The use of fetal bovine acellular dermal matrix in severe diabetic foot ulceration and threatened limbs with tissue loss the use of FBADM as an adjunct for complex wounds.

OBJECTIVES: Foot ulceration associated with diabetic foot disease (DFD) and chronic limb-threatening ischaemia (CLTI) presents a complex clinical challenge and failure to heal the wound imposes a significant risk of major limb amputation (MLA). In attempt to accelerate wound healing rates and decrease MLA, tissue engineering research into bio-engineered scaffolds and skin substitutes has become a growing area of interest. Advanced wound therapies such as fetal bovine acellular dermal matrix (FBADM) may have success in the treatment of difficult to heal chronic foot ulcers. The FBADM traps and binds the patients' own epithelial cells to rebuild the dermis layer of the skin. Previous studies have suggested that wounds treated with FBADM had a faster healing rate than wounds managed with conventional dressings. However, these studies excluded foot wounds with chronic exposed bone or tendon, active infection, gangrene, or osteomyelitis and patients with uncontrolled blood glucose levels were excluded. The aim of this study was to assess the efficacy of FBADM for patients admitted to hospital acutely with severe foot ulceration secondary to DFD and CLTI. METHODS: Between February 2020 and December 2021, inpatients admitted acutely at a single tertiary centre with a severe non-healing foot ulcer and had a wound suitable for application of a FBADM after primary debridement were included in the study. A severe non-healing foot wound was defined as a Society for Vascular Surgery Wound, Ischaemia, and foot Infection (WIfI) stage of 3 or 4. Participants were prospectively followed up at regular intervals at a multidisciplinary high-risk diabetic foot clinic until June 2022. The primary endpoint was time to wound closure. The secondary endpoints were number of applications of FBADM, readmission rate and amputation-free survival. RESULTS: There were 22 patients included in the study with a median age of 71 (50-87) years and 15 were male. Five patients had a WIfI stage of 3 and 17 had a WIfI score of 4. Overall, 14 patients required revascularisation procedures (6 open surgery,8 endovascular intervention). A total of 18 patients achieved complete wound healing with a median time to wound healing of 178 (28-397) days. Two patients underwent a MLA and two patients died prior to complete wound healing. The median length of stay was 16.5 (5-115) days, and 4 patients were readmitted to hospital within 12 months. CONCLUSION: FBADM may be a useful adjunct in the acute setting of complex DFD and CLTI ulceration to assist with wound healing. Future comparative prospective studies are required to further validate these preliminary findings.

How have services for diabetes, eye, hearing and foot health been integrated for adults? Protocol for a scoping review.

INTRODUCTION: The global population is ageing, and by 2050, there will be almost 2.1 billion people over the age of 60 years. This ageing population means conditions such as diabetes are on the increase, as well as other conditions associated with ageing (and/or diabetes), including those that cause vision impairment, hearing impairment or foot problems. The aim of this scoping review is to identify the extent of the literature describing integration of services for adults of two or more of diabetes, eye, hearing or foot services. METHODS AND ANALYSIS: The main database searches are of Medline and Embase, conducted by an information specialist, without language restrictions, for studies published from 1 January 2000 describing the integration of services for two or more of diabetes, eye, hearing and foot health in the private or public sector and at the primary or secondary level of care, primarily targeted to adults aged ≥40 years. A grey literature search will focus on websites of key organisations. Reference lists of all included articles will be reviewed to identify further studies. Screening and data extraction will be undertaken by two reviewers independently and any discrepancies will be resolved by discussion. We will use tables, maps and text to summarise the included studies and findings, including where studies were undertaken, which services tended to be integrated, in which sector and level of the health system, targeting which population groups and whether they were considered effective. ETHICS AND DISSEMINATION: As our review will be based on published data, ethical approval will not be sought. This review is part of a project in Aotearoa New Zealand that aims to improve access to services for adults with diabetes or eye, hearing or foot conditions. The findings will be published in a peer-reviewed journal and presented at relevant conferences.

Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation.

