In ihrem 50. Jubiläumsjahr lud die Technische Universität Kaiserslautern am 26. und 27. November 2020 zu einem Höhepunkt ein: dem Kongress #Sport #Gesundheit #Digital. Für zwei Tage wurden im Rahmen eines Online-Forums gemeinsam die Themenfelder Sport, Gesundheit und Digitalisierung diskutiert. Wir freuen uns sehr, dass die Techniker Krankenkasse die TUK als Ausrichter der Veranstaltung besonders unterstützt hat. #SGD – Der Kongress setzte an der Schnittstelle von Sport, Gesundheit und Digitalisierung an und beleuchtete Chancen und Möglichkeiten, die durch das Zusammenspiel dieser Disziplinen entstehen können. Gleichzeitig wurden Risiken und Herausforderungen der digitalen Entwicklungen in Sport und Gesundheit betrachtet und perspektivisch mit Blick in die Zukunft analysiert. Hochkarätige Beiträge aus Wissenschaft und Praxis aus allen für das Themenspektrum relevanten Fachrichtungen sorgten für ein hohes Maß an Abwechslung und Transfer. Der Kongress richtete sich dabei nicht nur an Personen aus Wissenschaft und Praxis der Bereiche Gesundheitswesen und -management, Medizin und Psychologie. Ebenso angesprochen wurden Übungsleitende und Angehörige aus Hochschulsport und Sportwissenschaft, Studierende und Mitarbeitende aller bezogenen Fachrichtungen sowie alle allgemein interessierten Personen. Der vorliegende Kongressband stellt die Sammlung der Kongressinhalte dar. Neben den schriftlichen Beiträgen lassen sich hier auch Impressionen der Kongresstage und die Vorträge als interaktiv eingebundene Videos finden.

Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy Macc = 100%), followed by features based on simple descriptive statistics (Macc = 97.38%) and waveform data (Macc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.