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Craniosynostosis is a congenital disease characterized by the premature closure of one or multiple sutures of the infant’s skull. For diagnosis, 3D photogrammetric scans are a radiation-free alternative to computed tomography. However, data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet been analyzed. In this work, we use a 2D distance map representation of the infants’ heads with a convolutional-neural-network-based classifier and employ a generative adversarial network (GAN) for data augmentation. We simulate two data scarcity scenarios with 15% and 10% training data and test the influence of different degrees of added synthetic data and balancing underrepresented classes. We used total accuracy and F1-score as a metric to evaluate the final classifiers. For 15% training data, the GAN-augmented dataset showed an increased F1-score up to 0.1 and classification accuracy up to 3 %. For 10% training data, both metrics decreased. We present a deep convolutional GAN capable of creating synthetic data for the classification of craniosynostosis. Using a moderate amount of synthetic data using a GAN showed slightly better performance, but had little effect overall. The simulated scarcity scenario of 10% training data may have limited the model’s ability to learn the underlying data distribution.

This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-based classification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, most SSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace- Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes.

Orthognathic surgery is used to treat misaligned jaws in adults by repositioning them. Intervention planning has to take into account different clinical, anatomical, functional, and aesthetic parameters to determine the optimal position. Current planning systems usually present 3D surface models obtained from a CT or Cone-Beam scan of the patient. The surgeon then interactively positions the upper and lower jaw. Thus, the surgeon can manually optimize anatomical aspects but has to consider functional and aesthetic effects simultaneously, which may be error-prone. We are developing a computer-assisted planning system which generates an optimized position for both jaws based on different analyses (cephalometric, plaster model, photostat) of the head, using a gradient descent algorithm. For this purpose, landmarks are interactively identified on a 3D surface representation. The system is developed as a plugin for MITK utilizing a knowledge base realized in the sematic web standard RDFS, which is queried with SPARQL requests. In a preliminary evaluation with five different cases we compare the automatically generated planning proposal with the planning results of a maxillofacial expert (ground truth). Good general agreement is observed, although more research for the identification and development of 3D cephalometric analyses is needed.

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. The algorithms are applied on top of an already existing deep learning-based classification pipeline. From six explain ability algorithms, four were implemented successfully and one yielded satisfactory results which might provide support to the radiologist. It was evident, that the areas relevant for the classification were highlighted, thus emphasizing the applicability of deep learning for classification of lung changes in CF patients. Using explainable concepts with deep learning could improve confidence of clinicians towards deep learning and introduction of more diagnostic decision support systems. [ABSTRACT FROM AUTHOR]

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. For developing and testing natural language processing methods, it is important to have a suitable corpus, specific to the medical domain that covers the intended target language. To improve the potential of natural language processing research, we developed tools to derive language specific medical corpora from publicly available text sources. n order to extract medicine-specific unstructured text data, openly available pub-lications from biomedical journals were used in a four-step process: (1) medical journal databases were scraped to download the articles, (2) the articles were parsed and consolidated into a single repository, (3) the content of the repository was de-scribed, and (4) the text data and the codes were released. In total, 93 969 articles were retrieved, with a word count of 83 868 501 in three different languages (German, English, and Spanish) from two medical journal databases Our results show that unstructured text data extraction from openly available medical journal databases for the construction of unified corpora of medical text data can be achieved through web scraping techniques. [ABSTRACT FROM AUTHOR]

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. fmri, MEG, EEG). The system is programmed in C++ using Open GL for visualisation, and was developed in a close cooperation with a neurosurgical department to match existing needs. Preoperatively the data may be combined, registered by a rigid or elastic matching process, enriched by user specified planning information such as annotations or trajectories, and visualised in a standard fashion using different segmentation schemes, interactive rotation, zooming etc. Selected portions of the gathered and generated information may then be exported for neuronavigation input in DICOM or a vendor specific format. Intraoperatively, this information may, on the one hand, be simply used as an integrational part of the routinely used navigational data. On the other hand, the system is also capable of interacting with the navigational system to integrate the actual spatial information of the ongoing procedure into the preoperative data, thus allowing further planning and visualisation beyond the scope of the navigational unit. Furthermore, intraoperatively updated information such as intraoperative MR images or electrophysiological data may be integrated and correlated to the existing information. Ongoing developments comprise redistribution of the relevant data for injection onto the screen of the navigational system or into the optical pathway of the (3D capable) microscope display/ocular. This also includes information about the actual automatically optimized accuracy of the navigation process in relation to the head markers in use. [ABSTRACT FROM AUTHOR]

