Next‐generation phenotyping (NGP) is an application of advanced methods of computer vision on medical imaging data such as portrait photos of individuals with rare disorders. NGP on portraits results in gestalt scores that can be used for the selection of appropriate genetic tests, and for the interpretation of the molecular data. Here, we report on an exceptional case of a young girl that was presented at the age of 8 and 15 and enrolled in NGP diagnostics on the latter occasion. The girl had clinical features associated with Koolen‐de Vries syndrome (KdVS) and a suggestive facial gestalt. However, chromosomal microarray (CMA), Sanger sequencing, multiplex ligation‐dependent probe analysis (MLPA), and trio exome sequencing remained inconclusive. Based on the highly indicative gestalt score for KdVS, the decision was made to perform genome sequencing to also evaluate noncoding variants. This analysis revealed a 4.7 kb de novo deletion partially affecting intron 6 and exon 7 of the KANSL1 gene. This is the smallest reported structural variant to date for this phenotype. The case illustrates how NGP can be integrated into the iterative diagnostic process of test selection and interpretation of sequencing results.
For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research.
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