搜索结果：Journal of biomedical optics

Photoacoustic imaging (PAI) is a noninvasive functional imaging modality capable of assessing tissue oxygenation and other tumor-related biological characteristics, and it has been increasingly explored in both preclinical and clinical breast cancer research. However, bibliometric evidence regarding the developmental trajectory and knowledge structure of this field remains limited. To address this gap, this study reviews PAI literature for breast cancer assessment and summarizes the research status and emerging hotspots. Publications on PAI in breast cancer were retrieved from the Science Citation Index Expanded of the Web of Science Core Collection, with a data cutoff of December 31, 2025. After screening and exclusion of non-English publications, review articles, meeting abstracts, and other ineligible document types, eligible research articles were included for bibliometric analysis. VOSviewer and the bibliometrix R package were used to analyze publication trends, geographic and institutional distributions, author collaboration, journal characteristics, and keyword evolution. A total of 214 research articles were included. Annual publication output increased progressively between 2004 and 2025, reaching a peak of 27 articles in 2025. China and the United States were the dominant contributors, jointly accounting for 71% of all publications; China ranked first in publication volume (n=89), whereas the United States showed the highest total citations (n=4,053). Jinan University was the most productive institution (n=17). Journal of Biomedical Optics was the leading journal in publication output (n=18) and also had the highest total citations (n=879). Among authors, Manohar, Srirang was the leading contributor in both publication output (n=20) and total citations (n=1,229). Keyword analysis showed that, beyond core terms, "ultrasound", "molecular imaging", and "photothermal therapy" were major recurring themes, whereas "radiomics", "prediction", and "deep learning" emerged as recent research frontiers. Research on PAI in breast cancer has expanded steadily over the past two decades, with the field evolving from technology-driven system development toward clinically oriented applications. Current hotspots indicate a growing emphasis on artificial intelligence-assisted diagnosis and predictive modeling. Future work should prioritize multicenter validation, cross-platform standardization, and deeper integration of PAI with multimodal imaging and intelligent analytical methods to facilitate broader clinical translation.

Abnormal placental development is a major cause of adverse pregnancy outcomes, but current methods for placenta monitoring are not suitable for bedside use. Continuous-wave near-infrared spectroscopy (CW-NIRS) is an optical technique that takes advantage of the near-infrared light to provide functional measurements such as tissue oxygenation at the bedside. However, the placenta is an organ located beneath several layers of tissue, making robust measurement of placental oxygenation with a CW-NIRS device a complex task. We propose a framework based on light propagation simulations to evaluate the sensitivity of CW-NIRS devices for placenta detection, along with tools to support NIRS instrument development for engineers. The maternal abdomen was modeled as a four-layer structure (i.e., skin, adipose tissue, muscle, and placenta). We used a numerical solution of the diffusion equation using a finite-element method to assess the sensitivity to measure placental function under various conditions (tissue layer thickness, skin tone, tissue oxygen saturation). We used a calibration procedure to evaluate the probability of acquiring a sufficient irradiation with a CW-NIRS device. We collected ultrasound abdomen images from 142 healthy pregnant participants that we segmented and digitized to demonstrate our approach. With a Mini-CYRIL CW-NIRS device, we showed that placenta monitoring is not possible when using short integration time with a subject having a deep placenta ( ≥ 20    mm ) and dark skin tones. With an integration time of 10 s and a temporal binning of 10 points, simulations indicated that subjects with very fair skin tone have a placenta-scanning probability of 12% at a placenta depth of 20 mm and 39% at a depth of 10 mm, using a 50 mm source-detector separation. Thick skin and dark skin tones act as a filter on the NIRS signal, blocking backscattered light and leading to greater absorption in deeper tissues. The spatially resolved spectroscopy method can be used to monitor placental oxygenation with a placenta close to the surface and an oxygen saturation in the muscle layer lower than that of the placenta. The simulation of a realistic cohort of 142 maternal abdomens aimed to identify the optimal acquisition conditions for CW-NIRS devices to be used in placental monitoring. We proposed a framework to evaluate and optimize CW-NIRS sensitivity for placenta detection. Further work is needed to improve the reliability of placental tissue oxygenation.

