Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine.

This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classifications. Lumbar IVD MRI data from 300 patients were retrospectively analyzed. Two clinicians assessed the manual segmentation and grading for inter-rater reliability using Cohen's kappa.

The Emergency Medical Team Operating System - a vision for field hospital data management in following the concepts of predictive, preventive, and personalized medicine.

In times where sudden-onset disasters (SODs) present challenges to global health systems, the integration of predictive, preventive, and personalized medicine (PPPM / 3PM) into emergency medical responses has manifested as a critical necessity. We introduce a modern electronic patient record system designed specifically for emergency medical teams (EMTs), which will serve as a novel approach in how digital healthcare management can be optimized in crisis situations. This research is based on the principle that advanced information technology (IT) systems are key to transforming humanitarian aid by offering predictive insights, preventive strategies, and personalized care in disaster scenarios.

Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications.

Background: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment.

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular.

Using Digital Twins to Support Multiple Stages of the Patient Journey.

New possibilities in personalized medicine need to be complemented by clinical decision support systems as well as context-specific applications to be used in clinical routine. We aim to implement a shared technical backend for a large variety of applications in personalized head-and-neck cancer treatment. The infrastructure is conceptualized as a multi-purpose digital twin for cancer treatment.

Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches.

Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS.

Analysing multi-perspective patient-related data during laparoscopic gynaecology procedures.

Fusing data from different medical perspectives inside the operating room (OR) sets the stage for developing intelligent context-aware systems. These systems aim to promote better awareness inside the OR by keeping every medical team well informed about the work of other teams and thus mitigate conflicts resulting from different targets. In this research, a descriptive analysis of data collected from anaesthesiology and surgery was performed to investigate the relationships between the intra-abdominal pressure (IAP) and lung mechanics for patients during laparoscopic procedures.

Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging.

Problem: Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research. Even though a range of works have been presented, only a few showed slightly promising results for human tissue, and these were mostly focused on pathological and non-pathological tissue classification.

Methods: In this work, several spectral similarity measures on hyperspectral (HS) images of in vivo human tissue were evaluated for tissue discrimination purposes.

A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks.

The increase in diagnostic and therapeutic procedures in the treatment of oncological diseases, as well as the limited capacity of experts to provide information, necessitates the development of therapy decision support systems (TDSS). We have developed a treatment decision model that integrates available patient information as well as tumor characteristics. They are assessed according to their relevance in evaluating the optimal therapy option.

Alternative intraoperative optical imaging modalities for fluorescence angiography in gastrointestinal surgery: spectral imaging and imaging photoplethysmography.

Introduction: Intraoperative near-infrared fluorescence angiography with indocyanine green (ICG-FA) is a well-established modality in gastrointestinal surgery. Its main drawback is the application of a fluorescent agent with possible side effects for patients. The goal of this review paper is the presentation of alternative, non-invasive optical imaging methods and their comparison with ICG-FA.

The Emergency Medical Team Operating System: design, implementation, and evaluation of a field hospital information management system.

In case of sudden-onset disasters (SODs), the World Health Organization deploys specialized emergency medical teams (EMTs); yet, the coordination and operation of such teams pose significant challenges. One issue is the lack of digital information systems and standards. We developed a highly customizable and scalable electronic medical record (EMR) system, tailored to EMT requirements, called the "Emergency Medical Team Operating System" (EOS).

Topics Mentioned by Stroke Patients during Supportive Phone Calls-Implications for Individualized Aftercare Programs.

By understanding stroke as a chronic disease, aftercare becomes increasingly important. For developing aftercare programs, the patients' perspective regarding, for example, stroke-related symptoms and interactions with the healthcare system is necessary. Records from a local stroke pilot program were used to extract relevant topics from the patients' perspective, as mentioned during a phone call two months after hospital discharge.

Ontology-based surgical workflow recognition and prediction.

Background: Surgical context-aware systems can adapt to the current situation in the operating room and thus provide computer-aided assistance functionalities and intraoperative decision-support. To interact with the surgical team perceptively and assist the surgical process, the system needs to monitor the intraoperative activities, understand the current situation in the operating room at any time, and anticipate the following possible situations.

Methods: A structured representation of surgical process knowledge is a prerequisite for any applications in the intelligent operating room.

[BIOPASS hybrid navigation for endoscopic sinus surgery - an assistance system].

Previous navigation systems can determine the position of the "tracked" surgical instrument in CT images in the context of functional endoscopic sinus surgery (FESS), but do not provide any assistance directly in the video endoscopic image of the surgeon. Developing this direct assistance for intraoperative orientation and risk reduction was the goal of the BIOPASS project (ld ntologie und rozessgestütztes istenzsystem). The Project pursues the development of a novel navigation system for FESS without markers.

Design and validation of a medical robotic device system to control two collaborative robots for ultrasound-guided needle insertions.

The percutaneous biopsy is a critical intervention for diagnosis and staging in cancer therapy. Robotic systems can improve the efficiency and outcome of such procedures while alleviating stress for physicians and patients. However, the high complexity of operation and the limited possibilities for robotic integration in the operating room (OR) decrease user acceptance and the number of deployed robots.

Comparison of image registration methods for combining laparoscopic video and spectral image data.

Laparoscopic procedures can be assisted by intraoperative modalities, such as quantitative perfusion imaging based on fluorescence or hyperspectral data. If these modalities are not available at video frame rate, fast image registration is needed for the visualization in augmented reality. Three feature-based algorithms and one pre-trained deep homography neural network (DH-NN) were tested for single and multi-homography estimation.

Web-Based Patient-Reported Outcomes for ENT Patients-Evaluation of the Status Quo, Patients' View, and Future Perspectives.

Background: Patient-reported outcomes (PRO) assess disease burden and indicate unmet needs. Home-based electronic PRO measures (ePROMs) can support tumor aftercare (TAC). Creating an ePROM is the next step after implementing the software "OncoFunction" to assess PROs during TAC of head- and neck-cancer patients (HNC).