bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Underlying Gendered Factors Influencing Access to and Utilization of Skilled Birth Attendance (Sba): A Case Study in Ghana
Authors: Ambrose Naawa , Leonard Baatiema , Kor Jacob Aaworb-nang Maabobr , Sumah Anthony Nwinkaara , Zwanikken Prisca 
Published: July 2022

Maternal mortality continues to be a major public health concern in Ghana where women still die from preventable maternal deaths. Several factors contribute to the high maternal mortality in the country including delay in seeking skill birth attendance, sociocultural factors among others. Pursuance to the aforesaid, the study aimed at analyzing the role of gender in accessing skilled birth attendance in Ghana. A review of relevant literature was conducted to respond to the role of gender in accessing and utilizing skilled birth attendance. The search was conducted in Web of Science, Medline, PubMed, Academic Search Complete, and Cochrane database of systematic reviews including manual search of relevant literature in articles reference list. The 12 Centre for Evidence Based Management (CEBM) guidelines were used to assess the quality of the studies. The findings revealed a power play and a significant role of gender in accessing skilled birth attendance. Among the 10 studies included in this review, half of them were from northern Ghana whiles the other half in southern Ghana. Significant element discussed among the papers reviewed included male involvement, participation in decision making processes to seek skilled delivery and the effect of women social network. Men although appreciate the importance of skilled birth attendance, they are unable to provide the needed support to their partners during labour. The decision on the place of birth is mostly taken by the elderly women in the families. Gender indeed plays a significant role in determining women access and utilization of skilled birth attendance.
End-of-discharge prediction for satellite lithium-ion battery based on evidential reasoning rule
Authors: Zhao Dao , Zhou Zhijie , Zhang Peng , Zhang Yijun , Qin Haibin , Gao Shan 
Published: July 2022

Abstract
To ensure the safety of the power supply for an in-orbit satellite, it is of great significance to accurately predict the end-of-discharge time of lithium-ion batteries for making a reasonable flight plan. Constrained by development time and experimental environment, it is usually difficult to obtain many full discharge voltage curves of satellite batteries from ground experiments as historical data. Because of insufficient data, the prediction accuracy of the single time series prediction method is low. To solve this problem, this paper takes the voltage of the discharge process as the time series and uses the evidential reasoning rule algorithm to fuse the outputs of three typical prediction models to improve the prediction accuracy. The result can be expressed as a form of belief degree distribution with the ability to express uncertainty. Using the NASA battery dataset, the effectiveness of the proposed method is verified, and the end-of-discharge of an in-orbit satellite battery is predicted by the telemetry data.
Laparoscopic Cholecystectomy – A Proper Model Surgery for AI based Prediction of Adverse Events?
Authors: Berlet Maximilian , Fuchtmann Jonas , Bernhard Lukas , Jell Alissa , Weber Marie-Christin , Neumann Philipp- Alexander , Friess Helmut , Kranzfelder Michael , Feussner Hubertus , Wilhelm Dirk 
Published: July 2022

Abstract
Laparoscopic cholecystectomy (LCHE) is a widely employed model for surgical instrument and phase recognition in the field of machine learning (ML), with the latter being assigned to identify critical events and to avoid complications. Although ML algorithms have been proven to be effective for this instance and in selected patients, it is questionable whether patients receiving LCHE in daily clinical routine would actually benefit from adverse event prediction by ML applications. We believe, that the statistical problem of low prevalence (PREV) of potential adverse events in an unselected population and consequential low diagnostic yield was not considered adequately in recent research. Therefore, we performed a query to the G-DRG (German Diagnosis Related Groups) database of the German Federal Statistical Office with the aim to calculate prevalence of surgical and postoperative adverse events coming along with LCHE. The results enable an estimation of positive (PPV) and negative (NPV) predictive values hypothetically achievable by ML applications aiming to predict an adverse surgical course.
Annotation-efficient learning of surgical instrument activity in neurosurgery
Authors: Philipp Markus , Alperovich Anna , Lisogorov Alexander , Gutt-Will Marielena , Mathis Andrea , Saur Stefan , Raabe Andreas , Mathis-Ullrich Franziska 
Published: July 2022