Fluorescence-guided surgery (FGS) utilizes molecular contrast agents to highlight critical structures or pathological tissues in real time. The premise of FGS is to enable precise surgical decision-making through accurate visualization and quantitative assessment of fluorophore distribution. However, strong effects of diffusion and absorption of fluorescent light in tissue confound fluorescence images, preventing accurate quantitative assessment of the concentration and distribution of fluorescent markers. These optical artifacts may lead to misinterpretation of tissue boundaries and compromised surgical precision, thereby diminishing the capabilities of FGS. Resolving topological depth maps of fluorophore distribution at the millimeter scale is an important first step in performing quantitative sub-surface fluorescence imaging. In this study, we present a spatiotemporal deep learning architecture that utilizes picosecond single-photon avalanche diode (SPAD) sensor images to rapidly recover the depth topology of a fluorophore distribution embedded in diffuse media. The network is designed to work with wide-field, epi-illumination geometry and millimeter spatial resolution. A ConvLSTM-UNet deep learning network was developed for picosecond time-resolved image analysis. This network was trained on 5000 spatiotemporal maps simulated by the optical Monte Carlo method and convolved with the instrument response function (IRF) of the imaging system. The experimental setup utilized a SwissSPAD2 sensor synchronized with a 635 nm picosecond laser diode. Using only 10 selected temporal gates as input, the network could recover depth maps. Reconstruction accuracy was evaluated using mean error metrics across various depths and background concentrations of a fluorophore with a simulated decay time of 100 ps. A total of 75 different test fluorescence video data were evaluated. This set encompassed 15 unique inclusion shapes at five different depths. The network successfully reconstructed fluorescence topography up to 15 mm with a mean absolute error of less than 0.6 mm and mean depth variances below 0.5 mm. The inference time was ∼ 30    ms . Integrating temporal and spatial deep learning networks enabled depth mapping from time-resolved fluorescence data. Utilizing real IRF proved the applicability of SPAD sensors for sub-surface fluorescence mapping.

White blood cells (WBC) are hematopoietic cells of the immune system that protect the body by recognizing and eliminating infectious agents. Abnormalities in WBC production, maturation, or function can lead to disease and associated morphologic changes that, when systematically characterized, support diagnostic classification and clinical decision-making. We aim to investigate polarized hyperspectral imaging (PHSI) and polarized light imaging (PLI) microscopy for the visualization of WBCs. We developed a dual-modality microscopic imaging system that performs both polarized hyperspectral imaging and polarized light imaging. In the dual imaging setup, we used a snapscan hyperspectral camera and an RGB camera to acquire images separately and further calculate four Stokes parameters (S0, S1, S2, and S3) as well as three Stokes vector-derived parameters, namely, the degree of polarization, degree of linear polarization, and degree of circular polarization. Synthetic RGB images of Stokes vectors and Stokes vector-derived parameters were generated for the visualization of cellular components with PHSI images. The spectral signatures of representative WBCs, e.g., granulocytes and lymphocytes, were extracted for qualitative comparison. The preliminary results demonstrate that Stokes vector parameters can enhance the visualization of granules in granulocytes, the visualization of surface structures of lymphocytes, and the morphologic visualization of the monocyte nucleus. Furthermore, the results also reveal that the measured spectra of Stokes vector parameters could enhance the differentiation of WBCs in the spectral dimension, represented by the qualitative comparison between granulocytes and lymphocytes. Utilizing the spatial and spectral information from the Stokes vector data, our customized polarized hyperspectral microscopic imaging system enhances the visualization of WBCs and may provide a tool for the diagnosis of disorders related to white blood cells.

