Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review.

OBJECTIVE: The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements.

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO2) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO2 from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO2 emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO2 based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer "atmospheric potential oxygen" (APO). We detect and quantify ffCO2 reductions during 2020-2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near-real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO2 emissions reductions from three independent lower-frequency U.K. estimates.

Reaction Sintering of Biocompatible Al2O3-hBN Ceramics.

Biocompatible Al2O3-hBN ceramic was sintered from AlN and B2O3 precursors by reaction hot pressing at 1750 °C and 30 MPa for 8 min. The ceramic was compared to nonreactive (NR) one sintered from Al2O3 and BN under the same sintering conditions. The NR ceramic possesses 9% porosity as opposed to only 2% porosity for the reaction sintered Al2O3-hBN. The reaction sintered ceramic has crack resistance in the region of 5.0 ± 0.1 MPa·m1/2, which is approximately 20% higher than previously reported pure Al2O3 or Al2O3-hBN sintered without reaction support. The higher amount of hBN in the developed Al2O3-hBN material (27 vol %) facilitates hardness lowering to the region of 6 GPa, which is closer to the bone hardness and makes the ceramic machinable. Reaction sintering of the Al2O3-hBN composite opens a new area of creation and formation of load-bearing Al2O3-hBN ceramic bioimplants.

Finite element analysis to model ischemia experienced in the development of device related pressure ulcers.

Pressure ulcers are a common occurrence of damage to skin. Severity ranges from slightly discoloured skin to full thickness tissue damage which can be fatal in some cases. Engineering effort, typically developing computational models had made significant progress in the understanding and demonstration of the formation mechanism of pressure ulcers with the aetiology of excessive stress; however, relatively limited attempts had been made to develop relevant models for pressure ulcers caused by ischemia. The aim of this article is to present evidence of a computational model developed to simulate ischemic pressure ulcer formation and demonstrate the established relationship between the computational data and the acquired clinically relevant experimental data by utilising Laser Doppler Velocimetry. The application of the presented computational model and the established relationship allows the evaluation of the effect of a mechanical loading to the cutaneous blood flow velocity which is a step closer to understand and evaluate a mechanical load to the formation of pressure ulcers caused by ischemia.

Chemical Imaging by Dissolution Analysis: Localized Kinetics of Dissolution Behavior to Provide Two-Dimensional Chemical Mapping and Tomographic Imaging on a Nanoscale.

A new approach to achieving chemical mapping on a nanoscale is described that can provide 2D and tomographic images of surface and near-surface structure. The method comprises dissolving material from the surface of the sample by applying a series of aliquots of solvent, then analyzing their contents after removing them; in between exposures, the surface is imaged with atomic force microscopy. This technique relies on being able to compensate for any drift between images by use of software. It was applied to a blend of two polymers, PMMA and PS. The analytical data identified the material that was dissolved, and the topography images enabled the location of the various materials to be determined by analyzing local dissolution kinetics. The prospects for generalizing the approach are discussed.

Development of a synovial fluid analogue with bio-relevant rheology for wear testing of orthopaedic implants.

The rheological properties of synovial fluid (SF) are crucial to the performance of joint prostheses. During the development of joint prostheses, wear tests are performed, which simulate joint movements in diluted solutions (usually between 25 and 33% v/v) of bovine serum which has very different rheological properties compared with native SF, where rheology is maintained by hyaluronan. Consequently, there is a need to develop a more suitable artificial SF. In this study, we used rheological techniques to understand SF flow properties which provided an insight into the mechanical behaviour required of a practical SF analogue. Steady-shear viscosity measurements were performed to reveal changes as a function of shear rate. To analyse the viscoelastic properties small deformation oscillatory measurements of storage modulus (G') loss modulus (G″) and complex viscosity (η(⁎)) were made. The rheological properties of the SF where compared with those of the polysaccharides sodium alginate, gellan gum and mixtures of both polymers. Initial results revealed classic shear thinning behaviour for the SF with a small Newtonian plateau at low shear rates with a gradual reduction in viscosity with increasing shear rate. Viscoelasticity measurements also showed that at low frequencies of oscillation there was a viscous response with G″ greater than G' and at higher frequencies there was an elastic response. Rheological properties were found to be similar to that of a 50:50 mix of 2% w/v high molecular weight alginate and 0.75% w/v gellan gum. Importantly, the lubricating behaviour of the serum differed significantly from the biopolymer blend over a full range of sliding velocities. The biopolymer blend was shown to lubricate the opposing surfaces more effectively. This difference was attributed to the more rapid alignment of the polysaccharide during shear when compared with the bovine albumin (the most abundant protein in serum), which typically exhibits a globular structure and has a tendency for self-association. These results suggest that polysaccharide solutions with bio-relevant rheology maybe be suitable as lubricants for in vitro orthopaedic prosthetic wear tests.