Abstract
Machine learning-based solutions rely heavily on the quality and quantity of the training data. In the medical domain, the main challenge is to acquire rich and diverse annotated datasets for training. We propose to decrease the annotation efforts and further diversify the dataset by introducing an annotation-efficient learning workflow. Instead of costly pixel-level annotation, we require only image-level labels as the remainder is covered by simulation. Thus, we obtain a large-scale dataset with realistic images and accurate ground truth annotations. We use this dataset for the instrument localization activity task together with a studentteacher approach. We demonstrate the benefits of our workflow compared to state-of-the-art methods in instrument localization that are trained only on clinical datasets, which are fully annotated by human experts.
Motorized Percussion: A comparative Evaluation to Foster Telemedicine
Authors: Hangleiter Annika , Berlet Maximilian , Fuchtmann Jonas , Krumpholz Roman , Feußner Hubertus , Wilhelm Dirk 
Published: July 2022

Abstract
Purpose The demand for telemedicine is continuously increasing. However, many medical examination techniques such as auscultation are based on manual execution and subjective assessment by each individual doctor. For the increasing trend of telemedicine, we have developed an electric percussion device that examines in a standardized way. Methods This study evaluated the new approach of using motorized percussion against the gold standard of manual percussion. An examination was carried out on a healthy volunteer using both techniques. Thereby, occurring sounds from both pulmonary and non-pulmonary areas were recorded as the primary goal of percussion is to assess the size of the lung. Recordings were cut into individual samples and randomized within each examination variant. Results When discriminating between ventral and dorsal, and manual and motorized percussion, the outcome of the 28-participant anonymous survey showed that the noisesuppressed ventral motorized percussion with 93.2% (10.2) correctly identified percussion points surpassed all other conducted percussion techniques. Conclusion Making a distinction between pulmonary volume and exterior areas is very reliable with the motorized percussion. In order to be able to describe the clinical picture precisely in the future, further evaluations are necessary.
Towards multimodal interaction for needlebased procedures in a virtual radiology suite
Authors: Schott Danny , Heinrich Florian , Labsch Dominic , Hensen Bennet , Hansen Christian 
Published: July 2022

Abstract
Touchless interaction is popular in the medical domain because it maintains sterility and ensures physicians’ autonomy. Evaluating these technologies, however, proves difficult due to technical and human hurdles. Virtual reality leaves these limitations behind and allows for the exploration of promising concepts by simulating an environment and the interactions that takes place within it.We present a virtual radiology suite in the context of needle-based MR-interventions to evaluate touchless interactions. Hand and foot inputs were implemented on a custom interface and evaluated in a user study (n= 16). Results show that activating the system and manipulating values was faster with foot input. However, multimodal interaction is preferable because it is less demanding.
Exploring object and multi-target instrument tracking for AR-guided interventions
Authors: Schott Danny , Heinrich Florian , Stallmeister Lara , Hansen Christian 
Published: July 2022

Abstract
The rapid development of available hard- and software for computer-assisted or augmented reality (AR) guided interventions creates a need for fast and inexpensive prototyping environments. However, intraoperative tracking systems in particular represent a high cost threshold. Therefore, this work presents a low-cost tracking method based on a conventional RGB camera. Here, a combined approach of multiple image targets and 3D object target recognition is implemented. The system is evaluated with a systematic accuracy assessment analyzing a total of 385 3D positions. On average, a deviation of 15,69+-9,95 mm was measured. In addition, a prototypical AR-based needle navigation visualization was developed using Microsoft HoloLens 2. This system’s feasibility and usability was evaluated positively in a pilot study (n=3).
Towards a real-time control of robotic ultrasound using haptic force feedback
Authors: Schreiter J. , Semshchikov V. , Hanses M. , Elkmann N. , Hansen C. 
Published: July 2022

