The role of artificial intelligence technology in the care of diabetic foot ulcers: the past, the present, and the future

Artificial intelligence and prescription of antibiotic therapy: present and future

INTRODUCTION

In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.

AREAS COVERED

In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024.

EXPERT OPINION

Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

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.

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.

The efficacy and safety of non-surgical treatment of diabetic foot wound infections and ulcers: A systemic review and meta-analysis

This meta-analysis evaluates the efficacy and safety of non-surgical treatments for diabetic foot ulcers and infections. After a rigorous literature review, seven studies were selected for detailed analysis. The findings demonstrate that non-surgical treatments significantly reduce wound infection rates (standardized mean difference [SMD] = -15.15, 95% confidence interval [CI]: [-19.05, -11.25], p < 0.01) compared to surgical methods. Ulcer healing rates were found to be comparable between non-surgical and surgical approaches (SMD = 0.07, 95% CI: [-0.38, 0.51], p = 0.15). Importantly, the rate of amputations within 6 months post-treatment was significantly lower in the non-surgical group (risk ratio [RR] = 0.19, 95% CI: [0.09, 0.41], p < 0.01). Additionally, a lower mortality rate was observed in patients treated non-surgically (RR = 0.28, 95% CI: [0.13, 0.59], p < 0.01). These results affirm the effectiveness and safety of non-surgical interventions in managing diabetic foot ulcers, suggesting that they should be considered a viable option in diabetic foot care.

Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning

BACKGROUND

The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form, a comprehensive dataset named Zivot with patient population clinical feature breakdowns and a baseline for DFU detection using this dataset and a UNet architecture.

RESULTS

Following this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset, and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics.

CONCLUSIONS

This work and the Zivot database offer a foundation for further exploration of holistic and multimodal approaches to DFU research.

Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications

Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs. Remote monitoring and automated classification are expected to revolutionize wound care by allowing patients to self-monitor their wound pathology, assist in the remote triaging of patients by clinicians, and allow for more immediate interventions when necessary. This scoping review provides an overview of applicable CV and ML techniques. This includes automated CV methods developed for remote assessment of wound photographs, as well as predictive ML algorithms that leverage heterogeneous data streams. We discuss the benefits of such applications and the role they may play in diabetic foot care moving forward. We highlight both the need for, and possibilities of, computational sensing systems to improve diabetic foot care and bring greater knowledge to patients in need.

Electrodermal activity and heart rate variability for detection of peripheral abnormalities in type 2 diabetes: A review

Modern medicine exhibits an upward trend towards non-invasive methods for early detection of disease and long-term monitoring of patients' health. Diabetes mellitus and its complications are a promising area for implementation of new medical diagnostic devices. One of the most serious complications of diabetes is diabetic foot ulcer. The main causes responsible for diabetic foot ulcer are ischemia caused by peripheral artery disease and diabetic neuropathy caused by polyol pathway-induced oxidative stress. Autonomic neuropathy impairs function of sweat glands, which can be measured by electrodermal activity. On the other hand, autonomic neuropathy leads to changes in heart rate variability, which is used to assess autonomic regulation of the sinoatrial node. Both methods are enough sensitive to detect pathological changes caused by autonomic neuropathy and are promising screening methods for early diagnosis of diabetic neuropathy, which could prevent the onset of diabetic ulcer.

Current status and progress in research on dressing management for diabetic foot ulcer

Diabetic foot ulcer (DFU) is a major complication of diabetes and is associated with a high risk of lower limb amputation and mortality. During their lifetime, 19%-34% of patients with diabetes can develop DFU. It is estimated that 61% of DFU become infected and 15% of those with DFU require amputation. Furthermore, developing a DFU increases the risk of mortality by 50%-68% at 5 years, higher than some cancers. Current standard management of DFU includes surgical debridement, the use of topical dressings and wound decompression, vascular assessment, and glycemic control. Among these methods, local treatment with dressings builds a protective physical barrier, maintains a moist environment, and drains the exudate from DFU wounds. This review summarizes the development, pathophysiology, and healing mechanisms of DFU. The latest research progress and the main application of dressings in laboratory and clinical stage are also summarized. The dressings discussed in this review include traditional dressings (gauze, oil yarn, traditional Chinese medicine, and others), basic dressings (hydrogel, hydrocolloid, sponge, foam, film agents, and others), bacteriostatic dressings, composite dressings (collagen, nanomaterials, chitosan dressings, and others), bioactive dressings (scaffold dressings with stem cells, decellularized wound matrix, autologous platelet enrichment plasma, and others), and dressings that use modern technology (3D bioprinting, photothermal effects, bioelectric dressings, microneedle dressings, smart bandages, orthopedic prosthetics and regenerative medicine). The dressing management challenges and limitations are also summarized. The purpose of this review is to help readers understand the pathogenesis and healing mechanism of DFU, help physicians select dressings correctly, provide an updated overview of the potential of biomaterials and devices and their application in DFU management, and provide ideas for further exploration and development of dressings. Proper use of dressings can promote DFU healing, reduce the cost of treating DFU, and reduce patient pain.