Abstract
Assessing swelling is crucial for the surgical treatment of lower limb lymphedema and is often conducted using 2-dimensional (2D) imaging, which is available in most outpatient settings. Advanced methods, such as taping and computed tomography (CT), are limited by time and equipment constraints. The neural radiance field (NeRF) uses deep learning to reconstruct 3-dimensional (3D) scenes from 2D images, overcoming the challenges, particularly reflections and refractions, associated with photogrammetry. This study aimed to evaluate the effectiveness of Luma 3D, an iPhone application that utilizes NeRF, for volumetric assessment in an outpatient setting. An 88-year-old woman with chronic lymphedema was evaluated over a period of 12 months using NeRF-based 3D modeling. She presented with walking difficulty, owing to congestive dermatitis and persistent exudate, and was treated nonsurgically using topical applications and compression therapy. Initial CT scans showed a bilateral volume of 19,500 mL from the knee to the foot, whereas 3D measurements indicated a total volume of 20,100 mL. After 12 months of treatment, a CT scan showed a volume of 19,900 mL, whereas the 3D application measured a volume of 20,300 mL, demonstrating the capability of NeRF in volume change assessment, which could not be achieved by 2D assessments. Therefore, NeRF offers a promising solution for the 3D assessment of lymphedema in outpatient settings, providing accurate and noninvasive volumetric measurements. This approach addresses the limitations of traditional imaging methods and has potential applications in the fields of medical equipment and telemedicine.