Department of Electronic Engineering and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy.
Department of Surgery 2, Clinica San Luca, Strada della Vetta 3, 10020, Torino, Italy.
J Wound Care. 2020 Dec 2;29(12):692-706. doi: 10.12968/jowc.2020.29.12.692.
To report the clinical validation of an innovative, artificial intelligence (AI)-powered, portable and non-invasive medical device called Wound Viewer. The AI medical device uses dedicated sensors and AI algorithms to remotely collect objective and precise clinical data, including three-dimensional (3D) wound measurements, tissue composition and wound classification through the internationally recognised Wound Bed Preparation (WBP) protocol; this data can then be shared through a secure General Data Protection Regulation (GDPR)- and Health Insurance Portability and Accountability Act (HIPAA)-compliant data transfer system. This trial aims to test the reliability and precision of the AI medical device and its ability to aid health professionals in clinically evaluating wounds as efficiently remotely as at the bedside.
This non-randomised comparative clinical trial was conducted in the Clinica San Luca (Turin, Italy). Patients were divided into three groups: (i) patients with venous and arterial ulcers in the lower limbs; (ii) patients with diabetes and presenting with diabetic foot syndrome; and (iii) patients with pressure ulcers. Each wound was evaluated for area, depth, volume and WBP wound classification. Each patient was examined once and the results, analysed by the AI medical device, were compared against data obtained following visual evaluation by the physician and research team. The area and depth were compared with a Kruskal-Wallis one-way analysis of variations in the obtained distribution (expected p-value>0.1 for both tests). The WBP classification and tissue segmentation were analysed by directly comparing the classification obtained by the AI medical device against that of the testing physician.
A total of 150 patients took part in the trial. The results demonstrated that the AI medical device's AI algorithm could acquire objective clinical parameters in a completely automated manner. The AI medical device reached 97% accuracy against the WBP classification and tissue segmentation analysis compared with that performed in person by the physician. Moreover, data regarding the measurements of the wounds, as analysed through the Kruskal-Wallis technique, showed that the data distribution proved comparable with the other methods of measurement previously clinically validated in the literature (p=0.9).
These findings indicate that remote wound assessment undertaken by physicians is as effective through the AI medical device as bedside examination, and that the device was able to assess wounds and provide a precise WBP wound classification. Furthermore, there was no need for manual data entry, thereby reducing the risk of human error while preserving high-quality clinical diagnostic data.
报告一种创新的人工智能（AI）驱动的、便携式和非侵入性医疗设备 Wound Viewer 的临床验证。该 AI 医疗设备使用专用传感器和 AI 算法远程采集客观、精确的临床数据，包括通过国际公认的伤口床准备（WBP）协议进行的三维（3D）伤口测量、组织成分和伤口分类；然后，通过安全的通用数据保护条例（GDPR）和健康保险流通与责任法案（HIPAA）兼容的数据传输系统共享这些数据。该试验旨在测试 AI 医疗设备的可靠性和精度，以及其帮助医疗专业人员远程和床边临床评估伤口的能力。
这是一项非随机对照临床试验，在意大利都灵的 Clinica San Luca 进行。患者分为三组：（i）下肢静脉和动脉溃疡患者；（ii）患有糖尿病并出现糖尿病足综合征的患者；（iii）压疮患者。对每个伤口进行面积、深度、体积和 WBP 伤口分类评估。每位患者接受一次检查，由 AI 医疗设备分析结果，并与医生和研究团队的视觉评估获得的数据进行比较。使用 Kruskal-Wallis 单向方差分析比较获得的分布中的面积和深度（两个测试的预期 p 值均大于 0.1）。通过直接比较 AI 医疗设备获得的分类与测试医生的分类，分析 WBP 分类和组织分割。
共有 150 名患者参加了试验。结果表明，AI 医疗设备的 AI 算法可以完全自动获取客观的临床参数。AI 医疗设备在 WBP 分类和组织分割分析方面的准确率达到 97%，与医生的人工分析相比。此外，通过 Kruskal-Wallis 技术分析的伤口测量数据表明，数据分布与文献中先前临床验证的其他测量方法相当（p=0.9）。
这些发现表明，医生通过 AI 医疗设备进行远程伤口评估与床边检查同样有效，并且该设备能够评估伤口并提供精确的 WBP 伤口分类。此外，无需手动输入数据，从而降低了人为错误的风险，同时保留了高质量的临床诊断数据。
