PredyCLU: a prediction system for chronic leg ulcers based on fuzzy logic; part I - exploring the venous side

A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements

Sub‐epidermal moisture is an established biophysical marker of pressure ulcer formation based on biocapacitance changes in affected soft tissues, which has been shown to facilitate early detection of these injuries. Artificial intelligence shows great promise in wound prevention and care, including in automated analyses of quantitative measures of tissue health such as sub‐epidermal moisture readings acquired over time for effective, patient‐specific, and anatomical‐site‐specific pressure ulcer prophylaxis. Here, we developed a novel machine learning algorithm for early detection of heel deep tissue injuries, which was trained using a database comprising six consecutive daily sub‐epidermal moisture measurements recorded from 173 patients in acute and post‐acute care settings. This algorithm was able to achieve strong predictive power in forecasting heel deep tissue injury events the next day, with sensitivity and specificity of 77% and 80%, respectively, revealing the clinical potential of artificial intelligence‐powered technology for hospital‐acquired pressure ulcer prevention. The current work forms the scientific basis for clinical implementation of machine learning algorithms that provide effective, early, and anatomy‐specific preventive interventions to minimise the occurrence of hospital‐acquired pressure ulcers based on routine tissue health status measurements.

Review of Machine Learning in Predicting Dermatological Outcomes

Artificial intelligence is a broad branch of computer science that has garnered significant interest in the field of medicine because of its problem solving, decision making and pattern recognition abilities. Machine learning, a subset of artificial intelligence, hones in on the ability of computers to receive data and learn for themselves, manipulating algorithms as they organize the information they are processing. Dermatology is at a particular advantage in the implementation of machine learning due to the availability of large clinical image databases that can be used for machine training and interpretation. While numerous studies have implemented machine learning in the diagnostic aspect of dermatology, less research has been conducted on the use of machine learning in predicting long-term outcomes in skin disease, with only a few studies published to date. Such an approach would assist physicians in selecting the best treatment methods, save patients' time, reduce treatment costs and improve the quality of treatment overall by reducing the amount of trial-and-error in the treatment process. In this review, we aim to provide a brief and relevant introduction to basic artificial intelligence processes, and to consolidate and examine the published literature on the use of machine learning in predicting clinical outcomes in dermatology.

PredyCLU: A prediction system for chronic leg ulcers based on fuzzy logic; part II-Exploring the arterial side

Peripheral arterial disease (PAD) and its most severe form, critical limb ischaemia (CLI), are very common clinical conditions related to atherosclerosis and represent the major causes of morbidity, mortality, disability, and reduced quality of life (QoL), especially for the onset of ischaemic chronic leg ulcers (ICLUs) and the subsequent need of amputation in affected patients. Early identification of patients at risk of developing ICLUs may represent the best form of prevention and appropriate management. In this study, we used a Prediction System for Chronic Leg Ulcers (PredyCLU) based on fuzzy logic applied to patients with PAD. The patient population consisted of 80 patients with PAD, of which 40 patients (30 males [75%] and 10 females [25%]; mean age 66.18 years; median age 67.50 years) had ICLUs and represented the case group. Forty patients (100%) (27 males [67.50%] and 13 females [32.50%]; mean age 66.43 years; median age 66.50 years) did not have ICLUs and represented the control group. In patients of the case group, the higher was the risk calculated with the PredyCLU the more severe were the clinical manifestations recorded. In this study, the PredyCLU algorithm was retrospectively applied on a multicentre population of 80 patients with PAD. The PredyCLU algorithm provided a reliable risk score for the risk of ICLUs in patients with PAD.

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

Artificial intelligence (AI) has become a progressively prevalent Research Topic in medicine and is increasingly being applied to dermatology. There is a need to understand this technology's progress to help guide and shape the future for medical care providers and recipients. We reviewed the literature to evaluate the types of publications on the subject, the specific dermatological topics addressed by AI, and the most challenging barriers to its implementation. A substantial number of original articles and commentaries have been published to date and only few detailed reviews exist. Most AI applications focus on differentiating between benign and malignant skin lesions, however; others exist pertaining to ulcers, inflammatory skin diseases, allergen exposure, dermatopathology, and gene expression profiling. Applications commonly analyze and classify images, however, other tools such as risk assessment calculators are becoming increasingly available. Although many applications are technologically feasible, important implementation barriers have been identified including systematic biases, difficulty of standardization, interpretability, and acceptance by physicians and patients alike. This review provides insight into future research needs and possibilities. There is a strong need for clinical investigation in dermatology providing evidence of success overcoming the identified barriers. With these research goals in mind, an appropriate role for AI in dermatology may be achieved in not so distant future.

CGF Treatment of Leg Ulcers: a Randomized Controlled Trial

Background

Concentrated Growth Factors (CGF) is a concentration of second generation autologous growth factors compared to platelet rich plasma (PRP) and represents a multifactorial stimulation system that can be used for the management and treatment of chronic skin ulcers.

Aim

The aim of this work is to evaluate the additional benefits of the CGF compared to the standard of dressing and its effects on the dynamics of the healing process.

Methods

Autologous CGFs were obtained from 100 patients with chronic mixed ulcers (venous ulcers in patients with II stage claudication) of the lower limbs in a multicentric controlled randomized study.

Results

The results showed a significant advantage in the use of CGF in association with cleansing and selective compression in the healing time and stabilization of mixed ulcers of the lower extremities.

Conclusions

These results support the CGF’s clinical use for improving clinical outcomes in mixed ulcers of the legs.

