Artificial intelligence-enhanced multispectral imaging for burn wound assessment: Insights from a multi-centre UK...

Accurate burn wound assessment is essential for effective treatment, yet it remains heavily dependent on clinical judgment, which is highly subjective. While various optical-based instruments have been developed to address this issue, their clinical effectiveness has been limited due to high cost, penetration depth, lack of portability and validation primarily for use on days 2-5 post injury. The integration of artificial intelligence (AI) with multispectral imaging (MSI) represents a potential advancement in enhancing the accuracy and consistency of wound assessment. MSI captures complex physiological data beyond the visible spectrum, while AI may facilitate its interpretation by generating objective, consistent, and reproducible outputs. However, this relatively new approach still lacks robust clinical validation due to its novelty and the limited body of supporting evidence. This product evaluation examines the first application of AI-enhanced MSI for burn wound assessment in a multi-centre UK setting.

We conducted a multicentre prospective cohort evaluation of the Spectral Deepview device at the Newcastle upon Tyne and Manchester Adult Burn Centres. Inclusion criteria were patients 18 years old and older with superficial to full thickness burns that had not undergone surgery. MSI and clinical assessment were performed on admission, and the patients followed up for 21 days. The primary outcome was the reliability and reproducibility of healing prediction, whilst the secondary outcome was the instrument's feasibility. The AI's prediction was compared to the clinical healing assessment at 21 days, which served as the ground truth. ImageJ was used to calculate the number of pixels in the AI-generated wound maps that correctly corresponded to the ground truth classification of healed and unhealed areas, enabling a quantitative comparison. Statistical analysis was performed using the statistical package R (version 4.4.1).

The clinical evaluation included 40 patients and 70 burn images, generating approximately 13 million data points. The mean age of the patients was 51, with an average Total Body Surface Area (TBSA) of 4.06 %. Most burns were scalds (65.7 %). The AI-enhanced MSI system demonstrated a sensitivity of 80.7 % (95 % CI: 51.8 %-100 %) and a specificity of 95.5 % (95 % CI: 93.3 %-97.8 %). The overall accuracy of the system was 95.3 % (95 % CI: 93.2 %-97.6 %). The mean time from scan to result was five minutes and twelve seconds. The device was portable and effectively utilised in clinics, operating theatres, and emergency departments.

Our clinical evaluation demonstrates that the AI-enhanced MSI system offers high accuracy compared to clinical healing outcomes as the ground truth. It combines diagnostic precision, operational efficiency, and portability. However, the evidence base remains limited and requires further development. Future research is needed to assess the device's impact on clinical decision-making, workflow integration, and patient outcomes.