05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
Browse
Search Results
Item Open Access Improving the accuracy of musculotendon models for the simulation of active lengthening(2023) Millard, Matthew; Kempter, Fabian; Stutzig, Norman; Siebert, Tobias; Fehr, JörgVehicle accidents can cause neck injuries which are costly for individuals and society. Safety systems could be designed to reduce the risk of neck injury if it were possible to accurately simulate the tissue-level injuries that later lead to chronic pain. During a crash, reflexes cause the muscles of the neck to be actively lengthened. Although the muscles of the neck are often only mildly injured, the forces developed by the neck’s musculature affect the tissues that are more severely injured. In this work, we compare the forces developed by MAT_156, LS-DYNA’s Hill-type model, and the newly proposed VEXAT muscle model during active lengthening. The results show that Hill-type muscle models underestimate forces developed during active lengthening, while the VEXAT model can more faithfully reproduce experimental measurements.Item Open Access Deep learning aided clinical decision support(2023) Schneider, Rudolf; Staab, Steffen (Prof. Dr.)Medical professionals create vast amounts of clinical texts during patient care. Often, these documents describe medical cases from anamnesis to the final clinical outcome. Automated understanding and selection of relevant medical records pose an opportunity to assist medical doctors in their day-to-day work on a large scale. However, clinical text understanding is challenging, especially when dealing with clinical narratives such as nursing notes or diagnostic reports. These clinical documents differ extensively in length, structure, vocabulary, and lexical and grammatical correctness. In addition, they are highly context-dependent. For all these reasons, approaches based on syntactic rules and discrete text representation often fail to address the variety of clinical narratives propagating unrecoverable errors to downstream applications. Therefore, this thesis focuses on evaluating and designing methods and models that are generalizable and adaptable enough to deal with these challenges. Our goal is to enable text-based clinical decision support systems to utilize the knowledge from clinical archives and medical publications. We aim to design methods that can scale up to the growing amount of clinical documents in hospital archives. A fundamental problem in achieving deep-learning-enabled clinical decision support systems is designing a patient representation that captures all relevant information for automated processing. We engage these challenges by designing a framework for deep-learning-enabled differential diagnosis support. Guided by the needs emerging from this framework, we design and evaluate methods based on three information representation paradigms: (1) Discrete relation extraction using the open information extraction paradigm. (2) Neural text representations based on language and topic modeling. (3) Combining complementary neural text representations. Our framework translates clinical diagnostic steps and pathways to statistical and deep-learning-based models. Accordingly, we can show that deep-learning-enabled differential diagnosis benefits from contextualized information representations. Further, we identify shortcomings of the open information extraction paradigm in a comprehensive benchmark. We design a distributed text representation model based on topical information. Our extensive large-scale experiment results show that topical distributed text representations capture information complementary to language modeling-based approaches across domains, thus enabling a holistic text representation for medical texts. Our experiments with medical doctors using our prototypical implementation of the deep-learning-enabled differential diagnosis process validate this framework. Moreover, we identify seven crucial design challenges for text-based clinical decision support systems based on our qualitative and quantitative findings.Item Open Access Avoiding shortcut-learning by mutual information minimization in deep learning-based image processing(2023) Fay, Louisa; Cobos, Erick; Yang, Bin; Gatidis, Sergios; Küstner, ThomasItem Open Access Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study(2023) Fischer, Marc; Küstner, Thomas; Pappa, Sofia; Niendorf, Thoralf; Pischon, Tobias; Kröncke, Thomas; Bette, Stefanie; Schramm, Sara; Schmidt, Börge; Haubold, Johannes; Nensa, Felix; Nonnenmacher, Tobias; Palm, Viktoria; Bamberg, Fabian; Kiefer, Lena; Schick, Fritz; Yang, BinIn this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.Item Open Access Cervical muscle reflexes during lateral accelerations(2023) Millard, Matthew; Hunger, Susanne; Broß, Lisa; Fehr, Jörg; Holzapfel, Christian; Stutzig, Norman; Siebert, TobiasAutonomous vehicles will allow a variety of seating orientations that may change the risk of neck injury during an accident. Having a rotated head at the time of a rear-end collision in a conventional vehicle is associated with a higher risk of acute and chronic whiplash. The change in posture affects both the movement of the head and the response of the muscles. We are studying the reflexes of the muscles of the neck so that we can validate the responses of digital human body models that are used in crash simulations. The neck movements and muscle activity of 21 participants (11 female) were recorded at the Stuttgart FKFS mechanical driving simulator. During the maneuver we recorded the acceleration of the seat and electromyographic (EMG) signals from the sternocleidomastoid (STR) muscles using a Biopac MP 160 system (USA). As intuition would suggest, the reflexes of the muscles of the neck are sensitive to posture and the direction of the acceleration.Item Open Access A muscle model for injury simulation(2023) Millard, Matthew; Kempter, Fabian; Fehr, Jörg; Stutzig, Norman; Siebert, TobiasCar accidents frequently cause neck injuries that are painful, expensive, and difficult to simulate. The movements that lead to neck injury include phases in which the neck muscles are actively lengthened. Actively lengthened muscle can develop large forces that greatly exceed the maximum isometric force. Although Hill-type models are often used to simulate human movement, this model has no mechanism to develop large tensions during active lengthening. When used to simulate neck injury, a Hill model will underestimate the risk of injury to the muscles but may overestimate the risk of injury to the structures that the muscles protect. We have developed a musculotendon model that includes the viscoelasticity of attached crossbridges and has an active titin element. In this work we evaluate the proposed model to a Hill model by simulating the experiments of Leonard et al. [1] that feature extreme active lengthening.