Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis

Development of a Deep Learning-Based Model for Pressure Injury Surface Assessment

AIM

To develop a deep learning-based smart assessment model for pressure injury surface.

DESIGN

Exploratory analysis study.

METHODS

Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.

RESULTS

From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm2).

CONCLUSION

The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.

IMPLICATIONS AND IMPACT

This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.

REPORTING METHOD

The STROBE checklist guided study reporting.

PATIENT OR PUBLIC CONTRIBUTION

Patients provided image resources for model training.

Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities

With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.

Combined treatment of umbilical cord Wharton's jelly-derived mesenchymal stem cells and platelet-rich plasma for a surgical patient with hospital-acquired pressure ulcer: a case report and literature review

Hospital-acquired pressure ulcers (HAPUs) are still an important worldwide issue related to the safety and quality of patient care, which are among the top five adverse events reported. Patients who develop HAPUs have longer stays in the hospital than necessary, are at a greater risk of infections, and are more likely to die. Surgical patients are prone to developing PUs because they often remain immobile for extended periods of time, and their surgical procedures may limit the flow of blood oxygen and nutrition and lead to a decrease in muscle tone. Mesenchymal stem cells (MSCs) represent an attractive stem cell source for tissue regeneration in clinical applications, which have been demonstrated to improve wound healing through re-epithelialization, increased angiogenesis, and granulation tissue formation. Here, we present the case of an emergency surgical patient who developed an ulcer on the right heel during hospitalization. The human umbilical cord Wharton's jelly-derived MSCs (WJ-MSCs) re-suspended in platelet-rich plasma (PRP) were injected into ulcer margins. Four days after the WJ-MSC application, the patient showed progressive healing of the PU. From days 4 to 33, granulation tissue formation and re-epithelialization were clearly observed. The ulcer was almost healed completely on day 47, and the pain in the patient's wound area also decreased. Thus, intradermal transplantation of WJ-MSCs and PRP was safe and effective for treatment in patients with pressure ulcers. WJ-MSCs, together with PRP, may offer a promising treatment option for wound healing.

Automated wound segmentation and classification of seven common injuries in forensic medicine

In forensic medical investigations, physical injuries are documented with photographs accompanied by written reports. Automatic segmentation and classification of wounds on these photographs could provide forensic pathologists with a tool to improve the assessment of injuries and accelerate the reporting process. In this pilot study, we trained and compared several preexisting deep learning architectures for image segmentation and wound classification on forensically relevant photographs in our database. The best scores were a mean pixel accuracy of 69.4% and a mean intersection over union (IoU) of 48.6% when evaluating the trained models on our test set. The models had difficulty distinguishing the background from wounded areas. As an example, image pixels showing subcutaneous hematomas or skin abrasions were assigned to the background class in 31% of cases. Stab wounds, on the other hand, were reliably classified with a pixel accuracy of 93%. These results can be partially attributed to undefined wound boundaries for some types of injuries, such as subcutaneous hematoma. However, despite the large class imbalance, we demonstrate that the best trained models could reliably distinguish among seven of the most common wounds encountered in forensic medical investigations.

Smart diabetic foot ulcer scoring system

Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including the potential for amputations, highlighting the urgent need for improved diagnostic tools and care standards in the field. To address this issue, the objective of this study was to develop and evaluate the Smart Diabetic Foot Ulcer Scoring System, ScoreDFUNet, which incorporates artificial intelligence (AI) and image analysis techniques, aiming to enhance the precision and consistency of diabetic foot ulcer assessment. ScoreDFUNet demonstrates precise categorization of DFU images into "ulcer," "infection," "normal," and "gangrene" areas, achieving a noteworthy accuracy rate of 95.34% on the test set, with elevated levels of precision, recall, and F1 scores. Comparative evaluations with dermatologists affirm that our algorithm consistently surpasses the performance of junior and mid-level dermatologists, closely matching the assessments of senior dermatologists, and rigorous analyses including Bland-Altman plots and significance testing validate the robustness and reliability of our algorithm. This innovative AI system presents a valuable tool for healthcare professionals and can significantly improve the care standards in the field of diabetic foot ulcer assessment.

