A systematic review of advances in the use of spectral imaging in burn depth assessment

Abstract

Background

Accurate burn depth assessment is critical for determining appropriate treatment and optimizing patient outcomes. Conventional methods, such as clinical assessment and laser Doppler imaging, have limitations in terms of accuracy and timeliness. Spectral imaging, including multispectral imaging and hyperspectral imaging, has emerged as a promising non-invasive modality to improve burn depth evaluation. This systematic review aims to evaluate the advances in spectral imaging technologies for burn depth assessment, with a focus on diagnostic accuracy, the role of machine learning integration, and the quality of current evidence.

Methods

A comprehensive literature search was conducted in March 2024 using PubMed, Scottish Network, EMBASE, and Cochrane Library databases. Studies that evaluated spectral imaging for burn depth assessment and compared it to standard methods such as laser Doppler imaging, clinical assessment, or histological analysis were included. The quality of the included studies was assessed using the QUADAS tool.

Results

Seven studies from 1988 to 2023 met the inclusion criteria, evaluating a total of 167 patients with 269 burn sites. The pooled analysis revealed a combined sensitivity of 86 % (95 % CI [0.80; 0.90]) and specificity of 84 % (95 % CI [0.70; 0.93]. However, there was a large range of sensitivity identified from 61 % to 97.2 % and specificity from 45 % to 100 %. Notably, the integration of machine learning, particularly convolutional neural networks and support vector machines, improved classification accuracy, with some models achieving over 95 % sensitivity and specificity. Despite these promising results, significant variability in methodologies and a lack of standardized ground truthing were identified.

Conclusion

Spectral imaging, especially when combined with machine learning, shows strong potential as an effective tool for burn depth assessment, offering high diagnostic accuracy and reproducibility. Further research is needed to standardize protocols and validate these technologies across diverse patient populations, paving the way for clinical adoption and improved patient care.