An Overview of Clinical Examinations in the Evaluation and Assessment of Arterial and Venous Insufficiency Wounds

Abstract

Arterial and venous insufficiency are two major causes of chronic wounds with different etiology, pathophysiology, and clinical manifestations. With recent advancements in clinical examination, clinicians are able to obtain an accurate diagnosis of the underlying disease, which plays an important role in the treatment planning and management of patients. Arterial ulcers are mainly caused by peripheral artery diseases (PADs), which are traditionally examined by physical examination and non-invasive arterial Doppler studies. However, advanced imaging modalities, such as computed tomography angiography (CTA) and indocyanine green (ICG) angiography, have become important studies as part of a comprehensive diagnostic process. On the other hand, chronic wounds caused by venous insufficiency are mainly evaluated by duplex ultrasonography and venography. Several scoring systems, including Clinical–Etiology–Anatomy–Pathophysiology (CEAP) classification, the Venous Clinical Severity Score (VCSS), the Venous Disability Score, and the Venous Segmental Disease Score (VSDS) are useful in defining disease progression. In this review, we provide a comprehensive overlook of the most widely used and available clinical examinations for arterial and venous insufficiency wounds.

Validating Wound Severity Assessment via Region-Anchored Convolutional Neural Network Model for Mobile Image-Based Size and Tissue Classification

“…An overview of clinical examinations for evaluating arterial and venous insufficiency was presented using a CNN-based method [ 13 ]. The research made use of a pre-trained VGG-19 system that was developed particularly to divide ulcer pictures into the two aforementioned groups.…”

“…The “wound” category had a 82% reliability, compared to 72% for drainage and 97% for steri strip. On the other hand, Guerra and coworkers [ 13 ] suggested a CNN-based approach for separating pictures of ulcers from non-ulcer sources including VLUs. They used a pre-trained VGG-19 system programmed to categorise ulcer photos into both of the aforementioned groups.…”