Inequality persists in a large citizen science programme despite increased participation through ICT innovations.

Biological recording is a prominent and widely practised form of citizen science, but few studies explore long-term demographic trends in participation and knowledge production. We studied long-term demographic trends of age and gender of participants reporting to a large online citizen science multi-taxon biodiversity platform ( www.artportalen.se ). Adoption by user communities and continually developing Information and Communications Technologies (ICTs) greatly increased the number of participants reporting data, but profound long-term imbalances in gender contribution across species groups persisted over time. Reporters identifying as male dominated in numbers, spent more days in the field reporting and reported more species on each field day. Moreover, an age imbalance towards older participants amplified over time. As the first long-term study of citizen participation by age and gender, our results show that it is important for citizen science project developers to account for cultural and social developments that might exclude participants, and to engage with underrepresented and younger participants. This could facilitate the breadth of engagement and learning across a larger societal landscape, ensure project longevity and biodiversity data representation (e.g. mitigate gender bias influence on the number of reports of different species groups).

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). Three two-dimensional images were extracted from each photogrammetry scan. The datasets were divided into training, validation, and test sets. During the training, fine-tuned ResNet-152s were utilized. The performance was quantified using tenfold cross-validation. For the detection of craniosynostosis, sensitivity was at 0.94 with a specificity of 0.85. Regarding the differentiation of the five existing classes (trigonocephaly, scaphocephaly, positional plagiocephaly left, positional plagiocephaly right, and healthy), sensitivity ranged from 0.45 (positional plagiocephaly left) to 0.95 (scaphocephaly) and specificity ranged from 0.87 (positional plagiocephaly right) to 0.97 (scaphocephaly). We present a CNN-based approach to classify craniofacial deformities on two-dimensional images with promising results. A larger dataset would be required to identify rarer forms of craniosynostosis as well. The chosen 2D approach enables future applications for digital cameras or smartphones.

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. To validate the viability of the approach, a feasibility study was conducted on 164 simulated mandibular reconstructions. RESULTS: The ontology defines 244 different reconstruction variants and 80 analyses for optimization. In 146 simulated cases, a proposal could be automatically calculated (average time 8.79 ± 4.03 s). The assessments of the proposals by three clinical experts indicate the viability of the approach. CONCLUSIONS: Due to the modular separation between computational logic and domain knowledge, the developed concepts can be easily maintained, reused and adapted for other applications.

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. METHODS: The proposed distance maps sample 2D images from 3D surface scans using a coordinate transformation, ray casting, and distance extraction. We introduce a CNN-based classification pipeline and compare our classifier to alternative approaches on a dataset of 496 patients. We investigate into low-resolution sampling, data augmentation, and attribution mapping. RESULTS: Resnet18 outperformed alternative classifiers on our dataset with an F1-score of 0.964 and an accuracy of 98.4%. Data augmentation on 2D distance maps increased performance for all classifiers. Under-sampling allowed 256-fold computation reduction during ray casting while retaining an F1-score of 0.92. Attribution maps showed high amplitudes on the frontal head. CONCLUSION: We demonstrated a versatile mapping approach to extract a 2D distance map from the 3D head geometry increasing classification performance, enabling data augmentation during training on 2D distance maps, and the usage of CNNs. We found that low-resolution images were sufficient for a good classification performance. SIGNIFICANCE: Photogrammetric surface scans are a suitable craniosynostosis diagnosis tool for clinical practice. Domain transfer to computed tomography seems likely and can further contribute to reducing ionizing radiation exposure for infants.

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. Methods: We tested the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)-based classification of craniosynostosis. The CNN is trained only on synthetic data but is validated and tested on clinical data. Results: The combination of an SSM and a GAN achieved an accuracy of 0.960 and an F1 score of 0.928 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources. Conclusions: Without a single clinical training sample, a CNN was able to classify head deformities with similar accuracy as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.

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. 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.

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. METHODS: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. RESULTS: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. CONCLUSION: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.

Making particularity travel: Trust and citizen science data in Swedish environmental governance.