Stomach (gastric) cancer survival depends significantly on the stage in which it is detected, and surveillance with white light endoscopy exhibits poor contrast between gastric cancer and healthy tissue, especially at early stages. Early gastric cancer can exhibit changes in epithelial microstructure, including loss of regular gastric pit structure and collagen alterations which increase tissue stiffness. To improve contrast between early cancer and normal tissue, we investigate the use of optical coherence tomography (OCT) and elastography (OCE) to visualize changes in tissue structure and stiffness consistent with gastric cancer. Images of eight samples of ex vivo human stomach tissue from three patients were collected with a benchtop OCT system. OCT was performed for qualitative visualization of tissue structure. OCE was then performed on 17 regions of interest using a simplified optical palpation method to extract relative stiffness measurements. A transparent silicone reference layer was placed on the tissue, and axial compression was applied. The resulting deformation (strain) of the reference layer was measured, and the corresponding stress applied to the sample surface was extracted from the characteristic stress-strain curve of the reference material. Spatially resolved stress measurements were mapped and overlaid on en face OCT images. Tissue classification was confirmed by pathology. OCT image volumes showed more distinct gastric pit and tissue layer structure, as well as less optical attenuation, in normal tissue compared to gastric metaplasia and focal signet ring cell carcinoma (SRCC). Exemplary OCE-derived stress maps showed a trend of increasing measured stress with progression of precancer (metaplasia and dysplasia) and SRCC, suggesting increased tissue stiffness. This proof-of-concept study provides evidence that OCT and OCE may be capable of visualizing differences in tissue structure and stiffness between normal, metaplastic, dysplastic, and early cancerous gastric tissue, potentially providing the basis for improved screening tools with higher sensitivity.

Medical examination of human tissue is preferably performed by imaging the tissue surface. Optical imaging techniques are limited by low penetration depth due to high tissue scattering, whereas sensing techniques can detect changes deeper inside the tissue. Near-infrared sensing methods such as oximetry and fNIRS are already used clinically but have not yet been applied in endoscopy. We investigate the existence of iso-pathlength (IPL) points in endoscopic geometry, with the goal of extending the concept of IPL points from cylindrical and half-infinite geometries into hollow cylindrical tissue relevant to endoscopy. In addition, we demonstrate the ability to extract the absorption properties of a tissue at this structure by the IPL and demonstrate it by ex vivo experiment. The IPL point is a unique position in the full scattering profile, independent of tissue scattering and dependent only on the tissue absorption and geometry. We studied two directions in cylindrical endoscopic geometry: azimuthal and longitudinal. First, diffusion theory with extrapolated zero-boundary conditions was applied to predict IPL positions. These predictions were then tested using Monte Carlo simulations of photon distribution and validated experimentally using phantoms with cylindrical air holes measured by endoscopy. Finally, using the experimentally identified IPL point and applying the same procedure to a standard phantom, a hemoglobin-agar phantom, and chicken breast tissue, we were able to estimate the absorption coefficient of the chicken tissue. Both azimuthal and longitudinal IPL points were identified. The experimental azimuthal IPL point was found at an angle of 144    deg ± 3    deg , whereas the longitudinal IPL point appeared at a distance of 0.33 ± 0.05    cm from the laser spot center. These findings confirm the theoretical and simulation predictions. Moreover, from the ex vivo experiment of a chicken breast, the IPL point enables us to calculate the absorption coefficient and get μ a = 0.94    cm - 1 , within the range of 0.2    cm - 1 ≤ μ a ≤ 2    cm - 1 . The demonstration of IPL points in endoscopic geometry provides a new framework for depth-resolved optical sensing in hollow cylindrical tissues. This approach may enable self-calibrated absorption measurements and open the way for improved diagnostic tools in the digestive system, esophagus, and other hollow organs where conventional endoscopy lacks depth information.

Imaging 3D in vitro kidney models is essential to understand kidney function and pathology. Label-free characterization of such specimens seeks to supplement existing imaging techniques and avoid the need for contrast agents that can disturb the native state of living samples. Conventional label-free optical imaging techniques are compatible with living samples but face challenges such as poor sectioning capability, fragmentary morphology, and lack of chemical-specific information. We aim to develop and demonstrate a correlative label-free imaging platform capable of simultaneously capturing morphological and chemical-specific information from 3D cultured kidney mesangial cells. We combined simultaneous label-free autofluorescence-multiharmonic (SLAM) microscopy and gradient light interference microscopy (GLIM) to extract both chemical-specific and morphological tomography of 3D cultured kidney mesangial cells. In this approach, SLAM provides a nonlinear imaging platform with a single excitation source to simultaneously acquire autofluorescence (FAD and NAD(P)H), second- and third-harmonic signals from the cells. Complementarily, GLIM acquires high-contrast quantitative phase information to quantify structural changes in samples with a thickness of up to 250    μ m . Our correlative imaging results demonstrate the ability to image and quantify both morphology and chemical-specific signals of kidney mesangial cells in 3D. The combination of GLIM and SLAM provides complementary information critical for understanding kidney function, including metabolism and matrix deposition under controlled physiological conditions. The proposed correlative imaging approach establishes a versatile and hassle-free platform for morpho-chemical cellular tomography, offering unique opportunities for studying the structure and function of 3D kidney models in their native state.

