Andersen Niels K, Trøjgaard Pernille, Herschend Nana O, Størling Zenia M
VENZO.Nxt, Copenhagen, Denmark.
Coloplast A/S Denmark, Humlebaek, Denmark.
Front Artif Intell. 2020 Sep 10;3:72. doi: 10.3389/frai.2020.00072. eCollection 2020.
For people living with an ostomy, development of peristomal skin complications (PSCs) is the most common post-operative challenge. A visual sign of PSCs is discoloration (redness) of the peristomal skin often resulting from leakage of ostomy output under the baseplate. If left unattended, a mild skin condition may progress into a severe disorder; consequently, it is important to monitor discoloration and leakage patterns closely. The Ostomy Skin Tool is current state-of-the-art for evaluation of peristomal skin, but it relies on patients visiting their healthcare professional regularly. To enable close monitoring of peristomal skin over time, an automated strategy not relying on scheduled consultations is required. Several medical fields have implemented automated image analysis based on artificial intelligence, and these deep learning algorithms have become increasingly recognized as a valuable tool in healthcare. Therefore, the main objective of this study was to develop deep learning algorithms which could provide automated, consistent, and objective assessments of changes in peristomal skin discoloration and leakage patterns. A total of 614 peristomal skin images were used for development of the discoloration model, which predicted the area of the discolored peristomal skin with an accuracy of 95% alongside precision and recall scores of 79.6 and 75.0%, respectively. The algorithm predicting leakage patterns was developed based on 954 product images, and leakage area was determined with 98.8% accuracy, 75.0% precision, and 71.5% recall. Combined, these data for the first time demonstrate implementation of artificial intelligence for automated assessment of changes in peristomal skin discoloration and leakage patterns.
对于造口患者而言，造口周围皮肤并发症（PSC）的发生是最常见的术后挑战。PSC的一个视觉迹象是造口周围皮肤变色（发红），这通常是由于造口排泄物在底盘下渗漏所致。如果不加以处理，轻度皮肤状况可能会发展成严重疾病；因此，密切监测变色和渗漏模式非常重要。造口皮肤工具是目前评估造口周围皮肤的先进技术，但它依赖于患者定期拜访医护人员。为了能够长期密切监测造口周围皮肤，需要一种不依赖定期会诊的自动化策略。几个医学领域已经实施了基于人工智能的自动图像分析，这些深度学习算法在医疗保健中越来越被视为一种有价值的工具。因此，本研究的主要目的是开发深度学习算法，以提供对造口周围皮肤变色和渗漏模式变化的自动化、一致且客观的评估。总共614张造口周围皮肤图像用于变色模型的开发，该模型预测造口周围变色皮肤的面积，准确率为95%，精确率和召回率分别为79.6%和75.0%。基于954张产品图像开发了预测渗漏模式的算法，确定渗漏面积的准确率为98.8%，精确率为75.0%，召回率为71.5%。综合起来，这些数据首次证明了人工智能在自动评估造口周围皮肤变色和渗漏模式变化方面的应用。