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., Methods: Our new curriculum consists of three core components: Areas of Concentration that bundle elective courses in an important domain of medical informatics, a large catalog of elective courses, introductory/alignment courses for students without a bachelor's degree in medical informatics. We conducted an online survey of graduates and students with at least 75 credits to assess their opinion on the program's effectiveness and attractiveness., Results: Mandatory courses include clinical medicine, project management, research, and practical training in biomedical informatics. Five areas of concentration bundle elective courses for 30 credits to provide a solid foundation in an important domain in medical informatics. These are bioinformatics, data science, computer-aided diagnosis and therapy systems, information management, and software engineering in medicine. The catalog of electives offers a total of 67 courses. About 75% of them are assigned to more than one area of concentration. Our survey demonstrates that the participants highly appreciate the flexibility of the electives and the opportunity to develop an area of expertise., Conclusion: Offering a high degree of flexibility to our students has motivated them to join our program and resulted in a high level of student satisfaction. By designing the curriculum with areas of concentration and providing an infrastructure that permits courses on emerging topics to be added easily to the curriculum, we were able to meet our students' expectations., Competing Interests: The authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

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. The algorithms are applied on top of an already existing deep learning-based classification pipeline. From six explain ability algorithms, four were implemented successfully and one yielded satisfactory results which might provide support to the radiologist. It was evident, that the areas relevant for the classification were highlighted, thus emphasizing the applicability of deep learning for classification of lung changes in CF patients. Using explainable concepts with deep learning could improve confidence of clinicians towards deep learning and introduction of more diagnostic decision support systems.

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. Those 2D distance maps rely on ray casting to extract distances from a center point to the head surface, arranging them into a 2D image grid. We use the distance map for an original convolutional neural network (CNN)-based classification approach, which is evaluated on a publicly available synthetic dataset for benchmarking and also tested on clinical data. Qualitative differences of different head shapes can be ob-served in the distance maps. The CNN-based classifier achieves accuracies of 100 % on the publicly available synthetic dataset and 98.86 % on the clinical test set. Our distance map approach demonstrates the diagnostic value of 3D photogrammetry and the possibility of automatic, CNN-based diagnosis. Future steps include the improvement of the mapping method and testing the CNN on more pathologies.

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. Along with the Department of Oral and Maxillofacial Surgery at Heidelberg University Hospital, we are working on the development of a virtual planning system for mandibular reconstruction. In this work we describe a process for the structured acquisition and representation of surgical knowledge for mandibular reconstruction. Based on the acquired knowledge, an RDF(S) ontology was created. The ontology is connected to the virtual planning system via a SPARQL interface. The described process of knowledge acquisition can be transferred to other surgical use cases. Furthermore, the developed ontology is characterised by a reusable and easily expandable data model.

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. More sophisticated approaches to electronic patient records are trans-institutional or (inter-)national. Other aims for clinical documentation are quality management, reimbursement, legal issues, and medical research. Basic prerequisite for eHealth is interoperability, which can be divided into technical, semantic and process interoperability. There is a variety of international standards to support interoperability. Telemedicine is a subtopic of eHealth, which bridges spatial distance by using ICT for medical (inter-)actions. We distinguish telemedicine among healthcare professionals and telemedicine between health care professionals and patients. Both have a great potential to face the challenges of aging societies, the increasing number of chronically ill patients, multimorbidity and low number of physicians in remote areas. With ongoing digitalization more and more data are available digitally. Clinical documentation is an important source for big data analysis and artificial intelligence. The patient has an important role: Telemonitoring, wearable technologies, and smart home devices provide digital health data from daily life. These are high-quality data which can be used for medical decisions.

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. fmri, MEG, EEG). The system is programmed in C++ using Open GL for visualisation, and was developed in a close cooperation with a neurosurgical department to match existing needs. Preoperatively the data may be combined, registered by a rigid or elastic matching process, enriched by user specified planning information such as annotations or trajectories, and visualised in a standard fashion using different segmentation schemes, interactive rotation, zooming etc. Selected portions of the gathered and generated information may then be exported for neuronavigation input in DICOM or a vendor specific format. Intraoperatively, this information may, on the one hand, be simply used as an integrational part of the routinely used navigational data. On the other hand, the system is also capable of interacting with the navigational system to integrate the actual spatial information of the ongoing procedure into the preoperative data, thus allowing further planning and visualisation beyond the scope of the navigational unit. Furthermore, intraoperatively updated information such as intraoperative MR images or electrophysiological data may be integrated and correlated to the existing information. Ongoing developments comprise redistribution of the relevant data for injection onto the screen of the navigational system or into the optical pathway of the (3D capable) microscope display/ocular. This also includes information about the actual automatically optimized accuracy of the navigation process in relation to the head markers in use.