Quantitative Biophotonics for Tissue Characterization and Function

Eunice Kennedy Shriver National Institute Of Child Health & Human Development

Investigators

Linked publications & trials

Abstract

Placental oxygenation: Poor placental development and placental defects can lead to adverse pregnancy outcomes such as pre-eclampsia, fetal growth restriction, and stillbirth. We have developed two sensors, which use a near-infrared spectroscopy (NIRS) technique to measure placental oxygen saturation transabdominally (Nguyen et al., 2024). The first one, an NIRS sensor, is a wearable device consisting of multiple NIRS channels. The second one, a Multimodal sensor, which is an upgraded version of the NIRS sensor, is a wireless and wearable device, integrating a motion sensor and multiple NIRS channels. A pilot clinical study was conducted to assess the feasibility of the two sensors in measuring transabdominal placental oxygenation in 36 pregnant women (n = 12 for the NIRS sensor and n = 24 for the Multimodal sensor) at the Center for Advanced Obstetrical Care and Research of the Perinatology Research Branch, located at the Detroit Medical Center (DMC, Detroit, Michigan, USA) under protocol #090717MP4E. Among these subjects, 4 participants had an uncomplicated pregnancy, and 32 patients had either maternal pre-existing conditions/complications, neonatal complications, and/or placental pathologic abnormalities. The study results indicate that the patients with maternal complicated conditions (69.5 ± 5.4%), placental pathologic abnormalities (69.4 ± 4.9%), and neonatal complications (68.0 ± 5.1%) had statistically significantly lower transabdominal placental oxygenation levels than those with an uncomplicated pregnancy (76.0 ± 4.4%) (F (3,104) = 6.6, p = 0.0004). Additionally, this study shows the capability of the Multimodal sensor in detecting the maternal heart rate and respiratory rate, fetal movements, and uterine contractions. These findings demonstrate the feasibility of the two sensors in the real-time continuous monitoring of transabdominal placental oxygenation to detect at-risk pregnancies and guide timely clinical interventions, thereby improving pregnancy outcomes. On this topic, we have conducted a review of antenatal fetal monitoring techniques (Park et al., 2024) that can help patients and healthcare providers recognize and intervene in a timely manner to prevent conditions that threaten fetal well-being. Various fetal well-being assessment methods have been explored. The most used techniques include the non-stress test, contraction stress test, and biophysical profile that encompass both fetal movement and fetal heart rate. This review delves into technologies used for antenatal diagnostic methods, ranging from traditional methods to contemporary advancements. The presented categories serve as a guide, recommending techniques tailored to specific pregnancy stages and observational purposes. DFFOCT: In order to evaluate data collected in-vivo measurement, we have been assessing intrinsic intracellular activities of cells in different oxygen levels using the DFFOCT (Dynamic Full-field Optical Coherence Tomography) system. In our experimentation, we employ HeLa and HEK cells, which are similar to placental cells and the results of the study can be widely applied. We successfully constructed a customized incubator for cell cultivation in conjunction with the DFFOCT system. This incubator addresses past limitations associated with maintaining optimal 'temperature', 'gas concentration control', and the 'continuous supply of nutrients' during extended cell observations using the DFFOCT system. Notably, the incubator's systematic control over the supplied O2 concentration enables a more methodical exploration of the impact of placental oxygen levels on fetal development. We have found that cell viability is highly subjective to temperature variations (22C and 36C) in our preliminary 24-hour longitudinal studies. During hypothermia, cells showed blebbing and swelling and decomposed into small fragments, resulting in cell death. Conversely, at body temperature, cell proliferation and intracellular dynamic movements were seen even after 24 hours. Intracellular activities were shown in uniform movement with low mean frequency and high magnitude by DFFOCT. We have filed a provisional patent application entitled “Dynamic Assessment of Cellular Metabolism Through Optical Imaging and Artificial Intelligence Techniques” on 02/22/2024 and a Patent Cooperation Treaty (PCT) application on 02/22/2025 regarding this technology. Multimodal biosensor: Utilizing the NIRS technique, we have developed a multimodal biosensor, which can measure chest tissue oxygenation levels as well as cardiac and respiratory functions. The biosensor is currently being used for two different studies. The first study is to monitor participants with different breathing patterns and the second study is to monitor pediatric patients with obstructive sleep apnea. For the first study, we have developed a two-stream convolutional neural network (TCNN) for breathing pattern classification (Park et al., 2024). The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models. For the second study, we are currently collaborating with Dr. Tromberg (NICHD) and Dr. Buckley (NIMH) to conduct a clinical study on pediatric participants with and without obstructive sleep apnea (NICHD protocol #000224). Additionally, we have conducted a systematic review and meta-analysis to comprehensively synthesize the existing literature on cognitive, psychological, and developmental outcomes of obstructive sleep apnea in the pediatric population. Preliminary findings suggest a potential link between pediatric obstructive sleep apnea and adverse cognitive (overall effect size = -0.73 [-1.11, -0.36] for verbal WISC), psychological (overall effect size = 2.26 [- 0.29, 4.81] for total CBCL), and developmental (overall effect size = 1.90 [0.13, 3.67] for global BRIEF) outcomes. These results may help inform recommendations for early screening and intervention. Total Body Photography for Skin Cancer Research: Total Body Photography (TBP) is gaining popularity as a powerful tool for early melanoma detection. TBP allows monitoring of temporal changes in skin lesions, efficient screening of numerous lesions, and provides essential anatomical locations for dermatoscopic images. Despite its potential, several challenges are hindering its widespread application in skin cancer research. We propose a novel framework combining geometric and texture information to localize skin lesion correspondence from a source scan to a target scan in TBP. We evaluated the framework quantitatively on both a public and a private dataset, for which our success rates (at 10 mm criterion) are comparable to the only reported longitudinal study (Huang et al. 2023). We formulated the image capture problem as limited to the depth of field of a camera and designed a novel algorithm to improve overall image quality. The proposed EM and k-view algorithms improve the relative cost of the baseline single-view methods by at least 24% and 28% respectively, corresponding to increasing the in-focus surface area in total body photography by roughly 1550 cm2 and 1780 cm2 (Huang et al. 2024, submitted to WACV 2025). Finally, in collaboration with Dr. Mehran Armand and Dr. Jun Kang at Johns Hopkins University, we have developed a novel shape-aware TBP system for high-resolution, quality-surface-coverage-maximized, and multi-spectral images. A clinical protocol has been approved by the JHU IRB and allows the study to evaluate the clinical effectiveness of the proposed system, with 12 patients collected.