Early detection of Alzheimer's diseases, diabetic retinopathy, or macular degeneration with advanced retinal imaging technologies can help improve patient care and treatment outcome. We aim to create a high-resolution hyperspectral imaging (HSI) system for the retina. Retinal vessel diameter and oxygenation rate will be extracted simultaneously from HSI data. Our hyperspectral retinal imaging system consists of a snapshot hyperspectral camera, a high-resolution RGB camera, a beamsplitter, and an imaging endoscope. Multiple pansharpening algorithms, including deep learning methods, were developed to generate high-resolution hyperspectral images that were further used for the measurement of vessel size and oxygenation rate in mice. The hyperspectral retinal imaging system was tested for its spatial resolution and spectral fidelity in retina phantoms. In vivo imaging experiments were performed in mice. The deep learning-based pansharpening algorithm achieved a root mean square error (RMSE) of 2.15 ± 0.64 , a correlation coefficient (CC) of 0.96 ± 0.05 , a spectral angle score of 0.06 ± 0.03 radians, and an error relative global dimensionless synthesis (ERGAS) score of 2.37 ± 1.71 . Oxygen saturation ( sO 2 ) and lumen diameters of blood vessels were measured in the retina. The average lumen diameter of the venules was 45.7 ± 13.6    μ m , whereas the average lumen diameter of the arterioles was 31.5 ± 8.7    μ m . The average arteriole sO 2 was 98%, whereas the average venule sO 2 was 58%. A high-resolution hyperspectral imaging system was developed and validated for retina imaging and measurement of blood vessels and oxygen saturation.

Performing a ratiometric analysis of the fluorescence signals noninvasively measured at two different wavelengths can provide depth estimates of subsurface inner structures in a simple and fast manner, allowing for real-time applications in clinical settings. This can be done using the initially proposed single-excitation-multiple-emission wavelengths approach or by implementing a modified multiple-excitation-single-emission approach; the latter being sometimes preferred due to the larger variation of tissue optical properties at shorter wavelengths. However, previous works validating this method with Monte Carlo (MC) simulations, experiments on tissue-mimicking phantoms, and in vivo measurements on small animal models have reported different degrees of accuracy. We tested the influence of factors not generally accounted for in the analytical model used for data interpretation (e.g., tissue geometry and boundaries, inclusion size and shape, and spectral characteristics of the excitation source). To address these limitations, we developed an improved theoretical framework that explicitly accounts for these factors during data interpretation. Model validation was carried out with MC simulations and with phantom experiments using indocyanine green as the fluorescence contrast agent. The aimed tissue optical properties were those characteristic of the prostate in a wide range of wavelengths (from 550 to 900 nm). The aforementioned factors have a strong influence when changing the original single-excitation-multiple-emission approach to a multiple-excitation-single-emission approach. Though this might make the latter a less preferable method, the low variability of the optical properties in the multiple emission approach (as it happens with prostate tissue) negatively impacts the depth reconstruction process. Regardless of the ratiometric strategy employed, accurate depth estimation requires that the theoretical model closely replicate the experimental conditions. Careful matching of model assumptions to the measurement environment is essential to achieve reliable data interpretation.