Abstract
Ultrasound is a widely used imaging technique and is appreciated for its non-invasiveness, absence of radiation, widespread availability, and compact equipment. Ergonomic difficulties in manual handling of the probe could be enhanced by a robotic controlled ultrasound. The paper addresses the development of such a system which enables remote operation of a ultrasound probe and includes haptic force feedback as well as video conferencing components for visual feedback. The development process followed a user-centered approach by investigating needs of potential end-users. Preliminary results demonstrated the functionality of the developed system for generating medical image data under laboratory conditions.
Experimental and numerical evaluation of simulated dental arch expansion during orthodontic therapy
Authors: Jaeger Rudolf , Glöggler Julia , Mai Pham To , Schmidt Falko , Schramm Elena , Schramm Alexander , Lapatki Bernd G. 
Published: July 2022

Abstract
In this publication, methods are presented to improve predictions of perioral soft-tissue changes following the expansion of the dental arches during orthodontic therapy. Acrylic veneers with different thicknesses were reversibly attached to the buccal surfaces of the upper and lower incisors to simulate their protrusion. The resulting morphological changes of the perioral soft-tissue surface were determined by 3D face scans. Experimentally-determined 3D soft-tissue changes are compared to numerical predictions using detailed finite-element (FE) models of the face of two individuals differing in the body mass index (BMI). The results suggest that common estimates of material constants used by the detailed and individualized FE models might be sufficient to explain absolute soft-tissue displacements although differences occurred between experimental and modeling results regarding the directions of displacements. The aim of this investigation is to create predictions of post-treatment appearance that are helpful for therapy planning.
The effect of Virtual Reality (VR) on anxiety and pain in patients undergoing port implantation
Authors: Sargut Mine , Schorn Stefan , Novotny Alexander , Friess Helmut , Wilhelm Dirk , Berlet Maximilian , Kranzfelder Michael 
Published: July 2022

Abstract
A growing body of evidence supports Virtual Reality (VR) as an effective and safe strategy for management of pain and stress associated with medical procedures in both adults and children. We therefore initiated a feasibility study to investigate the effect of VR on pain, stress, and anxiety during elective surgery, e.g. implantation of a central-venous port catheter, hypothesizing that VR can reduce intraoperative pain, stress and anxiety of the patient. In this manuscript, the preliminary results of the first 20 (out of 6o planned) patients are presented. Baseline pain characteristics did not differ between the two study groups (VR group (n=10) and standard (no VR device) group (n=10)). System usability (“easy to use”, “easy to learn” and “safe”) was rated “good - very good” by the study participants. Selfassessment of anxiety components (Y-6 item questionnaire) revealed a calming (3.3 ± 0.5 vs. 2.4 ± 0.5, P= 0.009) and relaxing (2.7 ± 1.2 vs. 1.8 ± 0.4; P=0.09) effect of the VR device. Evaluation of pain level (Short form McGill questionnaire) during the procedure revealed a lower pain intensity (VAS) level (17.5 ± 12.1 vs. 19.5 ± 10.6; P= 0.834) and present pain intensity (PPI) score (0.9 ± 0.6 vs. 1.0 ± 0.5; P= 0.841) in the VR group Preliminary data of our feasibility study indicates a positive effect of VR towards reduction of pain and stress in patients undergoing minor surgery in local anaesthesia. However, further data is needed to substantiate these results.
Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization
Authors: Dinkar Jagtap Abhishek , Heinrich Mattias , Himstedt Marian 
Published: July 2022

Abstract
An increasing number of colonoscopic guidance and assistance systems rely on machine learning algorithms which require a large amount of high-quality training data. In order to ensure high performance, the latter has to resemble a substantial portion of possible configurations. This particularly addresses varying anatomy, mucosa appearance and image sensor characteristics which are likely deteriorated by motion blur and inadequate illumination. The limited amount of readily available training data hampers to account for all of these possible configurations which results in reduced generalization capabilities of machine learning models. We propose an exemplary solution for synthesizing colonoscopy videos with substantial appearance and anatomical variations which enables to learn discriminative domain-randomized representations of the interior colon while mimicking real-world settings.
Segmentation of Circle of Willis from 7T TOF-MRI data and immersive exploration using VR
Authors: Spitz Lena , Allgaier Mareen , Mpotsaris Anastasios , Behme Daniel , Preim Bernhard , Saalfeld Sylvia 
Published: July 2022

