Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration.

This study aims to advance the field of digital wound care by developing and evaluating convolutional neural network (CNN) architectures for the automatic classification of maceration, a significant wound healing complication, in 458 annotated wound images. Detection and classification of maceration can improve patient outcomes. Several CNN models were compared and MobileNetV2 emerged as the top-performing model, achieving the highest accuracy despite having fewer parameters.

Automation Bias in AI-Decision Support: Results from an Empirical Study.

Introduction: Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, self-confidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention.

Clinical characteristics and treatment modalities in uremic and non uremic calciphylaxis - a dermatological single-center experience.

Calciphylaxis (CP) is a serious, potentially life-threatening disease that presents with medial calcification of small-sized vessels and painful ischemic ulcerations. Although calciphylaxis is frequently seen in patients with end-stage kidney disease on dialysis (calcific uremic arteriolopathy, CUA), there are reported cases of nonuremic calciphylaxis (NUC), which often remain undiagnosed. We conducted a retrospective chart review at our dermatological hospital and evaluated data concerning the epidemiology, comorbidities, medication, laboratory abnormalities, and therapeutic approaches of 60 patients diagnosed with calciphylaxis between 01/2012 and 12/2022.

Design and Implementation of an ETL-Process to Transfer Wound-Related Data into a Standardized Common Data Model.

For observational studies, which are relevant especially for chronic conditions like chronic wounds, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) offers a standardized database schema. In this study an ETL process for the transition of wound related data was developed. After understanding the data in general and mapping the relevant codes to concepts available in OMOP, the ETL process was implemented.

Predilection sites of pyoderma gangrenosum: Retrospective study of 170 clearly diagnosed patients.

Pyoderma gangrenosum (PG) is a non-infectious, neutrophilic dermatosis that was difficult to diagnose in clinical practice. Today, the PARACELSUS score is a validated tool for diagnostics. Based on this score, patients with clearly diagnosed PG were examined with regard to predilection sites.

Poor Adherence to Self-Applied Topical Drug Treatment Is a Common Source of Low Lesion Clearance in Patients with Actinic Keratosis-A Cross-Sectional Study.

Background: Many treatments for actinic keratosis (AK) have been proven efficient in clinical trials. However, patients with AK may still experience unsatisfactory therapeutic outcomes in clinical practice.

Objectives: To investigate patient adherence to self-applied topical interventions for AK and to explore factors associated with adherence in a real-world setting.

Can Synthetic Images Improve CNN Performance in Wound Image Classification?

For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set.

Automatic Wound Type Classification with Convolutional Neural Networks.

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations.

An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers.

Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images.

Clinical Manifestation of Cutaneous Leishmaniasis Following a Mechanical Trauma.

Unusual skin ulcers frequently represent a diagnostic challenge. When the most common disease entities such as arterial, venous or diabetic ulcers have been excluded, the question of further differential diagnoses and procedures arises. Other possible causes include chronic inflammatory diseases, neoplasia, self-inflicted wounds, primary infectious diseases and physical/chemical damage to the skin.