Implementing 3D printing and extended reality in anatomy education: Organization, evolution, and assessment of an innovative teaching program in an undergraduate medical school in the Netherlands.

Background: A knowledge gap exists on how to implement three dimensional (3D) printing and extended reality (XR) technologies in anatomy education. Also, it remains unclear how students experience the implementation of these new technologies in anatomy education,. This report describes the result of an elective course on the use of 3D prints and XR at our Faculty of Medical Sciences.

Three-dimensional facial morphology in patients with craniofacial microsomia and microtia.

Introduction: Craniofacial microsomia (CFM) is classified using the subjective Orbit, Mandible, Ear, Nerve and Soft tissue (OMENS) tool. Digital stereophotogrammetry (i.e.

Evaluating the Effectiveness of Transtibial Prosthetic Socket Shape Design Using Artificial Intelligence: A Clinical Comparison With Traditional Plaster Cast Socket Designs.

Objective: To investigate the feasibility of creating an artificial intelligence (AI) algorithm to enhance prosthetic socket shapes for transtibial prostheses, aiming for a less operator-dependent, standardized approach.

Design: The study comprised 2 phases: first, developing an AI algorithm in a cross-sectional study to predict prosthetic socket shapes. Second, testing the AI-predicted digitally measured and standardized designed (DMSD) prosthetic socket against a manually measured and designed (MMD) prosthetic socket in a 2-week within-subject cross-sectional study.

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.

Fully automated landmarking and facial segmentation on 3D photographs.

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach.

Longitudinal Three-Dimensional Follow-Up and Secondary Treatment Aspects after Endoscopic and Open Scaphocephaly Surgery.

Background: This retrospective cohort study evaluated the longitudinal three-dimensional cranial shape developments and the secondary treatment aspects after endoscopically assisted craniosynostosis surgery (EACS) with helmet therapy and open cranial vault reconstruction (OCVR) for scaphocephaly.

Methods: Longitudinally collected three-dimensional photographs from scaphocephaly patients and healthy infants were evaluated. Three-dimensional cranial shape measurements and growth maps were compared between the groups over time.

Views on Augmented Reality, Virtual Reality, and 3D Printing in Modern Medicine and Education: A Qualitative Exploration of Expert Opinion.

Although an increased usage and development of 3D technologies is observed in healthcare over the last decades, full integration of these technologies remains challenging. The goal of this project is to qualitatively explore challenges, pearls, and pitfalls of AR/VR/3D printing applications usage in the medical field of a university medical center. Two rounds of face-to-face interviews were conducted using a semi-structured protocol.

Automatic grading of patients with a unilateral facial paralysis based on the Sunnybrook Facial Grading System - A deep learning study based on a convolutional neural network.

Purpose: In order to assess the severity and the progression of a unilateral peripheral facial palsy the Sunnybrook Facial Grading System (SFGS) is a well-established grading system due to its clinical relevance, sensitivity, and robust measuring method. However, training is required in order to achieve a high inter-rater reliability. This study investigated the automated grading of facial palsy patients based on the SFGS using a convolutional neural network.

Static and Motion Facial Analysis for Craniofacial Assessment and Diagnosing Diseases.

Deviation from a normal facial shape and symmetry can arise from numerous sources, including physical injury and congenital birth defects. Such abnormalities can have important aesthetic and functional consequences. Furthermore, in clinical genetics distinctive facial appearances are often associated with clinical or genetic diagnoses; the recognition of a characteristic facial appearance can substantially narrow the search space of potential diagnoses for the clinician.

Outcomes after Flow Diverter Treatment in Subarachnoid Hemorrhage: A Meta-Analysis and Development of a Clinical Prediction Model (OUTFLOW).

Background: patients with a subarachnoid hemorrhage (SAH) might need a flow diverter (FD) placement for complex acutely ruptured intracranial aneurysms (IAs). We conducted a meta-analysis and developed a prediction model to estimate the favorable clinical outcome after the FD treatment in acutely ruptured IAs.

Methods: a systematic literature search was performed from 2010 to January 2021 in PubMed and Embase databases.

The effectiveness of the use of augmented reality in anatomy education: a systematic review and meta-analysis.

The use of Augmented Reality (AR) in anatomical education has been promoted by numerous authors. Next to financial and ethical advantages, AR has been described to decrease cognitive load while increasing student motivation and engagement. Despite these advantages, the effects of AR on learning outcome varies in different studies and an overview and aggregated outcome on learning anatomy is lacking.

Mixed-methods exploration of students' motivation in using augmented reality in neuroanatomy education with prosected specimens.

