Enhancing Clinical Assessment of Skin Ulcers with Automated and Objective Convolutional Neural Network-Based Segmentation and 3D Analysis

Skin ulcers are open wounds on the skin characterized by the loss of epidermal tissue. Skin ulcers can be acute or chronic, with chronic ulcers persisting for over six weeks and often being difficult to heal. Treating chronic wounds involves periodic visual inspections to control infection and maintain moisture balance, with edge and size analysis used to track wound evolution. This condition mostly affects individuals over 65 years old and is often associated with chronic conditions such as diabetes, vascular issues, heart diseases, and obesity. Early detection, assessment, and treatment are crucial for recovery. This study introduces a method for automatically detecting and segmenting skin ulcers using a Convolutional Neural Network and two-dimensional images. Additionally, a three-dimensional image analysis is employed to extract key clinical parameters for patient assessment. The developed system aims to equip specialists and healthcare providers with an objective tool for assessing and monitoring skin ulcers. An interactive graphical interface, implemented in Unity3D, allows healthcare operators to interact with the system and visualize the extracted parameters of the ulcer. This approach seeks to address the need for precise and efficient monitoring tools in managing chronic wounds, providing a significant advancement in the field by automating and improving the accuracy of ulcer assessment.