AI technology for remote clinical assessment and monitoring. (2nd December 2020)

• Record Type: Journal Article
• Authors: Zoppo, Gianluca; Marrone, Francesco; Pittarello, Monica; Farina, Marco; Uberti, Alberto; Demarchi, Danilo; Secco, Jacopo; Corinto, Fernando; Ricci, Elia
• Is Part Of: Journal of wound care. Volume 29:Number 12(2020)
• Page Start: 692
• Page End: 706
• Publication Date: 2020-12-02
• Subjects: automatic wound classification -- medical device -- telemedicine -- three-dimensional wound measurement -- wound bed preparation score -- wound care -- wound healing -- wound measurement techniques -- wounds; Wounds and injuries -- Treatment -- Periodicals; Wound healing -- Periodicals; 617.1
• DOI: 10.12968/jowc.2020.29.12.692
• Languages: English
• ISSNs: 0969-0700

Abstract

Abstract : Objective: 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. Method: 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. Results: 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). Conclusion: 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.