The use of augmented reality (AR) in teaching and studying neuroanatomy has been well researched. Previous research showed that AR-based learning of neuroanatomy has both alleviated cognitive load and was attractive to young learners. However, how the attractiveness of AR effects student motivation has not been discovered.

Virtual reality and annotated radiological data as effective and motivating tools to help Social Sciences students learn neuroanatomy.

Neuroanatomy as a subject is important to learn, because a good understanding of neuroanatomy supports the establishment of a correct diagnosis in neurological patients. However, rapid changes in curricula reduced time assigned to study (neuro)anatomy. Therefore, it is important to find alternative teaching methods to study the complex three-dimensional structure of the brain.

Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning.

The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on surgically achieved mandibular displacements (training group).

Prediction Models in Aneurysmal Subarachnoid Hemorrhage: Forecasting Clinical Outcome With Artificial Intelligence.

Background: Predicting outcome after aneurysmal subarachnoid hemorrhage (aSAH) is known to be challenging and complex. Machine learning approaches, of which feedforward artificial neural networks (ffANNs) are the most widely used, could contribute to the patient-specific outcome prediction.

Objective: To investigate the prediction capacity of an ffANN for the patient-specific clinical outcome and the occurrence of delayed cerebral ischemia (DCI) and compare those results with the predictions of 2 internationally used scoring systems.

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis.

Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to investigate if a deep learning algorithm is capable of correctly classifying the head shape of infants as either healthy controls, or as one of the following three craniosynostosis subtypes; scaphocephaly, trigonocephaly or anterior plagiocephaly.

Neuroanatomy Learning: Augmented Reality vs. Cross-Sections.

Neuroanatomy education is a challenging field which could benefit from modern innovations, such as augmented reality (AR) applications. This study investigates the differences on test scores, cognitive load, and motivation after neuroanatomy learning using AR applications or using cross-sections of the brain. Prior to two practical assignments, a pretest (extended matching questions, double-choice questions and a test on cross-sectional anatomy) and a mental rotation test (MRT) were completed.

Automated detection of third molars and mandibular nerve by deep learning.

The approximity of the inferior alveolar nerve (IAN) to the roots of lower third molars (M3) is a risk factor for the occurrence of nerve damage and subsequent sensory disturbances of the lower lip and chin following the removal of third molars. To assess this risk, the identification of M3 and IAN on dental panoramic radiographs (OPG) is mandatory. In this study, we developed and validated an automated approach, based on deep-learning, to detect and segment the M3 and IAN on OPGs.

Systematic Review and Neural Network Analysis to Define Predictive Variables in Implantable Motor Cortex Stimulation to Treat Chronic Intractable Pain.

Implantable motor cortex stimulation (iMCS) has been performed for >25 years to treat various intractable pain syndromes. Its effectiveness is highly variable and, although various studies revealed predictive variables, none of these were found repeatedly. This study uses neural network analysis (NNA) to identify predictive factors of iMCS treatment for intractable pain.

Use of artificial neural networks to predict anterior communicating artery aneurysm rupture: possible methodological considerations.

Use of algorithms to generate synthetic cases might result in a misrepresentation of the entire population. Training an artificial neural network with a mix of real and synthetic data might lead to non-realistic prediction precision.

Importance and Presence of High-Quality Evidence for Clinical Decisions in Neurosurgery: International Survey of Neurosurgeons.

Background: The publication rate of neurosurgical guidelines has increased tremendously over the past decade; however, only a small proportion of clinical decisions appear to be based on high-quality evidence.

Objective: The aim was to evaluate the evidence available within neurosurgery and its value within clinical practice according to neurosurgeons.

Methods: A Web-based survey was sent to 2552 neurosurgeons, who were members of the European Association of Neurosurgical Societies.

Cranial shape comparison for automated objective 3D craniosynostosis surgery planning.

Virtual planning of open cranial vault reconstruction is used to simulate and define an pre-operative plan for craniosynostosis surgery. However, virtual planning techniques are subjective and dependent on the experience and preferences of the surgical team. To develop an objective automated 3D pre-operative planning technique for open cranial vault reconstructions, we used curvature maps for the shape comparison of the patient's skull with an age-specific reference skull.

Radiation-free 3D head shape and volume evaluation after endoscopically assisted strip craniectomy followed by helmet therapy for trigonocephaly.

Introduction: Radiation-free 3D post-operative sequential follow-up in craniosynostosis is hindered by the lack of consistent markers restricting evaluation to subjective comparison. However, using the computed cranial focal point (CCFP), it is possible to perform correct sequential image superposition and objective evaluation. We used this technique for mean volume and shape change evaluation of the head utilizing 3D photos after endoscopically assisted trigonocephaly surgery.