Total knee arthroplasty (TKA) patients may present with genetic deformities, such as trochlear dysplasia, or deformities related to osteoarthritis. This pathologic morphology should be corrected by TKA to compensate for related functional deficiencies. Hence, a reconstruction of an equivalent physiological knee morphology would be favorable for detailed preoperative planning and the patient-specific implant selection or design process. A parametric database of 673 knees, each described by 36 femoral parameter values, was used. Each knee was classified as pathological or physiological based on cut-off values from literature. A clinical and a mathematical classification approach were developed to distinguish between affected and unaffected parameters. Three different prediction methods were used for the restoration of physiological parameter values: regression, nearest neighbor search and artificial neural networks. Several variants of the respective prediction model were considered, such as different network architectures. Regarding all methods, the model variant chosen resulted in a prediction error below the parameters' standard deviation, while the regression yielded the lowest errors. Future analyses should consider other deformities, also of tibia and patella. Furthermore, the functional consequences of the parameter changes should be analyzed.

The effect of the presence of an air bubble, inside an infusion line, on the time (Tnew) needed for a new medication to reach the patient after a syringe exchange was studied in this paper. If an air bubble escapes through an air filter, then a sudden drop in pressure occurs, causing a relaxation of the compressible part of the syringe, followed by a gradual restoration of the flow rate in the line. We modeled this phenomenon mathematically and measured it experimentally in vitro. In an example with a pump flow rate of 5mL/h and an air bubble of 1cm length inside an infusion line (diameter 1mm) with an air filter, both theory and experiment yield an additional increase of at least 600% in delay time if a naive estimate (based on the size of the bubble alone) is replaced by a more realistic estimate incorporating compressibility. Furthermore, we show that an air bubble in a line without air filter may increase Tnew by a factor 2, depending on the initial position of the air bubble. We conclude that an air bubble in an infusion line causes delays that may not be expected by health care professionals.

Infusion therapy is the most common form of therapy used in health care. However, the existing infusion devices show higher flow discrepancies as flow rates decrease to a few nLmin-1. As a result, dosing errors can contribute to the morbidity and mortality of patients. In thescope of project 18HLT08 MeDD II- Metrology for drugdelivery, this investigation aims at the development of a silicon microchip flow pump capable of steadily and continuously dispense very low flow rates of a few nLmin-1. The fabrication methodologies explored here use a combination of typical cleanroom micro/nanofabrication techniques and off-the-shelf equipment. Preliminary tests show flow rates as low as 45nLmin-1 can be obtained in this microfluidic electroosmotic pump. The experimental flow rates are in good agreement with results predicted bymultiphysics simulation, with less than 8% deviation ratio. This cost effective electroosmotic micropump has the potential to act as a steady and continuous drug delivery system to neonatal patients as well as to organs on chip (OoC), determining the stability of the shear stress imposed on the cells or the right cell culture medium conditions.

Article Abstracts of the 2022 Joint Annual Conference of the Austrian (ÖGBMT), German (VDE DGBMT) and Swiss (SSBE) Societies for Biomedical Engineering, including the 14th Vienna International Workshop on Functional Electrical Stimulation was published on August 1, 2022 in the journal Biomedical Engineering / Biomedizinische Technik (volume 67, issue s1).

The incidence of vision impairment is rapidly increasing. Diagnosis and classifying retinal abnormalities in ophthalmological applications is a significant challenge. Using Optical Coherence Tomography (OCT), the study aims to develop a computer aided diagnosis system for detecting and classifying retinal disorders. Choroidal neovascularization, diabetic macular edema, drusen, and normal cases are the investigated groups. Both deep learning and machine learning are combined to build the system. The SqueezeNet neural network was modified toextract features. The Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) algorithms were used for disorder classification. The Bayesian optimization technique was also used to determine the best hyperparameters for each model. The model' performance was evaluated through nine criteria using 12,000 OCT images. The results have demonstrated accuracies of 97.39, 97.47, 96.98, and 95.25% for the SVM, K-NN, DT, and EM, respectively. When results are compared to relevant studies in terms of accuracy andtested samples, they show superior performance. As aresult, a novel computer-aided diagnosis system for detecting and classifying retinal diseases has been developed, reducing human error while also saving time.

Abstract Many researchers have measured the differences in electroencephalography (EEG) while viewing 2D and 3D movies to uncover the neuromechanism underlying distinct viewing experiences. Using whole-brain network analyses of scalp EEG, our previous study reported that beta and gamma bands presented higher global efficiencies while viewing 3D movies. However, scalp EEG is influenced by volume conduction, not allowing inference from a neuroanatomy perspective; thus, source reconstruction techniques are recommended. This paper is the first to measure the differences in the frontal-occipital networks in EEG source space during 2D and 3D movie viewing. EEG recordings from 40 subjects were performed during 2D and 3D movie viewing. We constructed frontal-occipital networks of alpha, beta, and gamma bands in EEG source space and analyzed network efficiencies. We found that the beta band exhibited higher global efficiency in 3D movie viewing than in 2D movie viewing; however, the alpha global efficiency was not statistically significant. In addition, a support vector machine (SVM) classifier, taking functional connectivities as classification features, was built to identify whether the frontal-occipital networks contain patterns that could distinguish 2D and 3D movie viewing. Using the 6 most important functional connectivity features of the beta band, we obtained the best accuracy of 0.933. Our findings shed light on uncovering the neuromechanism underlying distinct experiences while viewing 2D and 3D movies.

Disturbances of the cardiac conduction system causing reentry mechanisms above the atrioventricular (AV) node are induced by at least one accessory pathway with different conducting properties and refractory periods. This work aims to further develop the already existing and continuously expanding Offenburg heart rhythm model to visualise the most common supraventricular reentry tachycardias to provide a better understanding of the cause of the respective reentry mechanism.

In this work an enhanced wide range dual band spectral domain optical coherence tomography technique (SD-OCT) is presented to increase the depth and accuracy of the measurement of optical A-scan biometry. The setup uses a Michelson interferometer with two wide-spectrum Superluminescent Diodes (SLD). The emissions of the SLDs are filtered by a long-pass filter (900nm) in front of the reference mirror. The light is spectrally decomposed using a single reflective diffraction grating (1,800 lines/mm) and the whole spectrum captured with two CCD line sensors. The capabilities of the system have been validated using a self-made human model eye.