Laser scanner for 3D reconstruction of a wound's edge and topology

Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time

Since the 1970s, artificial intelligence (AI) has played an increasingly pivotal role in the medical field, enhancing the efficiency of disease diagnosis and treatment. Amidst an aging population and the proliferation of chronic disease, the prevalence of complex surgeries for high-risk multimorbid patients and hard-to-heal wounds has escalated. Healthcare professionals face the challenge of delivering safe and effective care to all patients concurrently. Inadequate management of skin wounds exacerbates the risk of infection and complications, which can obstruct the healing process and diminish patients' quality of life. AI shows substantial promise in revolutionizing wound care and management, thus enhancing the treatment of hospitalized patients and enabling healthcare workers to allocate their time more effectively. This review details the advancements in applying AI for skin wound assessment and the prediction of healing timelines. It emphasizes the use of diverse algorithms to automate and streamline the measurement, classification, and identification of chronic wound healing stages, and to predict wound healing times. Moreover, the review addresses existing limitations and explores future directions.

A comprehensive review of the application of 3D-bioprinting in chronic wound management

INTRODUCTION

Chronic wounds require more sophisticated care than standard wound care because they are becoming more severe as a result of diseases like diabetes. By resolving shortcomings in existing methods, 3D-bioprinting offers a viable path toward personalized, mechanically strong, and cell-stimulating wound dressings.

AREAS COVERED

This review highlights the drawbacks of traditional approaches while navigating the difficulties of managing chronic wounds. The conversation revolves around employing natural biomaterials for customized dressings, with a particular emphasis on 3D-bioprinting. A thorough understanding of the uses of 3D-printed dressings in a range of chronic wound scenarios is provided by insights into recent research and patents.

EXPERT OPINION

The expert view recognizes wounds as a historical human ailment and emphasizes the growing difficulties and expenses related to wound treatment. The expert acknowledges that 3D printing is revolutionary, but also points out that it is still in its infancy and has the potential to enhance mass production rather than replace it. The review highlights the benefits of 3D printing for wound dressings by providing instances of smart materials that improve treatment results by stimulating angiogenesis, reducing pain, and targeting particular enzymes. The expert advises taking action to convert the technology's prospective advantages into real benefits for patients, even in the face of resistance to change in the healthcare industry. It is believed that the increasing evidence from in-vivo studies is promising and represents a positive change in the treatment of chronic wounds toward sophisticated 3D-printed dressings.

Comparison of digital and traditional skin wound closure assessment methods in mice

BACKGROUND

Chronic skin wounds are a common complication of many diseases such as diabetes. Various traditional methods for assessing skin wound closure are used in animal studies, including wound tracing, calliper measurements and histological analysis. However, these methods have poorly defined wound closure or practical limitations. Digital image analysis of wounds is an increasingly popular, accessible alternative, but it is unclear whether digital assessment is consistent with traditional methods. This study aimed to optimise and compare digital wound closure assessment with traditional methods, using a diabetic mouse model. Diabetes was induced in male C57BL/6J mice by high-fat diet feeding combined with low dose (65 mg/kg of body weight) streptozotocin injections. Mice fed normal chow were included as controls. After 18 weeks, four circular full-thickness dorsal skin wounds of 4 mm diameter were created per mouse. The wounds were photographed and measured by callipers. Wound closure rate (WCR) was digitally assessed by two reporters using two methods: wound outline (WCR-O) and re-epithelialisation (WCR-E). Wounded skin tissues were collected at 10-days post-wounding and wound width was measured from haematoxylin and eosin-stained skin tissue.

RESULTS

Between reporters, WCR-O was more consistent than WCR-E, and WCR-O correlated with calliper measurements. Histological analysis supported digital assessments, especially WCR-E, when wounds were histologically closed.

CONCLUSIONS

WCR-O could replace calliper measurements to measure skin wound closure, but WCR-E assessment requires further refinement. Small animal studies of skin wound healing can greatly benefit from standardised definitions of wound closure and more consistent digital assessment protocols.

Automatic Robot-Driven 3D Reconstruction System for Chronic Wounds

Chronic wounds, or wounds that are not healing properly, are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be even higher. Wound assessment should be fast and accurate in order to reduce the possible complications, and therefore shorten the wound healing process. Contact methods often used by medical experts have drawbacks that are easily overcome by non-contact methods like image analysis, where wound analysis is fully or partially automated. This paper describes an automatic wound recording system build upon 7 DoF robot arm with attached RGB-D camera and high precision 3D scanner. The developed system presents a novel NBV algorithm that utilizes surface-based approach based on surface point density and discontinuity detection. The system was evaluated on multiple wounds located on medical models as well as on real patents recorded in clinical medical center.