone. We previously reported high-rate material property data for human infant cranial bone tested perpendicular to trabeculae fiber orientation. In this study, we measure the anisotropic properties of human infant cranial bone by analyzing bending modulus parallel to the trabeculae fibers. We tested human bone specimens from nine donors ranging in age from 32 weeks gestational age to 10 months at strain rates of 12.3-30.1 s-1. Bending modulus significantly increased with donor age (p=0.008) and was 13.4 times greater along the fiber direction compared to perpendicular to the fibers. Ultimate stress was greater by 5.1 times when tested parallel to the fibers compared to perpendicular (p=0.067). Parietal bone had a higher modulus and ultimate stress compared to occipital bone, but this trend was not significant, as previously shown perpendicular to fiber orientation. Combined, these data suggest that the pediatric skull is highly age-dependent, anisotropic, and regionally dependent. The incorporation of these characteristics in finite element models of infant head impact will be necessary to advance pediatric head injury research and further our understanding of the mechanisms of head injury in children.