Anthony Joseph Durkin

Spatial frequency domain imaging (SFDI) has demonstrated the ability to provide early, quantitative assessment of burn wound severity. Previous studies using a research-grade SFDI platform showed that high-dimensional datasets incorporating multiple spatial frequencies and wavelengths can predict healing outcomes in controlled porcine models of graded burns. To assess the impact of reduced measurement dimensionality on diagnostic performance, we compared classifications derived from a research-grade SFDI system (Reflect RS) with those obtained using a simplified commercial SFDI platform (Clarifi RS). Both systems were used to image graded burns 24 hours after injury. Pixel-level classifiers were trained using regions defined by 28-day healing outcomes, and models based on the full Reflect dataset were compared with classifiers generated from reduced Reflect feature sets and datasets designed to mimic Clarifi acquisition features. Performance was evaluated using leave-one-subject-out validation. The full Reflect dataset achieved the highest classification performance, with mean F1 scores approaching 0.88. Although reducing the number of measured features decreased classification accuracy, simplified models and Clarifi-based datasets maintained F1 scores greater than 0.8 for binary classification. These findings indicate that dimensionality reduction produces a measurable but manageable loss in performance and support the development of clinically practical, application-specific SFDI systems for burn assessment.

SignificancePrompt care is essential for burn wound recovery. Spatial frequency domain imaging (SFDI) has previously shown promise in predicting healing outcomes across burn severity grades. This study builds on that by demonstrating calibrated reflectance images (Rd) from SFDI can estimate thermally induced collagen denaturation depth (CDD), a histology-based metric of burn severity linked to healing outcomes. These findings may simplify future hardware design by clarifying contrast sources in SFDI.AimTo develop predictive models for: 1) Day-1 postburn CDD using SFDI Rd and 2) Day-28 healing outcomes using day-1 CDD.ApproachUsing a previously reported graded-severity porcine burn model (n=4) with eight contact durations (5 to 40 s), we collected SFDI and color images on days 0, 1, 3, 7, 14, 21, and 28. Histological analysis using Picrosirius red staining and polarization microscopy was performed on days 1, 7, 14, 21, and 28 to assess CDD. Healing outcomes were clinically evaluated on day 28. For analysis, a two-step regression framework was applied:Step 1: Multiple linear regression, where day-1 SFDI Rd is used to predict same-day CDD.Step 2: Logistic regression, where day-1 CDD is used to predict day-28 healing outcome.Together, these steps established a regression framework to predict day-1 CDD and day-28 healing outcomes using day-1 SFDI Rd.ResultsThe linear model using Rd across eight wavelengths (471-851 nm) and five spatial frequencies (0 to 0.2 mm−1) predicted CDD with a root mean square error of 105 μm and adjusted R2 of 0.71. The logistic model predicted healing outcomes with an ROC-AUC of 0.88, supporting CDD as an early indicator for burn severity assessed by healing potential.ConclusionsThis two-step framework enables early prediction (as early as day 1) of burn severity and healing using SFDI Rd.

SignificanceShort-wave infrared (SWIR) light has recently gained popularity in tissue spectroscopy and imaging applications for a wide range of biomedical applications, primarily due to advancements in hardware (e.g., cameras).AimWe aim to provide a detailed review of SWIR-based biomedical optics studies from the past decade, during which there has been a proliferation of SWIR-based tissue-optics studies.ApproachWe report literature occurring after the publication of our previous (2015) review of this space, describing next-generation SWIR-based techniques that hold significant promise for enhanced in vivo tissue characterization and clinical translation.ResultsInterest from the biophotonics field in SWIR technology is typically attributable to (1) the capability of SWIR light to provide greater sensitivity to chromophores such as water and lipids, with absorption peaks not as prominent in the visible-to-near-infrared (VIS-NIR) spectral region, and (2) the potential for SWIR photons to penetrate through superficial tissue layers due to lower scattering in the SWIR than in the VIS-NIR, as well as substantially reduced attenuation from hemoglobin and melanin.ConclusionThis review of emerging SWIR biophotonic technologies illustrates the rapid growth in the use of SWIR light for in vivo tissue spectroscopy and imaging.

Periocular inflammatory and infiltrative conditions are of particular importance given the proximity to the eye, thereby raising the risk for dry eyes, exposure keratopathy, eyelid malposition, blurry vision, eye pain, vision loss, double vision, and rarely, extension to the central nervous system. Diseases that affect the region include thyroid eye disease, rosacea, and eczema, as well as basal cell carcinoma, squamous cell carcinoma, melanoma, and vascular malformations. Clinical diagnosis and management of these diseases are oftentimes dependent on invasive, time-intensive, and costly procedures and testing, as well as immunotherapy, surgical resection, and periocular reconstruction. Non-invasive objective approaches to measuring local inflammation and infiltration are therefore needed to better quantify disease activity, progression, and treatment response.

The brain is a nutrient-demanding organ whose hemodynamics are well regulated to ensure proper functional activities. Blood flow in the brain is tightly controlled to maintain homeostasis (i.e., vascular reactivity) and to deliver sufficient glucose, oxygen, and other nutrients to support neuronal activities (i.e., neurovascular coupling). The neurovascular unit plays a crucial role in controlling essential vascular activities, such as vasodilation and constriction to regulate cerebral hemodynamics.^1,2 Studying cerebral hemodynamics at resting states or in relationship to perturbations may provide diagnostic or prognostic information on the onset or progression of brain conditions, such as stroke and Alzheimer’s disease (AD).^1,3,4

Accurate, prompt classification of burn wound severity is crucial for patient care. We use non-contact multispectral spatial frequency domain imaging technology coupled with machine learning algorithms to categorize millimeter-wave radiation burns in a preclinical model.