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.

Region-Based Semi-Two-Stream Convolutional Neural Networks for Pressure Ulcer Recognition

Pressure ulcers are a common, painful, costly, and often preventable complication associated with prolonged immobility in bedridden patients. It is a significant health problem worldwide because it is frequently seen in inpatients and has high treatment costs. For the treatment to be effective and to ensure an international standardization for all patients, it is essential that the diagnosis of pressure ulcers is made in the early stages and correctly. Since invasive methods of obtaining information can be painful for patients, different methods are used to make a correct diagnosis. Image-based diagnosis method is one of them. By using images obtained from patients, it will be possible to obtain successful results by keeping patients away from such painful situations. At this stage, disposable wound rulers are used in clinical practice to measure the length, width, and depth of patients' wounds. The information obtained is then entered into tools such as the Braden Scale, the Norton Scale, and the Waterlow Scale to provide a formal assessment of risk for pressure ulcers. This paper presents a novel benchmark dataset containing pressure ulcer images and a semi-two-stream approach that uses the original images and the cropped wound areas together for diagnosing the stage of pressure ulcers. Various state-of-the-art convolutional neural network (CNN) architectures are evaluated on this dataset. Our experimental results (test accuracy of 93%, the precision of 93%, the recall of 92%, and the F1-score of 93%) show that the proposed semi-two-stream method improves recognition results compared to the base CNN architectures.

PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography

Cardiovascular disease, primarily caused by atherosclerotic plaque formation, is a significant health concern. The early detection of these plaques is crucial for targeted therapies and reducing the risk of cardiovascular diseases. This study presents PlaqueNet, a solution for segmenting coronary artery plaques from coronary computed tomography angiography (CCTA) images. For feature extraction, the advanced residual net module was utilized, which integrates a deepwise residual optimization module into network branches, enhances feature extraction capabilities, avoiding information loss, and addresses gradient issues during training. To improve segmentation accuracy, a depthwise atrous spatial pyramid pooling based on bicubic efficient channel attention (DASPP-BICECA) module is introduced. The BICECA component amplifies the local feature sensitivity, whereas the DASPP component expands the network's information-gathering scope, resulting in elevated segmentation accuracy. Additionally, BINet, a module for joint network loss evaluation, is proposed. It optimizes the segmentation model without affecting the segmentation results. When combined with the DASPP-BICECA module, BINet enhances overall efficiency. The CCTA segmentation algorithm proposed in this study outperformed the other three comparative algorithms, achieving an intersection over Union of 87.37%, Dice of 93.26%, accuracy of 93.12%, mean intersection over Union of 93.68%, mean Dice of 96.63%, and mean pixel accuracy value of 96.55%.

Augmented Decision-Making in wound Care: Evaluating the clinical utility of a Deep-Learning model for pressure injury staging

BACKGROUND

Precise categorization of pressure injury (PI) stages is critical in determining the appropriate treatment for wound care. However, the expertise necessary for PI staging is frequently unavailable in residential care settings.

OBJECTIVE

This study aimed to develop a convolutional neural network (CNN) model for classifying PIs and investigate whether its implementation can allow physicians to make better decisions for PI staging.

METHODS

Using 3,098 clinical images (2,614 and 484 from internal and external datasets, respectively), a CNN was trained and validated to classify PIs and other related dermatoses. A two-part survey was conducted with 24 dermatology residents, ward nurses, and medical students to determine whether the implementation of the CNN improved initial PI classification decisions.