Light-field microscopy (LFM) is a scanning-free 3D imaging technique that is useful for observing dynamic biological systems due to its unique capability to capture both spatial and angular information from samples in a single exposure. However, LFM suffers from the spatial-angular information trade-off associated with microlens arrays, and its spatial resolution is usually unsatisfactory for fine-structure imaging. To overcome this bottleneck, we introduce a deep-learning-based image fusion technique that combines LFM images with Fourier LFM (FLFM) images. The high spatial resolution of FLFM is combined with the dense angular acquisition capability of LFM to improve 3D image reconstruction quality. The deep learning network was trained with LFM, FLFM, and epipolar plane image data. The proposed neural network employs specialized feature extraction modules for each modality, with a U-Net backbone for 3D reconstruction, and integrates a hierarchical cascade-based result-level fusion strategy to jointly optimize multimodal features. This approach significantly enhances detail preservation and depth recovery in the final output. Results obtained using a publicly available dataset of synthetic tubulins demonstrate that the proposed method outperforms state-of-the-art techniques. Quantitatively, it achieved a peak signal-to-noise ratio (PSNR) of 38.4729 and a structural similarity index measure (SSIM) of 0.9876, significantly outperforming both traditional algorithms and single-modality deep learning approaches. Furthermore, validation on a mouse brain blood vessels dataset confirms the effectiveness of the method in reconstructing biological structures, achieving a PSNR of 35.0548 and an SSIM of 0.8424. We introduce an approach that combines LFM with FLFM, providing an efficient and reliable solution for practical LFM applications. The deep-learning-based framework demonstrates significant potential to simultaneously accelerate imaging acquisition and enhance 3D reconstruction quality, offering further possibilities for computational microscopy.

Hyperspectral imaging (HSI) is an advanced spectral imaging technique that captures spatial and spectral information across numerous wavelength bands. This capability allows tissue characterization, disease detection and diagnosis, surgical guidance, and digital histopathology, making it an increasingly valuable tool with wide biological and medical applications. We aim to provide readers with (1) an understanding of the principles and technological advancements in HSI, (2) a comprehensive overview of HSI data processing and analysis methods, and (3) an updated survey of biomedical applications, from disease detection, intraoperative imaging, to histopathology. A systematic literature search was conducted using PubMed and Google Scholar with the keyword "hyperspectral imaging." We previously published a comprehensive review paper on medical HSI in 2014, which was widely cited in the field. Therefore, this updated review focused on new technology advancements and emerging applications. Based on their biological and medical relevance, 612 HSI papers were included and analyzed in this review. Recent advances in HSI span both hardware and computational techniques, including improvements in sensor technology, data processing and analysis, short-wave near-infrared imaging, and deep-learning and AI tools. HSI is actively explored for various applications in oncology, neurology, ophthalmology, dermatology, cardiology, gastroenterology, hepatology, wound care, endocrinology, dentistry, infectious disease, plastic and reconstructive surgery, general surgery, intraoperative guidance, histopathology, microbiology, nanopathology, and pharmacology. HSI has become an emerging imaging modality in biomedical research and clinical settings. Continued advancements in hardware miniaturization, computational efficiency, and clinical validation will further solidify the role of next-generation HSI in biomedicine.

Atrial fibrillation is treated with thermal ablation to isolate ectopic signals. Although this is the current standard of care, recurrence occurs in up to 40% of cases. Clinicians have no reliable way to predict treatment durability intraoperatively. Adding the capability of direct optical measurement of the tissue to an ablation catheter could help better guide the treatment. A radiofrequency ablation catheter was developed with polarization-sensitive optical coherence tomography (PSOCT) and near-infrared spectroscopy (NIRS) to demonstrate, for the first time, simultaneous monitoring of thermal lesion formation. We fabricated the multimodal PSOCT-NIRS ablation catheter and validated optical metrics using known targets of tissue-like phantom, deoxygenated blood, and atrial tissue. We then demonstrated recording PSOCT and NIRS data during ex vivo ablation of swine atria. PSOCT-NIRS metrics showed expected values in known targets. Measurements during ablation also exhibited previously reported patterns-OCT scattering increases, PSOCT birefringence decreases, and NIRS lesion optical index increases. Furthermore, simultaneous measurement revealed varying rates of change and magnitudes of response to different powers of thermal ablation. PSOCT-NIRS can measure tissue response to thermal energy delivery, and the optical metrics are complementary. By collecting more information during thermal energy delivery, PSOCT-NIRS metrics could contribute to understanding treatment durability in future investigations.