This paper focuses on how particularities are performed and made to travel through the creation of trust. The Swedish Species Observation System (Artportalen) is one of the largest inscription and calculation centers for citizen data in the world, used extensively by public authorities in Sweden. Observations by members of the public become actionable through environmental governance laws in Sweden. These observations are made through networks of things and humans in which trust is created but unevenly distributed. Important for them to be trusted and to travel are such things as computer software to filter and map observations, red lists, GIS-tools to determine time and place, and validation committees. However, trust is more concentrated in a core set of actors, and there depends on interpersonal relations - though these relations are facilitated by other parts of the epistemic system.

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. 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.

Current Use of Sensor-Based Measurements for Paraplegics: A Literature Review.

Sensors are used in many fields to measure physical phenomena, often on mobile persons. Paraplegia is a disease with a massive impact on the ability to move, so patients show changes in walking behaviour or are even wheelchair users. It is unclear how sensors can be used with paraplegics to generate valid data for research. In the ParaReg project, such data shall be integrated with a medical register. In this paper, we elaborate current approaches for sensor measurements that might serve as an additional data source for ParaReg with the help of a literature review. We queried IEEE Xplore and MEDLINE to find publications that describe the current use of sensors for patients with paraplegia. The retrieved publications were screened for eligibility by two reviewers independently. To ensure reproducibility, we conducted an initial alignment. All publications were assessed based on their abstract. Inclusions were analysed for their major topic, and we proposed categories, which were statistically described afterwards. We retrieved 685 publications in total and included 287 publications in our analysis. The categories we found are: "diagnostic sensor tools for clinical environments", "seating position analysis", "functional electrical stimulation (FES) / neuroprosthesis control", "seating position analysis", "control systems for devices" and "assessment of physical activity". "FES / neuroprosthesis control" shows most publications and the highest publication rate in history, followed by "assessment of physical activity".

The lived experience of Huntington's disease: A phenomenological perspective on genes, the body and the lived experience of a genetic disease.

The purpose of this article is to explore the intersections between genes, the body and the lived experience of a genetic disease. This article is based on empirical material from a study on how individuals affected by Huntington's disease experience their everyday life. This study identified two themes that represent and capture the experience of the affected individuals. The themes are (1) noticing symptoms in everyday life and (2) neither health nor disease. The analysis of the empirical material was performed by employing a theoretical framework based on phenomenology. The findings of this study showed that the lived experiences among individuals affected by Huntington's disease were both fluid and dynamic in their nature. Furthermore, the analysis of the empirical material suggests that this fluid and dynamic character can be linked to a dimension that revolves around the intersections between genetics and the body. Following phenomenologist Drew Leder's outline of the divergence between the invisible and the visible features of the body, the analysis of the empirical material suggests that the mutated gene that causes Huntington's disease can be seen as a phenomenological nullpoint. It is important that the healthcare system acknowledges and addresses the lived experiences that are discussed in this article, particularly, as the use of genetics and genetic testing becomes more widespread usage within medicine.

Genetics and democracy-what is the issue?

Current developments in genetics and genomics entail a number of changes and challenges for society as new knowledge and technology become common in the clinical setting and in society at large. The relationship between genetics and ethics has been much discussed during the last decade, while the relationship between genetics and the political arena-with terms such as rights, distribution, expertise, participation and democracy-has been less considered. The purpose of this article is to demonstrate the connection between genetics and democracy. In order to do this, we delineate a notion of democracy that incorporates process as well as substance values. On the basis of this notion of democracy and on claims of democratisation in the science and technology literature, we argue for the importance of considering genetic issues in a democratic manner. Having established this connection between genetics and democracy, we discuss this relation in three different contexts where the relationship between genetics and democracy becomes truly salient: the role of expertise, science and public participation, and individual responsibility and distributive justice. As developments within genetics and genomics advance with great speed, the importance and use of genetic knowledge within society can be expected to grow. However, this expanding societal importance of genetics might ultimately involve, interact with, or even confront important aspects within democratic rule and democratic decision-making. Moreover, we argue that the societal importance of genetic development makes it crucial to consider not only decision-making processes, but also the policy outcomes of these processes. This argument supports our process and substance notion of democracy, which implies that public participation, as a process value, must be complemented with a focus on the effects of policy decisions on democratic values such as distributive justice.