An artificial intelligence-enabled smartphone app for real-time pressure injury assessment

Abstract

The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%–100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.

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

“…Hence, it is important to highlight that because the performance indicators employed in these research studies differ, it is challenging to directly compare the results. Our model outperformed [29,69], as demonstrated in the table, with superior metrics and a significantly higher mAP value. These results demonstrate the potential of our model for accurate pressure ulcer detection and classification.…”

“…However, previous studies have limitations, such as small dataset sizes, reliance on limited feature extraction methods, and lack of generalizability. Therefore, a more comprehensive and accurate solution to pressure ulcer detection and classification is needed [27][28][29][30][31][32][33].…”

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review

“…Current image-based wound management practices, often involving manual wound photography and assessment carried out by nurses, are time-and labor-intensive [21]. In contrast, models of care augmented with ML-enabled methods can be automated [22,23]. The portability of these methods might also be employed to conduct such assessments remotely [24], reducing patient travel burden and improving access to wound care in rural areas [25,26].…”