Fluorescence lifetime imaging (FLIm) offers label-free contrast based on intrinsic tissue properties, making it a promising tool for clinical diagnostics and intraoperative guidance. However, the lack of robust, reproducible standards for system validation limits cross-platform comparability, impedes quality assurance, and hinders clinical translation. We aim to develop and characterize a set of stable solid-state fluorescence lifetime (FLT) standards using dyed epoxy resins, with the goal of enabling reliable calibration, benchmarking, and validation of FLIm systems in both research and clinical environments. A series of solid standards incorporating different dyes were fabricated to span a range of lifetimes from sub-nanosecond to over 3.5 ns. These materials were evaluated for FLT, emission intensity, photostability under UV exposure, and fabrication repeatability. The influence of dye concentration and microstructural uniformity was assessed using a confocal microscope. The standards were also applied to validate a chip-on-tip FLIm micro-camera designed for endoscopic imaging. The dyed epoxy standards demonstrated consistent and reproducible lifetimes, good photostability, and scalable fabrication. Confocal imaging revealed some microstructural heterogeneity, whereas bulk measurements remained robust. The standards enabled effective validation of the FLIm micro-camera, including spatial and temporal resolution assessment, and highlighted platform-dependent biases in lifetime estimation. Dyed epoxy materials show strong potential as practical, scalable tools for FLIm system calibration and quality assurance. These standards may support cross-platform validation and benchmarking of emerging FLIm technologies and could contribute to the development of future regulatory frameworks for clinical adoption.

Laser safety studies of the eye are well documented for visible wavelength and continuous wave lasers. There are fewer experimental results for infrared wavelengths and pulsed lasers. We aim to fill the gap at 1645 nm for single nanosecond pulse duration exposures of rabbit cornea and determine the threshold radiant exposure to generate lesions 50% of the time (estimated dose ED 50 ). Images of the cornea during exposures were acquired using slit lamp microscopy and optical coherence tomography. A histological analysis helped provide dosimetry relationships with morphology and mechanisms of the damage. We measured the energy ED 50 value at 3.86 ± 0.085    mJ utilizing the slit lamp biomicroscopy. Incorporating the experimental spot size diameter, this corresponds to a peak radiant exposure of 102    J / cm 2 . By contrast, the average radiant exposure ED 50 over a 1-mm diameter limiting aperture as per the ANSI Z.136 convention was 0.49    J / cm 2 . Additional analysis via optical coherence tomography (OCT) and histology examined the severity and degree of damage. This experimental approach performed well to characterize damage and identify damage thresholds to inform the laser safety standard community of the accuracy of current exposure limits.

Ureteral injuries represent a major concern during a range of surgical procedures, due to the proximity of the ureter to target surgical structures. Intraoperative identification of the ureter is critical to prevent this accidental damage. We demonstrate the first known in vivo photoacoustic imaging of the ureter in swine following intravenous administration of FDA-approved methylene blue, enabled by a software-hardware integration that has not been previously reported in the literature. Photoacoustic channel data from the ureters of two swine were acquired using a Vevo F2 ultrasound system and an Opotek Phocus Mobile laser. Images were beamformed using a delay-and-sum algorithm. Photoacoustic image quality was evaluated using contrast, signal-to-noise ratio (SNR), and generalized contrast-to-noise ratio (gCNR) metrics, measured 10 to 80&#xa0;min after methylene blue injection. Across the 10- to 80-min imaging window, median contrast (3.46-11.43&#xa0;dB), SNR (2.84-6.99), and gCNR (0.27-0.64) confirmed sustained ureter visibility with methylene blue. Maximum image quality was observed 20- to 30-min after methylene blue injection, with significantly higher contrast, SNR, and gCNR values compared with earlier or later time points ( p < 0.05 ). In vivo results demonstrate that methylene-blue-enhanced photoacoustic imaging can visualize the ureter over a time duration that is consistent with the length of surgical procedures, providing initial feasibility for real-time photoacoustic-guided surgery applications.

Accurate classification of endometrial pathology is clinically challenging due to the heterogeneous and focal nature of precancerous and malignant lesions. Vascular remodeling is closely linked to tumor progression and may serve as a biomarker for malignancy. We aim to characterize a label-free optical-resolution photoacoustic microscopy (OR-PAM) approach for high-resolution imaging and quantitative characterization and separability assessment of endometrial vasculature. A custom-built OR-PAM system was used to image 34 fresh uterus samples with histologically confirmed diagnoses: normal, benign, endometrial intraepithelial neoplasia (EIN), and endometrial cancer (EC). Thirty-one quantitative vascular features were extracted from structural and spectral analyses of the photoacoustic data, and five statistically significant and minimally correlated features were selected for the separability assessment framework. A pairwise cosine similarity matrix based on these features was computed to construct a weighted similarity network, which was embedded into a two-dimensional (2D) space with a force-directed layout. A logistic regression boundary was applied to the 2D embedding to evaluate separability between normal/benign and EC/EIN clusters. A logistic regression classifier was developed from a cosine similarity matrix and cross-validated using a leave-one-out strategy. The cosine-similarity network graph placed 39 of 40 images on the expected side of the separation boundary. The logistic regression classifier yielded an area under the ROC curve (AUC) of 0.943, demonstrating strong discrimination between normal/benign and EC/EIN groups. OR-PAM combined with imaging feature analysis enables robust differentiation of endometrial pathologies and demonstrates potential as a noninvasive optical biopsy tool for endometrial assessment.

