Abstract:

Introduction: 3D surface scan-based diagnosis of craniosynostosis is a promising radiation-free alternative to traditional diagnosis using computed tomography. The cra- nial index (CI) and the cranial vault asymmetry index (CVAI) are well-established clinical parameters that are widely used. However, they also have the benefit of being easily adaptable for automatic diagnosis without the need of extensive prepro- cessing.Methods: We propose a multi-height-based classification ap- proach that uses CI and CVAI in different height layers and compare it to the initial approach using only one layer. We use ten-fold cross-validation and test seven different classi- fiers. The dataset of 504 patients consists of three types of craniosynostosis and a control group consisting of healthy and non-synostotic subjects.Results: The multi-height-based approach improved classifica- tion for all classifiers. The k-nearest neighbors classifier scored best with a mean accuracy of 89 % and a mean F1-score of 0.75.Conclusion: Taking height into account is beneficial for the classification. Based on accepted and widely used clinical pa- rameters, this might be a step towards an easy-to-understand and transparent classification approach for both physicians and patients.

Abstract:

The electrocardiogram (ECG) is a standard cost-efficient and non-invasive tool for the early detection of various cardiac diseases. Quantifying different timing and amplitude features of and in between the single ECG waveforms can reveal important information about the underlying (dys-)function of the heart. Determining these features requires the detection of fiducial points that mark the on- and offset as well as the peak of each ECG waveform (P wave, QRS complex, T wave). Manually setting these points is time-consuming and requires a physician’s expert knowledge. Therefore, the highly modular ECGdeli toolbox for MATLAB was developed, which is capable of filtering clinically recorded 12-lead ECG signals and detecting the fiducial points, also called delineation. It is one of the few open toolboxes offering ECG delineation for P waves, T Waves and QRS complexes. The algorithms provided were evaluated with the QT database, an ECG database comprising 105 signals with fiducial points annotated by clinicians. The median difference between the fiducial points set by the boundary detection algorithm and the clinical annotations serving as a ground truth is less than 4 samples (16 ms) for the P wave and the QRS complex markers.

Abstract:

The most common arrhythmia worldwide is atrial fibrillation (AF), recognized as a substantialpublic health burden due to its rising incidence. Patients affected by AF face elevated risksfor stroke, myocardial infarction, and mortality. Moreover, current treatment approachesoften prove ineffective, resulting in a high recurrence rate. Hence, there is an urgent need forfurther investigation into the mechanisms underlying AF to advance treatment strategies.The objective of this study was to assess the impact of the morphology of the conductionvelocity (CV) restitution curve on reentry events. We evaluated this influence using metricssuch as the vulnerability window, the average reentry duration, and the dominant frequency.By conducting this vulnerability assessment, the aim was to establish correlations betweenthe morphology of the CV restitution curve and these key features.We investigated the impact of using the pacing cycle length (PCL) and the diastolic interval(DI) on the restitution curve through simulations in the monodomain model. Additionally, theinfluence of the maximum longitudinal CV on the CV restitution curve was analyzed. ClinicalCV restitution curves of 13 patients with persistent AF, measured at various atrial locations,were employed in simulations on a 2D tissue slab utilizing the diffusion reaction eikonalalternant model (DREAM) to simulate electrical wave propagation with a personalizedionic model (Courtemanche) for the action potential (AP). The vulnerability assessmentwas done using an S1-S2 protocol. The experiments encompassed diverse morphologies ofrestitution curves and varying maximal longitudinal CV values. Moreover, experiments withheterogeneous meshes using two different restitution curve morphologies were conducted.No notable influence of the maximal longitudinal CV on the morphology of the CV restitutioncurve was identified. Moreover, the ionic model was successfully personalized using afunction that interpolated conductance values between healthy and AF tissue. Additionally, acorrelation between the steepness of the CV restitution curve and the vulnerability window,average reentry duration, and dominant frequency was established.Nevertheless, this work has limitations regarding the data acquisition and the model usedfor the electrophysiological simulations although it was shown that a shallow CV restitutioncurve is more vulnerable to AF and maintains it longer. Summarizing, the CV restitutioncurve proved to be a crucial factor for reentry events, promising to improve vulnerabilityassessment and treatment outcomes of AF.

Abstract:

The early and correct differentiation between the types of cranial deformities affecting young children is particularly important, as it increases the chances of successful treatment. To assist physicians in their diagnosis and to make examinations more patient-friendly in clinical practice, there are some successful classification approaches based on neuronal networks. Although these approaches provide promising results, their medical acceptance is rather low due to their difficult explainability. To increase medical acceptance, an explainable Decision Tree Classifier is investigated in this study with regard to its applicability in clinical practice. For this, we trained and tested the classifier with essentially three different types of input features. First, we investigated the classification based on clinical indices (Cranial Index and Cranial Vault Asymmetry Index) and their underlying feature points, which are widely used in clinical practice. In addition, we evaluated an icosphere approach based on feature points uniformly distributed on the head. While the first approach, based on clinical indices, considers insufficiently representative points for classification, the second approach has limited applicability in clinical practice. Therefore, we developed our own layer approach, which consisted of four layers at different heights of the head. Each layer contained the eight feature points that would correspond to the Cranial Index and Cranial Vault Asymmetry points of the respective layer. We evaluated the performance of all three approaches using the performance parameters accuracy, balanced accuracy and F1 score. From this, we found that the decision tree that classified based on features of the layer approach gave the best results. However, the classification using a Linear Discriminant Analysis Classifier could not be outperformed. To reduce the measurement effort for our self-designed approach in clinical practice, we evaluated which feature points of the layer approach were most relevant. With a reduction of the measurement effort by 50%, the optimized layer approach only led to a slight deterioration of the investigated performance parameters. To investigate the effects of measurement errors on classification, we tested the Decision Tree Classifier with features of varying degrees of noise. We found that even in the presence of strong noise, the classifications based on the optimized layer approach were only slightly worse than for the unnoised features. Since our final approach is explainable, known measurement methods can be used, the measurement effort barely increases and the performance improves strongly while being resistant to measurement errors, we see good chances that this approach can support physicians in their diagnoses of cranial deformities.