Abstract
7T TOF MRI scans provide high resolution images of intracranial vasculature. When segmented, the Circle of Willis is detailed and thus opens up new possibilities, in research but also in education. We propose a segmentation pipeline for the Circle ofWillis, and introduce a prototype that enables exploration of not just the entire Circle of Willis, but also of its centerline, in an immersive VR enviroment. In our prototype, the model can be freely rotated, placed and scaled. A qualitative evaluation was performed with two experienced neuroradiologists, who rated the prototype and its potential positively.
Deep automatic segmentation of brain tumours in interventional ultrasound data
Authors: Zeineldin Ramy A. , Pollok Alex , Mangliers Tim , Karar Mohamed E. , Mathis-Ullrich Franziska , Burgert Oliver 
Published: July 2022

Abstract
Intraoperative imaging can assist neurosurgeons to define brain tumours and other surrounding brain structures. Interventional ultrasound (iUS) is a convenient modality with fast scan times. However, iUS data may suffer from noise and artefacts which limit their interpretation during brain surgery. In this work, we use two deep learning networks, namely UNet and TransUNet, to make automatic and accurate segmentation of the brain tumour in iUS data. Experiments were conducted on a dataset of 27 iUS volumes. The outcomes show that using a transformer with UNet is advantageous providing an efficient segmentation modelling long-range dependencies between each iUS image. In particular, the enhanced TransUNet was able to predict cavity segmentation in iUS data with an inference rate of more than 125 FPS. These promising results suggest that deep learning networks can be successfully deployed to assist neurosurgeons in the operating room.
Bending stiffness variability between a deployable robotic laser osteotome and its insertion device
Authors: Tomooka Yukiko , Rauter Georg , Gerig Nicolas , Takeda Ryo , Cattin Philippe , Eugster Manuela 
Published: July 2022

Abstract
Varying device stiffness on purpose can provide safety and performance improvements in interventions involving flexible minimal-invasive surgical robotic devices. For example, when flexible robotic devices are used for inserting minimal-invasive tools along curved paths into the knee joint for arthroscopy, a high device stiffness can allow precise path following where desirable. In contrast, when the minimalinvasive flexible device has reached its desired location inside the patient’s body, decoupling the surgical tool (end-effector) from the flexible robotic insertion device can be beneficial in avoiding transferring disturbances such as vibrations to the robot-patient interaction. In this paper, we investigate bending stiffness variability in dependence on the length of a flexible supply channel. Our experiments have shown that bending stiffness in fact decreases with the length of the supply channel but the highest stiffness connection is not sufficiently rigid and we plan to implement a more rigid connection to allow precise path following during insertion.
Surface and Event Characterization - Proximal Audio Sensing to improve Manual and Robotic Device Interventions
Authors: Friebe Michael , Boese Axel , Heryan Katarzyna , Spiller Moritz , Sühn Thomas , Esmaeili Nazila , Illanes Alfredo 
Published: July 2022

Abstract
Minimal-invasive procedures come with significant advantages for the patient. They also come with problems as the navigation/guidance of the devices to a target location is either based on pre-operatively acquired images and then performed free-hand or is accompanied by intraoperative imaging such as MRI or CT that is expensive, complicated and produces artifacts. Using robotic systems for moving and guiding these interventional and therapeutic devices adds additional issues like lack of palpation sensation and missing tissue feedback. While it is possible to add sensors to the distal tip, this creates other obstacles concerning reduced functionality, cables, sterility issues and added complexity and cost. We propose to use a proximally attached audio sensor to record the tissue tool interaction and provide real-time feedback to the clinician. This paper reports on initial attempts to use this technology with robotic arms for surface characterization and interventional vascular procedures that gain increased attention in combination with robotic devices. In summary, Proximal Audio Sensing could be a versatile, cost-effective and powerful tool to guide minimally invasive needle interventions and enable (semi-) autonomous robot-assisted surgery.
Software engineering defect detection and classification system based on artificial intelligence
Authors: Wang Hong , Yuan Limin 
Published: July 2022