RESULTS

The top-1 accuracy of the model was 0.793 (95% confidence interval [CI], 0.778-0.808) and 0.717 (95% CI, 0.676-0.758) over the internal and external testing sets, respectively. The accuracy of PI staging among participants was 0.501 (95% CI, 0.487-0.515) in Part I, improving by 17.1% to 0.672 (95% CI, 0.660-0.684) in Part II. Furthermore, the concordance between participants increased significantly with the use of the CNN model, with Fleiss' κ of 0.414 (95% CI, 0.410-0.417) and 0.641 (95% CI, 0.638-0.644) in Parts I and II, respectively.

CONCLUSIONS

The proposed CNN model can help classify PIs and relevant dermatoses. In addition, augmented decision-making can improve consultation accuracy while ensuring concordance between the clinical decisions made by a diverse group of health professionals.

K. SK, Jayaraman S. Automatic foot ulcer segmentation using conditional generative adversarial network (AFSegGAN): A wound management system

Effective wound care is essential to prevent further complications, promote healing, and reduce the risk of infection and other health issues. Chronic wounds, particularly in older adults, patients with disabilities, and those with pressure, venous, or diabetic foot ulcers, cause significant morbidity and mortality. Due to the positive trend in the number of individuals with chronic wounds, particularly among the growing elderly and diabetes populations, it is imperative to develop novel technologies and practices for the best practice clinical management of chronic wounds to minimize the potential health and economic burdens on society. As wound care is managed in hospitals and community care, it is crucial to have quantitative metrics like wound boundary and morphological features. The traditional visual inspection technique is purely subjective and error-prone, and digitization provides an appealing alternative. Various deep-learning models have earned confidence; however, their accuracy primarily relies on the image quality, the dataset size to learn the features, and experts' annotation. This work aims to develop a wound management system that automates wound segmentation using a conditional generative adversarial network (cGAN) and estimate the wound morphological parameters. AFSegGAN was developed and validated on the MICCAI 2021-foot ulcer segmentation dataset. In addition, we use adversarial loss and patch-level comparison at the discriminator network to improve the segmentation performance and balance the GAN network training. Our model outperformed state-of-the-art methods with a Dice score of 93.11% and IoU of 99.07%. The proposed wound management system demonstrates its abilities in wound segmentation and parameter estimation, thereby reducing healthcare workers' efforts to diagnose or manage wounds and facilitating remote healthcare.

YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification

Pressure ulcers are significant healthcare concerns affecting millions of people worldwide, particularly those with limited mobility. Early detection and classification of pressure ulcers are crucial in preventing their progression and reducing associated morbidity and mortality. In this work, we present a novel approach that uses YOLOv5, an advanced and robust object detection model, to detect and classify pressure ulcers into four stages and non-pressure ulcers. We also utilize data augmentation techniques to expand our dataset and strengthen the resilience of our model. Our approach shows promising results, achieving an overall mean average precision of 76.9% and class-specific mAP50 values ranging from 66% to 99.5%. Compared to previous studies that primarily utilize CNN-based algorithms, our approach provides a more efficient and accurate solution for the detection and classification of pressure ulcers. The successful implementation of our approach has the potential to improve the early detection and treatment of pressure ulcers, resulting in better patient outcomes and reduced healthcare costs.

Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review

It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection and periodic ulcer monitoring can considerably decrease amputation rates. Recent research has demonstrated that computer vision can be used to identify foot ulcers and perform non-contact telemetry by using ulcer and tissue area segmentation. However, the applications are limited to controlled lighting conditions, and expert knowledge is required for dataset annotation. This paper reviews the latest publications on the use of artificial intelligence for ulcer area detection and segmentation. The PRISMA methodology was used to search for and select articles, and the selected articles were reviewed to collect quantitative and qualitative data. Qualitative data were used to describe the methodologies used in individual studies, while quantitative data were used for generalization in terms of dataset preparation and feature extraction. Publicly available datasets were accounted for, and methods for preprocessing, augmentation, and feature extraction were evaluated. It was concluded that public datasets can be used to form a bigger, more diverse datasets, and the prospects of wider image preprocessing and the adoption of augmentation require further research.

Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers

Artificial intelligence (AI) in medical care can raise diagnosis accuracy and improve its uniformity. This study developed a diagnostic imaging system for chronic wounds that can be used in medically underpopulated areas. The image identification algorithm searches for patterns and makes decisions based on information obtained from pixels rather than images. Images of 50 patients with pressure sores treated at Kobe University Hospital were examined. The algorithm determined the presence of necrosis with a significant difference (p = 3.39 × 10−5). A threshold value was created with a luminance difference of 50 for the group with necrosis of 5% or more black pixels. In the no-necrosis group with less than 5% black pixels, the threshold value was created with a brightness difference of 100. The “shallow wounds” were distributed below 100, whereas the “deep wounds” were distributed above 100. When the algorithm was applied to 24 images of 23 new cases, there was 100% agreement between the specialist and the algorithm regarding the presence of necrotic tissue and wound depth evaluation. The algorithm identifies the necrotic tissue and wound depth without requiring a large amount of data, making it suitable for application to future AI diagnosis systems for chronic wounds.

AI-Assisted Assessment of Wound Tissue with Automatic Color and Measurement Calibration on Images Taken with a Smartphone

Wound assessment is essential for evaluating wound healing. One cornerstone of wound care practice is the use of clinical guidelines that mandate regular documentation, including wound size and wound tissue composition, to determine the rate of wound healing. The traditional method requires wound care professionals to manually measure the wound area and tissue composition, which is time-consuming, costly, and difficult to reproduce. In this work, we propose an approach for automatic wound assessment that incorporates automatic color and measurement calibration and artificial intelligence algorithms. Our approach enables the comparison of images taken at different times, even if they were taken under different lighting conditions, distances, lenses, and camera sensors. We designed a calibration chart and developed automatic algorithms for color and measurement calibration. The wound area and wound composition on the images were annotated by three physicians with more than ten years of experience. Deep learning models were then developed to mimic what the physicians did on the images. We examined two network variants, U-Net with EfficientNet and U-Net with MobileNetV2, on wound images with a size of 1024 × 1024 pixels. Our best-performing algorithm achieved a mean intersection over union (IoU) of 0.6964, 0.3957, 0.6421, and 0.1552 for segmenting a wound area, epithelialization area, granulation tissue, and necrotic tissue, respectively. Our approach was able to accurately segment the wound area and granulation tissue but was inconsistent with respect to the epithelialization area and necrotic tissue. The calibration chart, which helps calibrate colors and scales, improved the performance of the algorithm. The approach could provide a thorough assessment of the wound, which could help clinicians tailor treatment to the patient's condition.

Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera

Pressure injuries are a common problem resulting in poor prognosis, long-term hospitalization, and increased medical costs in an aging society. This study developed a method to do automatic segmentation and area measurement of pressure injuries using deep learning models and a light detection and ranging (LiDAR) camera. We selected the finest photos of patients with pressure injuries, 528 in total, at National Taiwan University Hospital from 2016 to 2020. The margins of the pressure injuries were labeled by three board-certified plastic surgeons. The labeled photos were trained by Mask R-CNN and U-Net for segmentation. After the segmentation model was constructed, we made an automatic wound area measurement via a LiDAR camera. We conducted a prospective clinical study to test the accuracy of this system. For automatic wound segmentation, the performance of the U-Net (Dice coefficient (DC): 0.8448) was better than Mask R-CNN (DC: 0.5006) in the external validation. In the prospective clinical study, we incorporated the U-Net in our automatic wound area measurement system and got 26.2% mean relative error compared with the traditional manual method. Our segmentation model, U-Net, and area measurement system achieved acceptable accuracy, making them applicable in clinical circumstances.

Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review

Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients' Electronic Health Records (EHR).