The amount of acquired radiology imaging studies grows worldwide at a rapid pace. Novel information technology tools for radiologists promise an increase of reporting quality and as well quantity at the same time. Automated text report drafting is one branch of this development. We defined for the present study in total 9 cases of distal radius fracture. Command files structured according to a template of the Radiological Society of North America (RSNA) and to Arbeitsgemeinschaft Osteosynthese (AO) classifiers were given as input to the natural language processing tool ChatGPT. ChatGPT was tasked with drafting an appropriate radiology report. A parameter study (n = 5 iterations) was performed. An overall high appraisal of ChatGPT radiology report quality was obtained in a score card based assessment. ChatGPT demonstrates the capability to adjust output files in response to minor changes in input command files. Existing shortcomings were found in technical terminology and medical interpretation of findings. Text drafting tools might well support work of radiologists in the future. They would allow a radiologist to focus time on the observation of image details and patient pathology. ChatGPT can be considered a substantial step forward towards that aim.

The roughness of material surfaces is of greatest relevance for applications. These include wear, friction, fatigue, cytocompatibility, or corrosion resistance. Today’s descriptors of the International Organization for Standardization show varying performance in discriminating surface roughness patterns. We introduce here a set of surface parameters which are extracted from the appropriate persistence diagram with enhanced discrimination power. Using the finite element method implemented in Abaqus Explicit 2019, we modelled American Rolling Mill Company pure iron specimens (volume 1.5 × 1.5 × 1.0 mm³) exposed to a shot peening procedure. Surface roughness evaluation after each shot impact and single indents were controlled numerically. Conventional and persistence-based evaluation is implemented in Python code and available as open access supplement. Topological techniques prove helpful in the comparison of different shot peened surface samples. Conventional surface area roughness parameters might struggle in distinguishing different shot peening surface topographies, in particular for coverage values > 69%. Above that range, the calculation of conventional parameters leads to overlapping descriptor values. In contrast, lifetime entropy of persistence diagrams and Betti curves provide novel, discriminative one-dimensional descriptors at all coverage ranges. We compare how conventional parameters and persistence parameters describe surface roughness. Conventional parameters are outperformed. These results highlight how topological techniques might be a promising extension of surface roughness methods.

Ferromagnetic fiber networks have the potential to deform in vivo imparting therapeutic levels of strain on in-growing periprosthetic bone tissue. 444 Ferritic stainless steel provides a suitable material for this application due to its ability to support cultures of human osteoblasts (HObs) without eliciting undue inflammatory responses from monocytes in vitro. In the present article, a 444 fiber network, containing 17 vol% fibers, has been investigated. The network architecture was obtained by applying a skeletonization algorithm to three-dimensional tomographic reconstructions of the fiber networks. Elastic properties were measured using low-frequency vibration testing, providing globally averaged properties as opposed to mechanical methods that yield only local properties. The optimal region for transduction of strain to cells lies between the ferromagnetic fibers. However, cell attachment, at early time points, occurs primarily on fiber surfaces. Deposition of fibrin, a fibrous protein involved in acute inflammatory responses, can facilitate cell attachment within this optimal region at early time points. The current work compared physiological (3 and 5 g·L(-1)) and supraphysiological fibrinogen concentrations (10 g·L(-1)), using static in vitro seeding of HObs, to determine the effect of fibrin deposition on cell responses during the first week of cell culture. Early cell attachment within the interfiber spaces was observed in all fibrin-containing samples, supported by fibrin nanofibers. Fibrin deposition influenced the seeding, metabolic activity, and early stage differentiation of HObs cultured in the fibrin-containing fiber networks in a concentration-dependant manner. While initial cell attachment for networks with fibrin deposited from low physiological concentrations was similar to control samples without fibrin deposition, significantly higher HObs attached onto high physiological and supraphysiological concentrations. Despite higher cell numbers with supraphysiological concentrations, cell metabolic activities were similar for all fibrinogen concentrations. Further, cells cultured on supraphysiological concentrations exhibited lower cell differentiation as measured by alkaline phosphatase activity at early time points. Overall, the current study suggests that physiological fibrinogen concentrations would be more suitable than supraphysiological concentrations for supporting early cell activity in porous implant coatings.

The finite element method has complimented research in the field of network mechanics in the past years in numerous studies about various materials. Numerical predictions and the planning efficiency of experimental procedures are two of the motivational aspects for these numerical studies. The widespread availability of high performance computing facilities has been the enabler for the simulation of sufficiently large systems. In the present study, finite element models were built for sintered, metallic fibre networks and validated by previously published experimental stiffness measurements. The validated models were the basis for predictions about so far unknown properties. The finite element models were built by transferring previously published skeletons of fibre networks into finite element models. Beam theory was applied as simplification method. The obtained material stiffness isn’t a constant but rather a function of variables such as sample size and boundary conditions. Beam theory offers an efficient finite element method for the simulated fibre networks. The experimental results can be approximated by the simulated systems. Two worthwhile aspects for future work will be the influence of size and shape and the mechanical interaction with matrix materials.

Objectives: The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for the acceleration and undersampling of imaging data. Resulting reduced acquisition times would lead to greater machine utility and to greater cost-efficiency of machine operations. Materials and Methods: Our case shows images from magnetic resonance arthrography under traction of the right hip joint from a 30-year-old, otherwise healthy, male patient. Results: The undersampled image data when reconstructed by a deep learning tool can contain false-positive cartilage delamination and false-positive diffuse cartilage defects. Conclusions: In the future, precision of this novel technology will have to be put to thorough testing. Bias of systems, in particular created by the choice of training data, will have to be part of those assessments.