Development of a Method for Clinical Evaluation of Artificial Intelligence-Based Digital Wound Assessment Tools.

Importance: Accurate assessment of wound area and percentage of granulation tissue (PGT) are important for optimizing wound care and healing outcomes. Artificial intelligence (AI)-based wound assessment tools have the potential to improve the accuracy and consistency of wound area and PGT measurement, while improving efficiency of wound care workflows. Objective: To develop a quantitative and qualitative method to evaluate AI-based wound assessment tools compared with expert human assessments. Design, Setting, and Participants: This diagnostic study was performed across 2 independent wound centers using deidentified wound photographs collected for routine care (site 1, 110 photographs taken between May 1 and 31, 2018; site 2, 89 photographs taken between January 1 and December 31, 2019). Digital wound photographs of patients were selected chronologically from the electronic medical records from the general population of patients visiting the wound centers. For inclusion in the study, the complete wound edge and a ruler were required to be visible; circumferential ulcers were specifically excluded. Four wound specialists (2 per site) and an AI-based wound assessment service independently traced wound area and granulation tissue. Main Outcomes and Measures: The quantitative performance of AI tracings was evaluated by statistically comparing error measure distributions between test AI traces and reference human traces (AI vs human) with error distributions between independent traces by 2 humans (human vs human). Quantitative outcomes included statistically significant differences in error measures of false-negative area (FNA), false-positive area (FPA), and absolute relative error (ARE) between AI vs human and human vs human comparisons of wound area and granulation tissue tracings. Six masked attending physician reviewers (3 per site) viewed randomized area tracings for AI and human annotators and qualitatively assessed them. Qualitative outcomes included statistically significant difference in the absolute difference between AI-based PGT measurements and mean reviewer visual PGT estimates compared with PGT estimate variability measures (ie, range, standard deviation) across reviewers. Results: A total of 199 photographs were selected for the study across both sites; mean (SD) patient age was 64 (18) years (range, 17-95 years) and 127 (63.8%) were women. The comparisons of AI vs human with human vs human for FPA and ARE were not statistically significant. AI vs human FNA was slightly elevated compared with human vs human FNA (median [IQR], 7.7% [2.7%-21.2%] vs 5.7% [1.6%-14.9%]; P < .001), indicating that AI traces tended to slightly underestimate the human reference wound boundaries compared with human test traces. Two of 6 reviewers had a statistically higher frequency in agreement that human tracings met the standard area definition, but overall agreement was moderate (352 yes responses of 583 total responses [60.4%] for AI and 793 yes responses of 1166 total responses [68.0%] for human tracings). AI PGT measurements fell in the typical range of variation in interreviewer visual PGT estimates; however, visual PGT estimates varied considerably (mean range, 34.8%; mean SD, 19.6%). Conclusions and Relevance: This study provides a framework for evaluating AI-based digital wound assessment tools that can be extended to automated measurements of other wound features or adapted to evaluate other AI-based digital image diagnostic tools. As AI-based wound assessment tools become more common across wound care settings, it will be important to rigorously validate their performance in helping clinicians obtain accurate wound assessments to guide clinical care.

A Framework to Assist Providers in the Management of Patients with Chronic, Nonhealing Wounds.

GENERAL PURPOSE: To describe the development of an evidence-based wound electronic medical record (WEMR) framework for providers to execute timely, protocol-based, best-practice care for patients with chronic, nonhealing wounds. TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After completing this continuing education activity, you should be better able to: ABSTRACT: The care of patients with nonhealing wounds involves a host of treatment modalities. The authors developed a wound-specific framework to enhance provider management of these wounds and a summary sheet to involve patients and caregivers in their own healthcare to improve treatment adherence and outcomes. Implementing evidence-based practice for chronic wounds enables corrective actions to optimize care.

A Wolf in Sheep's Clothing: An Unusual Presentation of Diabetic Myonecrosis.

GENERAL PURPOSE: To provide information about the diagnosis and treatment of diabetic myonecrosis (DMN).This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care.After participating in this educational activity, the participant should be better able to:1. Cite the incidence and symptomatology of diabetic myonecrosis.2. Identify the diagnostic tests associated with DMN.3. Summarize the evidence-based treatments for DMN.Diabetic myonecrosis is a rare complication of poorly controlled diabetes mellitus that presents similarly to many common conditions such as cellulitis, abscess, and fasciitis. Therefore, a high index of suspicion is required for diagnosis. Magnetic resonance imaging is the investigative test of choice. Treatment includes antiplatelet therapy, nonsteroidal anti-inflammatory agents, and glycemic control.

Wound Care Center of Excellence: A Process for Continuous Monitoring and Improvement of Wound Care Quality.

GENERAL PURPOSE: To provide information about a study using a new process for continuous monitoring to improve chronic wound care quality.This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care.After completing this continuing education activity, you should be better able to:1. Recognize problems associated with chronic wound care.2. Identify methods used in this project to improve care.3. Illustrate the findings from this and similar projects and implications for providing improved wound care.Patients with chronic wounds require complex care because of comorbidities that can affect healing. Therefore, the goal of this project was to develop a system of reviewing all hospitalized patients seen by the study authors' wound care service on a weekly basis to decrease readmissions, morbidity, and mortality. Weekly multidisciplinary conferences were conducted to evaluate patient data and systematically assess for adherence to wound care protocols, as well as to create and modify patient care plans. This review of pathology and the performance of root-cause analyses often led to improved patient care.

A Perioperative Approach to Increase Limb Salvage When Treating Foot Ulcers in Patients With Diabetes.

Foot ulceration in patients with diabetes increases the risk of lower extremity amputation. Major amputations produce substantial adverse consequences, increase length of hospital stay, diminish quality of life, and increase mortality. In this article, we describe approaches that decrease amputations and improve the quality of life for patients with diabetes and foot ulcers. We highlight the role of the perioperative nurse, who is essential to providing optimal patient care in the perioperative period. Perioperative care of patients with diabetes involves providing optimal surveillance for a break in the skin of the foot, screening for neuropathy, following guidelines for foot ulcer infections, preparing for pathophysiology-based debridement, using adjuvant therapies, and offloading the patient's affected foot. Nurses should understand the disease process and pathophysiology and how to use these approaches in the perioperative setting to assist in curtailing the morbidity and mortality associated with foot ulcers in patients with diabetes.

The Future of Data-Driven Wound Care.

Care for patients with chronic wounds can be complex, and the chances of poor outcomes are high if wound care is not optimized through evidence-based protocols. Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. This article provides an overview of the application of big data analytics and machine learning in health care, highlights important recent advances, and discusses how these technologies may revolutionize advanced wound care.