Evaluation of hyperspectral imaging as a modern aid in clinical assessment of burn wounds of the upper extremity

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

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.

Strategies for Optimizing Acute Burn Wound Therapy: A Comprehensive Review

Recent advancements in acute burn wound therapy are transforming the management of burn injuries, with a focus on improving healing times, graft integration, and minimizing complications. However, current clinical treatments face significant challenges, including the difficulty of accurately assessing wound depth and tissue viability, which can lead to suboptimal treatment planning. Traditional closure methods often struggle with issues such as delayed wound closure, limited graft survival, inadequate tissue regeneration, and insufficient vascularization. Furthermore, managing infection and minimizing scarring remain persistent obstacles, impacting functional recovery and aesthetic outcomes. Key areas of innovation include advanced imaging techniques that enable more precise assessment of wound depth, size, and tissue viability, allowing for more accurate treatment planning. In addition, new closure strategies are being developed to accelerate wound closure, enhance graft survival, and address challenges such as tissue regeneration, vascularization, and infection prevention. These strategies aim to optimize both functional recovery and aesthetic outcomes, reducing scarring and improving the quality of life for burn patients. While promising, these emerging techniques require further research and clinical validation to refine their effectiveness and expand their accessibility. Together, these innovations represent a significant shift in acute burn care, offering the potential for more personalized, efficient, and effective treatments.

Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review

Hyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerative diseases like Parkinson's and Alzheimer's, which illustrates its effectiveness in early diagnosis, early caries detection in periodontal disease, and dermatology by detecting skin cancer. Regardless of these advances, the challenges exist within every aspect that limits its broader clinical adoption. It has various constraints including difficulties with technology related to the complexity of the HSI system and needing specialist training, which may act as a drawback to its clinical settings. This article pertains to potential challenges expressed in medical applications and probable solutions to overcome these constraints. Successful companies that perform advanced solutions with HSI in terms of medical applications are being emphasized in this study to signal the high level of interest in medical diagnosis for systems to incorporate machine learning ML and artificial intelligence AI to foster precision diagnosis and standardized clinical workflow. This advancement signifies progressive possibilities of HSI in real-time clinical assessments. In conclusion despite HSI has been presented as a significant advanced medical imaging tool, addressing its limitations and probable solutions is for broader clinical adoption.

Non-invasive medical imaging technology for the diagnosis of burn depth

Currently, the clinical diagnosis of burn depth primarily relies on physicians' judgements based on patients' symptoms and physical signs, particularly the morphological characteristics of the wound. This method highly depends on individual doctors' clinical experience, proving challenging for less experienced or primary care physicians, with results often varying from one practitioner to another. Therefore, scholars have been exploring an objective and quantitative auxiliary examination technique to enhance the accuracy and consistency of burn depth diagnosis. Non-invasive medical imaging technology, with its significant advantages in examining tissue surface morphology, blood flow in deep and changes in structure and composition, has become a hot topic in burn diagnostic technology research in recent years. This paper reviews various non-invasive medical imaging technologies that have shown potential in burn depth diagnosis. These technologies are summarized and synthesized in terms of imaging principles, current research status, advantages and limitations, aiming to provide a reference for clinical application or research for burn specialists.

Burn characterization using object-oriented hyperspectral image classification

This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% ± 0.60%), precision (97.30% ± 2.46%), sensitivity (97.23% ± 3.02%) and specificity (98.02% ± 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.