Video-Based Automatic Quantification of Leg Edema: a Pilot Study in Patients With Hemodialysis With and Without Heart Failure　― Proof-of-Concept Study ―

Abstract

Background: Reliable assessment of pitting edema remains a challenge, especially in remote care, because it is inherently subjective. We developed a video-based deep learning (DL) model to objectively classify the severity of pitting edema.

Methods and Results: A total of 247 videos from 34 consecutive hemodialysis patients were analyzed. A convolutional neural-network (EfficientNetB0) was trained using pre and postpressing pretibial images graded on a 0–4 scale. The model achieved 81.5% accuracy, 81.2% sensitivity, and 81.9% specificity in distinguishing grades 3–4 edema from grades 0–1. For extreme cases (grade 0 vs. 4), accuracy improved to 85.8%.

Conclusions: This pilot study demonstrated feasibility of video-based DL for edema detection. Larger, more diverse datasets and clinical validation are needed for generalization.