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.

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.

A system for real-time multivariate feature combination of endoscopic mitral valve simulator training data.

Purpose: For an in-depth analysis of the learning benefits that a stereoscopic view presents during endoscopic training, surgeons required a custom surgical evaluation system enabling simulator independent evaluation of endoscopic skills. Automated surgical skill assessment is in dire need since supervised training sessions and video analysis of recorded endoscope data are very time-consuming. This paper presents a first step towards a multimodal training evaluation system, which is not restricted to certain training setups and fixed evaluation metrics.

[Ethical, legal and social implications in the use of artificial intelligence-based technologies in surgery : Principles, implementation and importance for the user].

Ethical, legal and social aspects are gaining increasingly more attention in the development and during the initial clinical application of medical devices. The introduction of elements of artificial intelligence (AI) and systems which are using AI makes this already complex topic even more challenging. The introduction of so-called dynamic AI or dynamic machine learning (ML) algorithms in this respect represents a turning point.

Analysis of the paraspinal muscle morphology of the lumbar spine using a convolutional neural network (CNN).

Purpose: This single-center study aimed to develop a convolutional neural network to segment multiple consecutive axial magnetic resonance imaging (MRI) slices of the lumbar spinal muscles of patients with lower back pain and automatically classify fatty muscle degeneration.

Methods: We developed a fully connected deep convolutional neural network (CNN) with a pre-trained U-Net model trained on a dataset of 3,650 axial T2-weighted MRI images from 100 patients with lower back pain. We included all qualities of MRI; the exclusion criteria were fractures, tumors, infection, or spine implants.

Language-based translation and prediction of surgical navigation steps for endoscopic wayfinding assistance in minimally invasive surgery.

Purpose: In the context of aviation and automotive navigation technology, assistance functions are associated with predictive planning and wayfinding tasks. In endoscopic minimally invasive surgery, however, assistance so far relies primarily on image-based localization and classification. We show that navigation workflows can be described and used for the prediction of navigation steps.

Processual Reasoning over Sequences of Situations in Endoscopic Surgery.

Minimally invasive surgery is a highly complex and technically demanding alternative to open surgery. Surgical procedures based on this method are characterized by small incisions and allow for a fast recovery of the patient. Such techniques are challenging for surgeons since they do not have a direct view of the surgical area.

Radiographic reconstruction of lower-extremity bone fragments: a first trial.

Purpose: The correct rotational alignment of the proximal and the distal bone fragments is an essential step in a long-bone deformity correction process. In order to plan the deformity correction, plain radiographs are conventionally used. But as three-dimensional information of the complex situation is not available, the correct amount of rotation can only be approximated.

Quantification of osseointegration of plasma-polymer coated titanium alloyed implants by means of microcomputed tomography versus histomorphometry.

A common method to derive both qualitative and quantitative data to evaluate osseointegration of implants is histomorphometry. The present study describes a new image reconstruction algorithm comparing the results of bone-to-implant contact (BIC) evaluated by means of µCT with histomorphometry data. Custom-made conical titanium alloyed (Ti6Al4V) implants were inserted in the distal tibial bone of female Sprague-Dawley rats.