Surface Treatment of Additively Manufactured Polyetheretherketone (PEEK) by Centrifugal Disc Finishing Process: Identification of the Key Parameters.

Polyetheretherketone is a promising material for implants due to its good mechanical properties and excellent biocompatibility. Its accessibility to a wide range of applications is facilitated by the ability to process it with an easy-to-use manufacturing process such as fused filament fabrication. The elimination of disadvantages associated with the manufacturing process, such as a poor surface quality, is a main challenge to deal with.

Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art.

In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations.

Experimental studies and mathematical modeling of the viscoelastic rheology of tracheobronchial mucus from respiratory healthy patients.

Background: Tracheobronchial mucus plays a crucial role in pulmonary function by providing protection against inhaled pathogens. Due to its composition of water, mucins, and other biomolecules, it has a complex viscoelastic rheological behavior. This interplay of both viscous and elastic properties has not been fully described yet.

Analyzing factors determining vaccination willingness against COVID-19 in Germany 2020.

The study is based on a German single-topic population survey on vaccination willingness against COVID-19 (VWC) by the authors (2020, n = 2014). The single-topic survey allowed us to test several competing explanations for VWC, as discussed in the literature. The VWC in the sample was 67.

Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation.

Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data.

An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis.

The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg.

[Capable or incapable of giving consent? Assessing a patient's capacity to consent: Procedures and challenges in daily clinical practice].

Background/objectives: The capacity of patients to give consent (CTC) is an indispensable prerequisite for informed consent to medical measures. When there is doubt about a patient's CTC, careful assessment is therefore required. Despite a broad theoretical discussion about the conception of CTC and possible procedures for its assessment, there is often a lack of orientation towards binding standardized procedural guidelines in everyday clinical practice.

[Parents' Willingness to Vaccinate with a COVID-19 Vaccine].

Background: Parents are faced with a vaccination decision in the context of their own vaccination and that of their children with a COVID-19 vaccine. At present, there is no (complete) vaccination recommendation.

Research Question: The study investigates the willingness to vaccinate of parents of minors and people without children who are minors, in which gender differences in particular are examined.

Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box.

Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice.

[The first steps after a proximal femoral fracture : Sensor-based mobility exploration in geriatric trauma patients].

Background: Sensor-based monitoring allows continuous observations of patient mobilization after proximal femoral fractures. A wrist-worn motion tracker allows long-term observation that is low in interruption and constraints for subjects.

Objective: Description of steps development after hip fracture surgery on a specialized geriatric trauma ward and beyond.

Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study.

Background: The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images.

Methods: Endoscopic images from three tertiary care centers in Germany were collected retrospectively.

Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis.

Objective: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value.

Design: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions.

Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks.

Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities.

A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology.

 The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders.