TY - JOUR AB - BACKGROUND AND OBJECTIVE: Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information. METHODS: In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information. RESULTS: Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m2 [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage. CONCLUSIONS: The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare. AU - Alkhodari, M.* AU - Khandoker, A.H.* AU - Jelinek, H.F.* AU - Karlas, A. AU - Soulaidopoulos, S.* AU - Arsenos, P.* AU - Doundoulakis, I.* AU - Gatzoulis, K.A.* AU - Tsioufis, K.* AU - Hadjileontiadis, L.J.* C1 - 70250 C2 - 55467 CY - Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland TI - Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images. JO - Comput. Meth. Programs Biomed. VL - 248 PB - Elsevier Ireland Ltd PY - 2024 SN - 0169-2607 ER - TY - JOUR AB - BACKGROUND AND OBJECTIVE: Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the efficient detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. METHODS: We present MemBrain - a new deep learning-aided pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. RESULTS: MemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. CONCLUSIONS: MemBrain is a powerful and annotation-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain. AU - Lamm, L. AU - Righetto, R.D. AU - Wietrzynski, W. AU - Pöge, M.* AU - Martinez-Sanchez, A.* AU - Peng, T. AU - Engel, B.D. C1 - 65766 C2 - 52906 TI - MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms. JO - Comput. Meth. Programs Biomed. VL - 224 PY - 2022 SN - 0169-2607 ER - TY - JOUR AB - Background and Objectives: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson's disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. Methods: We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. Results: The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman's rho =0.62, P=0.004). Conclusion: In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages. AU - van Veen, R.* AU - Meles, S.K.* AU - Renken, R.J.* AU - Reesink, F.E.* AU - Oertel, W.H. AU - Janzen, A.* AU - de Vries, G.J.* AU - Leenders, K.L.* AU - Biehl, M.* C1 - 65916 C2 - 52980 TI - FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder. JO - Comput. Meth. Programs Biomed. VL - 225 PY - 2022 SN - 0169-2607 ER - TY - JOUR AB - BACKGROUND AND OBJECTIVE: Fetal magnetoencephalography (fMEG) is a method for recording fetal brain signals, fetal and maternal heart activity simultaneously. The identification of the R-peaks of the heartbeats forms the basis for later heart rate (HR) and heart rate variability (HRV) analysis. The current procedure for the evaluation of fetal magnetocardiograms (fMCG) is either semi-automated evaluation using template matching (SATM) or Hilbert transformation algorithm (HTA). However, none of the methods available at present works reliable for all datasets. METHODS: Our aim was to develop a unitary, responsive and fully automated R-peak detection algorithm (FLORA) that combines and enhances both of the methods used up to now. RESULTS: The evaluation of all methods on 55 datasets verifies that FLORA outperforms both of these methods as well as a combination of the two, which applies in particular to data of fetuses at earlier gestational age. CONCLUSION: The combined analysis shows that FLORA is capable of providing good, stable and reproducible results without manual intervention. AU - Sippel, K. AU - Moser, J. AU - Schleger, F. AU - Preissl, H. AU - Rosenstiel, W.* AU - Spüler, M.* C1 - 55711 C2 - 46466 SP - 35-41 TI - Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data. JO - Comput. Meth. Programs Biomed. VL - 173 PY - 2019 SN - 0169-2607 ER - TY - JOUR AB - In this paper, we describe the ‘Telemedicine Benchmark’ (TMB), which is a set of standard procedures, protocols and measurements to test reliability and levels of performance of data exchange in a telemedicine session. We have put special emphasis on medical imaging, i.e. digital image transfer, joint viewing and editing and 3D manipulation. With the TMB, we can compare the aptitude of different video conferencing software systems for telemedicine issues and the effect of different network technologies (ISDN, xDSL, ATM, Ethernet). The evaluation criteria used are length of delays and functionality. For the application of the TMB, a data set containing radiological images and medical reports was set up. Considering the Benchmark protocol, this data set has to be exchanged between the partners of the session. The Benchmark covers file transfer, whiteboard usage, application sharing and volume data analysis and compression. The TMB has proven to be a useful tool in several evaluation issues. AU - Klutke, P.J. AU - Mattioli, P.* AU - Baruffaldi, F.* AU - Toni, A.* AU - Englmeier, K.-H. C1 - 21029 C2 - 19247 SP - 133-141 TI - The telemedicine benchmark: a general tool to measure and compare the performance of videoconferencing equipment in the telemedicine area. JO - Comput. Meth. Programs Biomed. VL - 60 IS - 2 PY - 1999 SN - 0169-2607 ER - TY - JOUR AB - The main aim of this study was to find out if the image format (TIFF or JPEG) influenced the time delay for transferring radiological images by the application sharing tool of a desktop videoconferencing system. The second task of the study was to define a procedure that optimized the time delay to load and remotely visualize the images. The results were achieved by applying a test procedure called ‘benchmark protocol’. The videoconferencing system used for the test was Intel ProShare 200™ v2.0. The image transfer was performed by a BRI ISDN connection. We showed that the image format had no significant influence on the time delay. We presented an optimal procedure for image transfer. Furthermore, store and forward procedures with simple file transfer were shown to be inferior to the use of application sharing. For radiological image transfer we recommend to use lossless file formats and application sharing with the image already loaded in because this method achieves the lowest time delays. AU - Mattioli, P.