Special_Issue_Teaching_and_Training_Future_Health_Informaticians: Managing the transition from tradition to innovation of the Heidelberg/Heilbronn Medical Informatics Master's Program.

Background: To keep pace with the developments in the medical informatics field, the curriculum of the Heidelberg/Heilbronn Medical Informatics Master of Science program is continuously updated. In its latest revision we restructured our program to allow more flexibility to accommodate updates and include current topics and to enable students' choices.

Objectives: To present our new concepts for graduate medical informatics education, share our experiences, and provide insights into the perception of these concepts by advanced students and graduates.

Explainable Artificial Intelligence for Deep-Learning Based Classification of Cystic Fibrosis Lung Changes in MRI.

Algorithms increasing the transparence and explain ability of neural networks are gaining more popularity. Applying them to custom neural network architectures and complex medical problems remains challenging. In this work, several algorithms such as integrated gradients and grad came were used to generate additional explainable outputs for the classification of lung perfusion changes and mucus plugging in cystic fibrosis patients on MRI.

Next-generation study databases require FAIR, EHR-integrated, and scalable Electronic Data Capture for medical documentation and decision support.

Structured patient data play a key role in all types of clinical research. They are often collected in study databases for research purposes. In order to describe characteristics of a next-generation study database and assess the feasibility of its implementation a proof-of-concept study in a German university hospital was performed.

Impact of data synthesis strategies for the classification of craniosynostosis.

Introduction: Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data are rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically.

The Use of Artificial Intelligence for the Classification of Craniofacial Deformities.

Positional cranial deformities are a common finding in toddlers, yet differentiation from craniosynostosis can be challenging. The aim of this study was to train convolutional neural networks (CNNs) to classify craniofacial deformities based on 2D images generated using photogrammetry as a radiation-free imaging technique. A total of 487 patients with photogrammetry scans were included in this retrospective cohort study: children with craniosynostosis (n = 227), positional deformities (n = 206), and healthy children (n = 54).

Automated calculation of ontology-based planning proposals: An application in reconstructive oral and maxillofacial surgery.

Background: Structured modelling of surgical knowledge and its automated processing is still challenging. The aim of this work is to introduce a novel approach for automated calculation of ontology-based planning proposals in mandibular reconstruction and conduct a feasibility study.

Methods: The presented approach is composed of an RDF(S) ontology, a 3D mandible template and a calculator-optimiser algorithm to automatically calculate reconstruction proposals with fibula grafts.

3D-2D Distance Maps Conversion Enhances Classification of Craniosynostosis.

Objective: Diagnosis of craniosynostosis using photogrammetric 3D surface scans is a promising radiation-free alternative to traditional computed tomography. We propose a 3D surface scan to 2D distance map conversion enabling the usage of the first convolutional neural networks (CNNs)-based classification of craniosynostosis. Benefits of using 2D images include preserving patient anonymity, enabling data augmentation during training, and a strong under-sampling of the 3D surface with good classification performance.

CNN-Based Classification of Craniosynostosis Using 2D Distance Maps.

Craniosynostosis is a condition associated with the premature fusion of skull sutures affecting infants. 3D photogrammetric scans are a promising alternative to computed tomography scans in cases of single suture or nonsyndromic synostosis for diagnostic imaging, but oftentimes diagnosis is not automated and relies on additional cephalometric measure-ments and the experience of the surgeon. We propose an alternative representation of the infant's head shape created from 3D photogrammetric surface scans as 2D distance maps.

A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling.

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet.

Knowledge Acquisition and Construction of a RDF-Ontology for Computer-Assisted Surgery.

The integration of surgical knowledge into virtual planning systems plays a key role in computer-assisted surgery. The knowledge is often implicitly contained in the implemented algorithms. However, a strict separation would be desirable for reasons of maintainability, reusability and readability.

eHealth and Clinical Documentation Systems.

eHealth is the use of modern information and communication technology (ICT) for trans-institutional healthcare purposes. Important subtopics of eHealth are health data sharing and telemedicine. Most of the clinical documentation to be shared is collected in patient records to support patient care.

On the Construction of Multilingual Corpora for Clinical Text Mining.

The amount of digital data derived from healthcare processes have increased tremendously in the last years. This applies especially to unstructured data, which are often hard to analyze due to the lack of available tools to process and extract information. Natural language processing is often used in medicine, but the majority of tools used by researchers are developed primarily for the English language.

Electromagnetic navigated condylar positioning after high oblique sagittal split osteotomy of the mandible: a guided method to attain pristine temporomandibular joint conditions.

Objectives: Reproduction of the exact preoperative proximal-mandible position after osteotomy in orthognathic surgery is difficult to achieve. This clinical pilot study evaluated an electromagnetic (EM) navigation system for condylar positioning after high-oblique sagittal split osteotomy (HSSO).

Study Design: After HSSO as part of 2-jaw surgery, the position of 10 condyles was intraoperatively guided by an EM navigation system.

Can electromagnetic-navigated maxillary positioning replace occlusional splints in orthognathic surgery? A clinical pilot study.

Introduction: Because of the inaccuracy of intermaxillary splints in orthognathic surgery, intraoperative guidance via a real time navigation system might represent a suitable method for enhancing the precision of maxillary positioning. Therefore, in this clinical trial, maxillary repositioning after Le Fort I osteotomy was guided splintless by an electromagnetic navigation system.

Materials And Methods: Conservatively planned maxillary reposition in each of 5 patients was transferred to a novel software module of the electromagnetic navigation system.

Computer assisted positioning of the proximal segment after sagittal split osteotomy of the mandible: Preclinical investigation of a novel electromagnetic navigation system.

Introduction: Modifications of the temporomandibular joint position after mandible osteotomy are reluctantly accepted in orthognathic surgery. To tackle this problem, we developed a new navigation system using miniaturized electromagnetic sensors. Our imageless navigation approach is therefore optimized to avoid complications of previously proposed optical approaches such as the interference with established surgical procedures and the line of sight problem.

Electromagnetic navigated positioning of the maxilla after Le Fort I osteotomy in preclinical orthognathic surgery cases.

Objectives: Inaccuracies in orthognathic surgery can be caused during face-bow registration, model surgery on plaster models, and intermaxillary splint manufacturing. Electromagnetic (EM) navigation is a promising method for splintless digitized maxillary positioning.

Study Design: After performing Le Fort I osteotomy on 10 plastic skulls, the target position of the maxilla was guided by an EM navigation system.

Approach to intraoperative electromagnetic navigation in orthognathic surgery: A phantom skull based trial.

Introduction: Intraoperative guidance using electromagnetic navigation is an upcoming method in maxillofacial surgery. However, due to their unwieldy structures, especially the line-of-sight problem, optical navigation devices are not used for daily orthognathic surgery. Therefore, orthognathic surgery was simulated on study phantom skulls, evaluating the accuracy and handling of a new electromagnetic tracking system.

Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.

Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells.

Pre- and intraoperative processing and integration of various anatomical and functional data in neurosurgery.

A software system is presented, capable of integrating various information sources for neurosurgical procedures. These include anatomical data such as a standard 3D DICOM image stacks, atlas data, as well as functional information (e.g.