Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera

Abstract

Pressure injuries are a common problem resulting in poor prognosis, long-term hospitalization, and increased medical costs in an aging society. This study developed a method to do automatic segmentation and area measurement of pressure injuries using deep learning models and a light detection and ranging (LiDAR) camera. We selected the finest photos of patients with pressure injuries, 528 in total, at National Taiwan University Hospital from 2016 to 2020. The margins of the pressure injuries were labeled by three board-certified plastic surgeons. The labeled photos were trained by Mask R-CNN and U-Net for segmentation. After the segmentation model was constructed, we made an automatic wound area measurement via a LiDAR camera. We conducted a prospective clinical study to test the accuracy of this system. For automatic wound segmentation, the performance of the U-Net (Dice coefficient (DC): 0.8448) was better than Mask R-CNN (DC: 0.5006) in the external validation. In the prospective clinical study, we incorporated the U-Net in our automatic wound area measurement system and got 26.2% mean relative error compared with the traditional manual method. Our segmentation model, U-Net, and area measurement system achieved acceptable accuracy, making them applicable in clinical circumstances.

Principles, applications, and future of artificial intelligence in dermatology

“…One of the primary methods in identifying and classifying skin lesions is segmenting the lesion from the backdrop of normal skin. Multiple studies have explored determining and measuring ill-defined wound boundaries using techniques that simplify images down to the pixel level ( 58 – 61 ). Recent work has been done to apply these techniques into broader hospital systems to predict pressure ulcers ( 62 ), with the ultimate goal of pressure ulcer prevention ( 63 , 64 ).…”

YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification

“…However, these studies have their limitations, including the need for labeled datasets and the requirement for the manual selection of parameters. The authors of [66] proposed a system that utilized a LiDAR sensor and deep learning models for automatically assessing pressure injuries, achieving satisfactory accuracy with U-Net outperforming Mask R-CNN; Zahia et al [67] used CNNs for automatic tissue classification in pressure injuries, achieving an overall classification accuracy of 92.01%; Liu et al [68] developed a system using deep learning algorithms to identify pressure ulcers and achieved high accuracy with the Inception-ResNet-v2 model; Fergus et al [69] used a faster region-based convolutional neural network and a mobile platform to classify pressure ulcers; Swerdlow et al [70] applied the Mask-R-CNN algorithm for simultaneous segmentation and classification of stage 1-4 pressure injuries; Elmogy et al [71] proposed a tissue classification system for pressure ulcers using a 3D-CNN. Table 4 summarizes these studies.…”

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

“…However, the implementation of this method requires advanced knowledge in signal processing, or the samples should be conditioned according to the physical characteristics of the patient. Also, the pressure array information is preferably exhibited in color images to be processed [16][17][18].…”