Systematic review of machine learning for diagnosis and prognosis in dermatology

Background: Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in 2012 in the image recognition challenge ImageNet LSVRC 2010 fueled expectations of the potential for using such systems in dermatology.Objective: To evaluate the ways in which machine learning has been utilized in dermatology to date and provide an overview of the findings in current literature on the subject.Methods: We conducted a systematic review of existing literature, identifying the literature through a systematic search of the PubMed database. Two doctors assessed screening and eligibility with respect to pre-determined inclusion and exclusion criteria.Results: A total of 2175 publications were identified, and 64 publications were included. We identified eight major categories where machine learning tools were tested in dermatology. Most systems involved image recognition tools that were primarily aimed at binary classification of malignant melanoma (MM). Short system descriptions and results of all included systems are presented in tables.Conclusions: We present a complete overview of artificial intelligence implemented in dermatology. Impressive outcomes were reported in all of the identified eight categories, but head-to-head comparison proved difficult. The many areas of dermatology where we identified machine learning tools indicate the diversity of machine learning.

Novel biomarkers for cardiovascular risk

Cardiovascular disease refers to different diseases involving the heart and/or the arteries and/or the veins. Cardiovascular disease, overall considered, is a notable source of morbidity and mortality worldwide. Therefore, several research studies are dedicated to explore, by means of biomarkers, the possiblity to calculate the cardiovascular risk both for the onset and for the complications of the related clinical manifestations such as coronary artery disease, carotid artery stenosis, peripheral artery disease, arterial aneurysm, chronic venous disease and venous thromboembolism. This review discusses the most updated information in the area of the novel biomarkers related to omics, imaging techniques and clinical data, that may help physicians in order to improve the knowledge and the management of the cardiovascular risk.

Skin tears and risk factors assessment: a systematic review on evidence-based medicine

Skin tears represent a common condition of traumatic wounds, which may be encountered in some categories of individuals at the extremes of age, such as infants and the elderly. Despite the high prevalence and cost of these lesions, there has been little investigation into the risk factors that lead to this condition. The aim of this review was to systematically evaluate the main risk factors involved in development of skin tears. We planned to include all the studies dealing with risk factors related to skin tears. Only publications in English were considered. We excluded all the studies that did not properly fit our research question and those with insufficient data. Of the 166 records found, 24 matched our inclusion criteria. After reading the full-text articles, we decided to exclude seven articles because of the following reasons: (1) not responding properly to our research questions and (2) insufficient data; the final set included 17 articles. From a literature search, we found the following main issues related to risk factors, which have been described in detail in this section: age-related skin changes, dehydration, malnutrition, sensory changes, mobility impairment, pharmacological therapies and mechanical factors related to skin care practices. Our findings clearly show that in frail populations (especially infant and elderly), the stratification risk, as a primary prevention strategy, is an effective tool in avoiding the development of chronic wounds. The development and the implementation of prevention strategies based on appropriate knowledge of the risk factors involved and the adoption of correct techniques during skin care practices could reduce or even avoid the onset of skin tears.

Study on the efficacy of surgery of the superficial venous system and of compression therapy at early stages of chronic venous disease for the prevention of chronic venous ulceration

The mainstay of treatment of chronic venous ulceration (CVU), as also suggested by current treatment guidelines for chronic venous disease (CVD), is represented by surgery and compression therapy for which there is strong evidence of their role in clinically relevant improvement in wound healing and also in the reduction of CVU recurrence, but no information is available as to whether or not these treatments provide effective protection from the onset of CVU. In our study, we have followed, for a median time of 13 years, a total of 3947 patients with CVD at classes C2-C3 of CEAP classification, treated with our treatment protocol (surgery and compression therapy) in order to track the natural history of these patients with regards to CVU development. We identified four groups of patients: 2354 patients (59·64%) (Group A) fully adherent to protocols; 848 patients (21·48%) (Group B) fully adherent to surgery and non-compliant to compression therapy; 432 patients (10·95%) (Group C) fully adherent to compression therapy and non-compliant to surgery; and 313 patients (7·93%) (Group D) non-compliant to either treatments. Regardless of compliance to treatments, the ulcer development rates were very similar between groups (range: 3·23-4.79%), with no statistical significance (P = 0·1522). Currents treatments used in the early stages of CVD appear to have no effects to progression to CVU. Additional longitudinal studies are required to confirm these findings.

From varices to venous ulceration: the story of chronic venous disease described by metalloproteinases

Chronic venous disease (CVD) and its most frightening complication, chronic venous ulceration (CVU), represent an important socioeconomic burden in the western world. Metalloproteinases have been identified in the pathogenesis of several vascular diseases such as venous problems. The aim of this study was to evaluate a broad range of metalloproteinases, such as matrix metalloproteinases (MMPs), ADAMs (a disintegrin and metalloproteinases) and ADAMTSs (a disintegrin and metalloproteinases with thrombospondin motifs) and their inhibitors, tissue inhibitor of metalloproteinases (TIMPs) and a related protein, neutrophil gelatinase-associated lipocalin (NGAL), in patients with CVD in order to correlate their serum levels with each stage of the disease. We performed a multicenter open-label study that comprised the enrolment of 541 patients with CVD of clinical stages C1-C6, (178 males, 363 females; mean age 57·29, median age 53·72, age range 29-81); 29 subjects without CVD were included in this study (9 males and 20 females; mean age 54·44, median age 50, age range 28-84) as the control group. Enzyme-linked immunosorbent assay (ELISA) was performed for measuring serum levels of proteases and related proteins. The study found that the serum elevation of MMP-2, ADAMTS-1 and ADAMTS-7 appeared to be correlated with the initial stages of CVD, whereas the serum elevation of MMP-1, MMP-8, MMP-9, NGAL, ADAM-10, ADAM-17 and ADAMTS-4 was particularly involved in skin change complications. This study showed that each stage of CVD may be described by particular patterns of metalloproteinases, and this may have therapeutic implications in discovering new targets and new drugs for the treatment of CVD.