OBJECTIVE: Conduct a multicenter proof-of-concept clinical evaluation to assess the accuracy of an artificial intelligence system on a smartphone for automated detection of diabetic foot ulcers. METHODS: The evaluation was undertaken with patients with diabetes (n = 81) from September 2020 to January 2021. A total of 203 foot photographs were collected using a smartphone, analysed using the artificial intelligence system, and compared against expert clinician judgement, with 162 images showing at least one ulcer, and 41 showing no ulcer. Sensitivity and specificity of the system against clinician decisions was determined and inter- and intra-rater reliability analysed. RESULTS: Predictions/decisions made by the system showed excellent sensitivity (0.9157) and high specificity (0.8857). Merging of intersecting predictions improved specificity to 0.9243. High levels of inter- and intra-rater reliability for clinician agreement on the ability of the artificial intelligence system to detect diabetic foot ulcers was also demonstrated (Kα > 0.8000 for all studies, between and within raters). CONCLUSIONS: We demonstrate highly accurate automated diabetic foot ulcer detection using an artificial intelligence system with a low-end smartphone. This is the first key stage in the creation of a fully automated diabetic foot ulcer detection and monitoring system, with these findings underpinning medical device development.

Diabetes-related foot disease research in Aotearoa New Zealand: a bibliometric analysis (1970-2020).

BACKGROUND: The aim of this bibliometric study was to examine trends in the quality and quantity of published diabetes-related foot disease (DRFD) research in Aotearoa/New Zealand (NZ) over the past five decades. METHOD: In July 2021, the Scopus® database was searched for DRFD-related publications (1970-2020) using predetermined search and inclusion criteria. Bibliometric data were extracted from Scopus® and Journal Citation Reports. Retrieved bibliometric indicators were analysed in Biblioshiny, an R Statistical Software interface and reported using descriptive statistics. RESULTS: Forty-seven DRFD-related articles were identified. The annual number of publications showed a significant upward trend increasing from one in 1988 to a peak of six in 2018 (P < 0.001). The majority of identified articles (n = 31, 66%) were published in the last decade (2011-2020). Basic/clinical research accounted for 87% (n = 41) of publications and 14 (30%) investigated the screening and/or prevention of DRFD. The average citation per article was 20.23 (range: 0-209) and the median impact factor was 4.31 (range, 1.82-79.32). Over a third of articles (36%) had an international authorship network. Funding was reported in 15 (32%) articles; 12 (26%) were supported by public national grants vs. three (6%) reporting industry-sponsorship. CONCLUSION: DRFD articles authored by NZ researchers have increased over the past five decades. Despite NZ researchers having increased their global impact through collaborative networks, most of the research was classified as low-level evidence, with limited focus on Indigenous Maori and limited financial support and funding. Increased funding for interventional research is required to enable a higher level of evidence-based and practice-changing research to occur. With rates of diabetes-related amputations higher in Maori future research must focus on reducing inequalities in diabetes-related outcomes for Maori by specifically targeting the prevention and screening of DRFD in primary care settings in NZ.

The DFUC 2020 Dataset: Analysis Towards Diabetic Foot Ulcer Detection.

Every 20 seconds a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a global solution. The International Conference on Medical Image Computing and Computer Assisted Intervention challenge, which concerns the automated detection of diabetic foot ulcers (DFUs) using machine learning techniques, will accelerate the development of innovative healthcare technology to address this unmet medical need. In an effort to improve patient care and reduce the strain on healthcare systems, recent research has focused on the creation of cloud-based detection algorithms. These can be consumed as a service by a mobile app that patients (or a carer, partner or family member) could use themselves at home to monitor their condition and to detect the appearance of a DFU. Collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospitals and the Manchester University NHS Foundation Trust has created a repository of 4,000 DFU images for the purpose of supporting research toward more advanced methods of DFU detection. This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluation results. It facilitates the challenge by providing useful insights into state-of-the-art and ongoing research.

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation.

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

Implementing a bariatric surgery program.

The number of people who are obese or morbidly obese is increasing in the United States. Currently, the most effective means of losing a substantial amount of weight and maintaining the weight loss is bariatric surgery, and health care providers, especially those in surgical services, must be able to safely care for patients undergoing these surgeries. Financial implications of starting a bariatric surgery program and the ongoing costs must be fully understood and supported by both administrators and employees. Special equipment and supplies are needed to handle the higher weight of bariatric patients, and careful planning is required for adequate medical and nursing expertise, staffing, equipment, supplies, facility resources, and patient support services.