610 Medizin, Gesundheit
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Menopause is the permanent cessation of menstruation occurring naturally in women's aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flow reduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women's oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype. We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic.
Evaluation of a contactless accelerometer sensor system for heart rate monitoring during sleep
(2024)
The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain.
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively.
Mobile monitoring of outpatients during cancer therapy becomes possible through technological advancements. This study leveraged a new remote patient monitoring app for in-between systemic therapy sessions. Patients’ evaluation showed that the handling is feasible. Clinical implementation must consider an adaptive development cycle for reliable operations.
Motivation
In order to enable context-aware behavior of surgical assistance systems, the acquisition of various information about the current intraoperative situation is crucial. To achieve this, the complex task of situation recognition can be delegated to a specialized system. Consequently, a standardized interface is required for the seamless transfer of the recognized contextual information to the assistance systems, enabling them to adapt accordingly.
Methods
Our group analyzed four medical interface standards to determine their suitability for exchanging intraoperative contextual information. The assessment was based on a harmonized data and service model derived from the requirements of expected context-aware use cases. The Digital Imaging and Communications in Medicine (DICOM) and IEEE 11073 for Service-oriented Device Connectivity (SDC) were identified as the most appropriate standards.
Results
We specified how DICOM Unified Procedure Steps (UPS), can be used to effectively communicate contextual information. We proposed the inclusion of attributes to formalize different granularity levels of the surgical workflow.
Conclusions
DICOM UPS SOP classes can be used for the exchange of intraoperative contextual information between a situation recognition system and surgical assistance systems. This can pave the way for vendor-independent context awareness in the OR, leading to targeted assistance of the surgical team and an improvement of the surgical workflow.
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
This project aims to evaluate existing big data infrastructures for their applicability in the operating room to support medical staff with context-sensitive systems. Requirements for the system design were generated. The project compares different data mining technologies, interfaces, and software system infrastructures with a focus on their usefulness in the peri-operative setting. The lambda architecture was chosen for the proposed system design, which will provide data for both postoperative analysis and real-time support during surgery.
Introduction: Even if there is a standard procedure of CI surgery, especially in pediatric surgery surgical steps often differ individually due to anatomical variations, malformations or unforseen events. This is why every surgical report should be created individually, which takes time and relies on the correct memory of the surgeon. A standardized recording of intraoperative data and subsequent storage as well as text processing would therefore be desirable and provides the basis for subsequent data processing, e.g. in the context of research or quality assurance.
Method: In cooperation with Reutlingen University, we conducted a workflow analysis of the prototype of a semi-automatic checklist tool. Based on automatically generated checklists generated from BPMN models a prototype user interface was developed for an android tablet. Functions such as uploading photos and files, manual user entries, the interception of foreseeable deviations from the normal course of operations and the automatic creation of OP documentation could be implemented. The system was tested in a remote usability test on a petrous bone model.
Result: The user interface allows a simple intuitive handling, which can be well implemented in the intraoperative setting. Clinical data as well as surgical steps could be individually recorded and saved via DICOM. An automatic surgery report could be created and saved.
Summary: The use of a dynamic checklist tool facilitates the capture, storage and processing of surgical data. Further applications in clinical practice are pending.
Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained.
Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified.
Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks.
Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.
Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.