Abstract
With the increasing reliance on automatic software-based applications, it is important to automate the classification of software defects and ensure software reliability. An automatic software defect classification system based on an expert system is proposed in this article. In this method, DACS first determines the category of software defects through the selection of typical features, then reduces the spatial knowledge base searched by the inference engine and selects the characteristics of a certain type of defect. Make a selection, determine the name of the defect, and finally select different causes and prevention methods for the defect as needed. The DACS structure was built, and the experiment showed that the AI system took 15 s to complete, whereas the traditional mechanism took 48 s; the accuracy of the AI was 99%, whereas the accuracy of the traditional mechanism was only 68%. According to the aforementioned experimental results, the recognition accuracy of the proposed research scheme is higher than that of the traditional mechanism. Hence, the time required to solve the problem of software engineering defect detection and classification is less than that of the traditional mechanism.
Factors Affecting Engagement of Older Adults in Exercise Following Hospitalization
Authors: Naseri Chiara , Haines Terry P. , Morris Meg E. , McPhail Steven M. , Etherton-Beer Christopher , Shorr Ronald , Flicker Leon , Waldron Nicolas , Bulsara Max , Hill Anne-Marie 
Published: July 2022

Background and Purpose:
Exercise interventions can improve physical recovery and reduce falls in older adults following hospitalization. The aim of the study was to identify factors associated with exercise engagement after hospital discharge.

Methods:
This study was a secondary analysis of data collected as part of a randomized controlled trial. Participants were 60 years and older, discharged from 3 rehabilitation hospitals in Australia, and followed for 6 months after discharge. The primary outcome was level of engagement in exercise after discharge, measured using setting, type, frequency, and time. A secondary outcome was self-efficacy for exercise at 6-month follow-up. Data were gathered at baseline in hospital and at 6 months after discharge by telephone using structured surveys. Associations between exercise and participant characteristics were evaluated using logistic regression models.

Results and Discussion:
Participants' (n = 292) mean age was 78 (SD 8) years and 63% were female. There were 146 (50%) who exercised after hospitalization for a median (interquartile range) time of 60 (60-75) minutes per week. Characteristics that were significantly associated with post-discharge engagement in exercise were having higher levels of functional ability at discharge (adjusted odds ratio [AOR] 1.2, 95% CI 1.0, 1.4), living with a partner (AOR 2.9, 95% CI 1.7, 4.9), and engagement in exercise prior to hospital admission (AOR 1.7, 95% CI 1.0, 2.8). The mean self-efficacy for exercise score at 6 months post-discharge was 58.5/90 (SD 24.5). Characteristics that were significantly predictive of a higher mean self-efficacy score at 6 months after hospitalization were having a college or university education (adjusted β-coefficient [Adj β] 11.5, 95% CI 3.8, 19.0), exercise prior to hospital admission (Adj β 12.3, 95% CI 5.1, 19.5), living with a partner at discharge (Adj β 14.5, 95% CI 7.1, 21.9), and higher functional ability at discharge (Adj β 4.0, 95% CI 1.9, 6.1).

Conclusion:
Older adults have low levels of engagement in exercise that might impact their recovery after hospitalization. During exercise prescription, clinicians should prioritize older adults who live alone, who have lower functional ability, and no previous habit of exercising.
Exploring Protein Structure with RasMol, a Free Online Tool
Authors: Fraley Amber D. , Odegaard Katherine E. , Vilchiz Victor H. , Kang ChulHee , Lee Cathy 
Published: July 2022

Understanding the structural and functional relationships of proteins is an important concept of biology. Therefore, we had Gordon State College students in the biochemistry class of spring 2019 apply and evaluate a hands-on bioinformatics activity, using the free bioinformatics program, RasMol. This module was designed to help students understand the relationships of a protein to a specific disease, and in this paper, we have chosen to discuss apolipoprotein E (APOE) relationships to Alzheimer’s disease since some of the students had an interest in neurobiochemistry.
During the learning module, students were asked to identify structural differences among the APOE isoforms. Overall, 20 out of 27 students (74.1%) evaluated the instructional value of the RasMol program positively after being surveyed. Student feedback also suggested they learned more about the molecular structure of APOE using RasMol. Therefore, utilization of free computer bioinformatics activities encourages students to apply logical, real-world solutions to biological problems.