The 3D skull 0-4 years: A validated, generative, statistical shape model

An open-source, three-dimensional growth model of the mandible

The available reference data for the mandible and mandibular growth consists primarily of two-dimensional linear or angular measurements. The aim of this study was to create the first open-source, three-dimensional statistical shape model of the mandible that spans the complete growth period. Computed tomography scans of 678 mandibles from children and young adults between 0 and 22 years old were included in the model. The mandibles were segmented using a semi-automatic or automatic (artificial intelligence-based) segmentation method. Point correspondence among the samples was achieved by rigid registration, followed by non-rigid registration of a symmetrical template onto each sample. The registration process was validated with adequate results. Principal component analysis was used to gain insight in the variation within the dataset and to investigate age-related changes and sexual dimorphism. The presented growth model is accessible globally and free-of-charge for scientists, physicians and forensic investigators for any kind of purpose deemed suitable. The versatility of the model opens up new possibilities in the fields of oral and maxillofacial surgery, forensic sciences or biological anthropology. In clinical settings, the model may aid diagnostic decision-making, treatment planning and treatment evaluation.

Current understanding of children's head shape and its impact on health: a cross-sectional study among pediatric medical staff in China

Background

Head shape problems are common in infancy and early childhood, and thus their early identification and management can benefit the health of children. This study aimed to investigate pediatric healthcare professionals' existing knowledge of children's head shape abnormalities and their associated effects in China, providing guidelines for future clinical interventions, training, and interdisciplinary collaboration.

Methods

We conducted a survey among pediatric medical staff, encompassing various age groups, genders, hospitals, and professional levels. The electronic questionnaire queried respondents' basic information, knowledge pertaining to head shape issues, diagnosis and treatment approaches, and the clinical development status of head shape problems. All surveys and data collection were conducted anonymously.

Results

A total of 214 valid questionnaires were collected. Differences in the level of understanding among medical staff regarding head shape issues were observed. Medical staff in tertiary care facilities showed the highest proficiency in diagnosing and treating positional plagiocephaly and cranial asymmetry (P<0.05), while those in primary care facilities exhibited the lowest competency in diagnosing head shape abnormalities (P<0.05). Most medical staff had a partial understanding of specific aspects of head shape issues, such as identifying high-risk individuals (n=144, 67.29%), making diagnoses (n=176, 82.24%), and understanding the consequences (n=151, 70.56%), with no significant differences across medical facilities of various levels. Additionally, 99.07% (n=212) of the medical staff believed that head shape measurements should be included as a routine component of pediatric physical examinations, and 75.23% (n=161) incorporate head shape assessment as part of their routine physical examination. Furthermore, 91.12% (n=195) of the medical staff received consultations on children's head shape issues, with a higher prevalence in secondary and tertiary care facilities. Finally, 93.97% (n=201) of the participants expressed the need for further education and knowledge on pediatric head shape, with no significant differences across medical facilities of various levels.

Conclusions

There is a limited understanding among medical personnel in China regarding children's head shape issues. Therefore, it is imperative to enhance training and educational initiatives for medical staff in China, with the goal of enhancing their awareness and knowledge regarding children's head shape problems.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Normal human craniofacial growth and development from 0 to 4 years

Knowledge of human craniofacial growth (increase in size) and development (change in shape) is important in the clinical treatment of a range of conditions that affects it. This study uses an extensive collection of clinical CT scans to investigate craniofacial growth and development over the first 48 months of life, detail how the cranium changes in form (size and shape) in each sex and how these changes are associated with the growth and development of various soft tissues such as the brain, eyes and tongue and the expansion of the nasal cavity. This is achieved through multivariate analyses of cranial form based on 3D landmarks and semi-landmarks and by analyses of linear dimensions, and cranial volumes. The results highlight accelerations and decelerations in cranial form changes throughout early childhood. They show that from 0 to 12 months, the cranium undergoes greater changes in form than from 12 to 48 months. However, in terms of the development of overall cranial shape, there is no significant sexual dimorphism in the age range considered in this study. In consequence a single model of human craniofacial growth and development is presented for future studies to examine the physio-mechanical interactions of the craniofacial growth.