Classification of burn injury using Raman spectroscopy and optical coherence tomography: An ex-vivo study on porcine skin.

Accurate depth assessment of burn wounds is a critical task to provide the right treatment and care. Currently, laser Doppler imaging is able to provide better accuracy compared to the standard clinical evaluation. However, its clinical applicability is limited by factors like scanning distance, time, and cost. Precise diagnosis of burns requires adequate structural and functional details. In this work, we evaluated the combined potential of two non-invasive optical modalities, optical coherence tomography (OCT) and Raman spectroscopy (RS), to identify degrees of burn wounds (superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT)). OCT provides morphological information, whereas, RS provides biochemical aspects. OCT images and Raman spectra were obtained from burns created on ex-vivo porcine skin. Algorithms were developed to segment skin region and extract textural features from OCT images, and derive spectral wave features from RS. These computed features were fed into machine learning classifiers for categorization of burns. Histological results obtained from trichrome staining were used as ground-truth. The combined performance of RS-OCT reported an overall average accuracy of 85% and ROC-AUC=0.94, in distinguishing the burn wounds. The significant performance on ex vivo skin motivates to assess the feasibility of combined RS-OCT in in vivo models.

A Decision-Support Tool for Renal Mass Classification.

We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists.

Dual-imaging system for burn depth diagnosis.

Currently, determination of burn depth and healing outcomes has been limited to subjective assessment or a single modality, e.g., laser Doppler imaging. Such measures have proven less than ideal. Recent developments in other non-contact technologies such as optical coherence tomography (OCT) and pulse speckle imaging (PSI) offer the promise that an intelligent fusion of information across these modalities can improve visualization of burn regions thereby increasing the sensitivity of the diagnosis. In this work, we combined OCT and PSI images to classify the degree of burn (superficial, partial-thickness and full-thickness burns). Algorithms were developed to integrate and visualize skin structure (with and without burns) from the two modalities. We have completed the proposed initiatives by employing a porcine burn model and compiled results that attest to the utility of our proposed dual-modal fusion approach. Computer-derived data indicating the varying burn depths were validated through immunohistochemical analysis performed on burned skin tissue. The combined performance of OCT and PSI modalities provided an overall ROC-AUC=0.87 (significant at p<0.001) in classifying different burn types measured after 1-h of creating the burn wounds. Porcine model studies to assess feasibility of this dual-imaging system for wound tracking are underway.

Feasibility of osteoporosis screening by dual-energy radiographic absorptiometry of the phalanx.

A new dual-energy radiographic absorptiometry-based technique is presented as a cost-effective method for mass osteoporosis screening. Designed for use in a dental health care setting, we propose a method and device for bone mineral density (BMD) assessment using the middle phalanx of the 3rd digit as our anatomical measurement site. Our 2-staged project includes the development of the prototype to carry out the measurement and the execution of a small pilot study to determine the efficacy of the method and device. Fifty subjects from the general adult population (age range: 25-82 yr), wherein 10 normal subjects (5 females and 5 males) and 40 target group subjects (30 females and 10 males) who were at risk for osteoporosis (as assessed qualitatively through questionnaire responses) were evaluated with our method. The BMD values obtained from the normal and target groups were significantly different (p<0.0001). Phantom measurements to determine the bias and coefficient of variation of the technique yielded values of 1.9% and 7%, respectively. The proposed technique could provide a relatively inexpensive and widely available means for mass osteoporosis screening. Further validation of this method, to include comparison to a gold standard, such as dual-energy X-ray absorptiometry, is warranted.