* AU - Klutke, P.J. AU - Baruffaldi, F.* AU - Viceconti, M.* AU - Toni, A.* AU - Englmeier, K.-H. C1 - 20827 C2 - 19251 SP - 89-97 TI - A study of the application sharing capabilities in telemedicine. JO - Comput. Meth. Programs Biomed. VL - 58 IS - 2 PY - 1999 SN - 0169-2607 ER - TY - JOUR AB - Investigation on the applicability of low-cost videoconferencing (VC) for health care services is becoming a real need. Reduced resources drive the administrators to evaluate inexpensive solutions for telemedicine. Considering this scenario, this work is a preliminary step to validate, from a technical point of view, if low-cost VC systems could be suitable for orthopaedic teleconsulting services. For this purpose, four different videoconferencing systems were tested. Each VC system was composed of a computer and a VC device installed in. VC devices were chosen among the most popular and distributed products (made by Intel, PictureTel and Aethra). The Telemedicine Benchmark, a specific tool defined by the authors, was applied to measure the overall systems performances in terms of time delays during basic rate ISDN connections (128 Kbit/s). Results showed that it is possible to apply low-cost videoconferencing systems for orthopaedic teleconsulting services. Most of the systems provided acceptable performance for medical image visualization and real time joint working. Further developments are recommendable to enhance the VC software tools capabilities and to improve software-user interface. AU - Mattioli, P.* AU - Klutke, P.J. AU - Baruffaldi, F.* AU - Villar-Guzman, A.* AU - Toni, A.* AU - Englmeier, K.-H. C1 - 21480 C2 - 19601 SP - 143-152 TI - Technical validation of low-cost videoconferencing systems applied in orthopaedric teleconsulting services. JO - Comput. Meth. Programs Biomed. VL - 60 IS - 2 PY - 1999 SN - 0169-2607 ER - TY - JOUR AB - DIABCARD provides the specification for the core of a Chip Card Based Medical Information System (CCMIS) for the treatment of patients with chronic diseases. It will provide an instrument for assessing health care services, improve the links between health care providers and set up communication between the different levels of health care. It will therefore improve the quality of care and thus the life of patients with chronic diseases. DIABCARD concentrates on diabetes at the moment, the concept of the diabetes chip card will, however, be extendable to other chronic diseases. | DIABCARD provides the specification for the core of a Chip Card Based Medical Information System (CCMIS) for the treatment of patients with chronic diseases. It will provide an instrument for assessing health care services, improve the links between health care providers and set up communication between the different levels of health care. It will therefore improve the quality of care and thus the life of patients with chronic diseases. DIABCARD concentrates on diabetes at the moment, the concept of the diabetes chip card will, however, be extendable to other chronic diseases. AU - Engelbrecht, R. AU - Hildebrand, C. AU - Kühnel, E. AU - Brenner, G.* AU - Corcoy, R.M.* AU - Eberhard, G.* AU - Gapp, C.* AU - Klepser, G.* AU - De Leiva, A.* AU - Massi-Benedetti, M.* AU - Mechtler, R.* AU - Piwernetz, K.* AU - Sembritzki, J.* AU - Thiery, J.* C1 - 33250 C2 - 38948 SP - 33-35 TI - A chip card for patients with diabetes. JO - Comput. Meth. Programs Biomed. VL - 45 IS - 1-2 PY - 1994 SN - 0169-2607 ER - TY - JOUR AB - A graphical method for the description of the spatial extension and temporal development of muscular weakness in neurological disorders implemented on a personal computer is described. Different degrees of paresis of individual muscle groups are represented by distinct grey tone values or colors in a semi-anatomic scheme. This representation provides a rapid recognition of essential features of the clinical syndrome, such as the pattern of muscular weakness and its temporal development. In parallel to the results of force testing, the results of other investigations in the same muscle groups can also be presented by the graphical method. | A graphical method for the description of the spatial extension and temporal development of muscular weakness in neurological disorders implemented on a personal computer is described. Different degrees of paresis of individual muscle groups are represented by distinct grey tone values or colors in a semi-anatomic scheme. This representation provides a rapid recognition of essential features of the clinical syndrome, such as the pattern of muscular weakness and its temporal development. In parallel to the results of force testing, the results of other investigations in the same muscle groups can also be presented by the graphical method. AU - Lipinski, H.G. AU - Kuether, G. C1 - 40830 C2 - 11109 SP - 69-73 TI - Graphical visualization of the pattern of muscular weakness in neuromuscular diseases. JO - Comput. Meth. Programs Biomed. VL - 34 IS - 1 PY - 1991 SN - 0169-2607 ER -