AI technology for remote clinical assessment and monitoring

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.

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

“…In fact, 14 of the excluded studies focused on the classification of body postures or movements (e.g., [19][20][21][22][23][24][25][26]), which represents a very important subject in the monitoring of PU. Four of the excluded studies (e.g., [27][28][29][30]) addressed wound image analysis to characterize or classify PU. Despite describing interesting works, these studies did not propose any type of actions or consequences related to the postures and movements identified, or the stage (i.e., pressure ulcers classification according to the level of tissue damage), to improve the prevention and treatment of PU.…”

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“…In technical terms, a CNN is a type of neural network that employs ML algorithms to process various types of data and draw conclusions and has been shown to achieve high accuracy in recognizing objects [8]. A CNN consists of multiple convolutional layers, the main building blocks to which filters are applied, each responsible for combining model inputs (e.g., images and sensor data) to create a map from which to extract relevant features or components/relationships of the model inputs.…”

“…This study uses deep-ML approaches to distinguish connections or predictive measures between these two data types. This combination has the potential to provide objective data through continuous remote monitoring of the wound, similar to the protocol by Zoppo and colleagues using remote monitoring through a telehealth platform [8] to alert patients about wound complications and to communicate critical information to clinicians. The long-term objective is to improve subjective wound assessment by providing a more "precise" parameter for healing status.…”

What COVID‐19 taught us: New opportunities and pathways from telemedicine and novel antiseptics in wound healing

“…In order to evaluate the precision and the usefulness of the device in the remote wound evaluation, a clinical trial has been conducted among 150 patients. The results showed that AI would really help the clinician to conduct an effective remote wound assessment, reaching 97% of accuracy in the WBP classification and tissue segmentation analysis compared with that performed by clinicians 67 …”

“…The results showed that AI would really help the clinician to conduct an effective remote wound assessment, reaching 97% of accuracy in the WBP classification and tissue segmentation analysis compared with that performed by clinicians. 67 …”