Prostate biopsy remains the gold standard for prostate cancer (PCa) diagnosis and treatment planning. However, current techniques suffer from low cancer detection rates, with most biopsy cores sampling benign tissue, leading to undergrading and repeat procedures. Label-free fluorescence lifetime imaging (FLIm) offers a potential solution by enabling real-time discrimination between malignant and benign tissue during biopsy collection, potentially reducing both the number of cores required and the repeat biopsy rates. This pilot study evaluates the feasibility of label-free FLIm for rapid discrimination of malignant from benign prostate tissue in freshly obtained core needle biopsies. Twenty patients undergoing prostate biopsy were enrolled. FLIm measurements were performed immediately after sample collection ( &#x223c; 10 &#x2009;&#x2009; s ) using a custom fiber-optic probe. For each point measurement, FLIm parameters from four spectral bands associated with the emission of distinct endogenous fluorophores including structural proteins and metabolic cofactors (e.g., NADH and FAD) were entered in the analysis. Each FLIm point measurement was labeled based on histological annotation. These data were analyzed to characterize tissue-type differences and to train and evaluate support vector machine (SVM) classifiers for malignancy detection. Separation between benign tissue and Gleason pattern &#x2265; 4 &#x2009;&#x2009; PCa can already be observed using just 2 out of 56 FLIm-derived parameters. The SVM classifier, using all parameters, achieved a receiver operating characteristic of 0.88 for identifying Gleason pattern 4 PCa. A shorter lifetime value observed in the NADH-associated band was observed for Gleason pattern 4 PCa relative to benign tissue, consistent with increased free NADH from upregulated glycolysis, supporting the biochemical basis for optical differentiation. FLIm demonstrates strong potential for identifying high-grade PCa. Because measurements were performed using a single fiber optic, this approach can be readily integrated into standard prostate biopsy devices to enable FLIm-guided and real-time tissue characterization during the biopsy procedure and to inform targeted tissue collection.

Dissolving microneedles (MN) have emerged as a promising platform for drug delivery, providing a minimally invasive approach to bypass the skin's natural barriers and enhance molecular penetration and diffusion. Their biocompatibility, user-friendly application, and ability to deliver precise therapeutic dosing make them particularly suitable for dermatological use. In addition to pharmacological benefits, dissolving MN possesses a geometric structure that enables optical waveguiding, thereby improving light penetration and distribution. We address a key limitation of photodynamic therapy (PDT): the limited penetration of light into biological tissues. PDT relies on activating photosensitizing agents with specific wavelengths of light to generate cytotoxic species, selectively targeting abnormal or diseased cells while minimizing effects on surrounding healthy tissue. Pyramidal dissolving MN arrays were fabricated from a biocompatible polymer and systematically characterized. Their light distribution profile under laser illumination was evaluated using image analysis. Quantitative analysis of light distribution demonstrates that MN can simultaneously facilitate drug delivery and light distribution. This multifunctionality provides a synergistic therapeutic advantage, as localized drug release is complemented by optimized light delivery, thereby enhancing treatment outcomes. The dual-function platform has significant implications for PDT, enabling the design of integrated therapeutic systems that combine chemical and photonic modalities within a single, biodegradable device. Such systems may be particularly advantageous in resource-limited settings or outpatient care, where ease of use and effectiveness are essential. This strategy offers an approach to overcoming the limitations of conventional light-based therapies, supporting the development of more effective and accessible treatments for skin cancer and other dermatological conditions.

Fluorescent imaging (FI) using indocyanine green (ICG) is a powerful tool in medical diagnostics and surgery. Although numerous studies have focused on optimizing injection protocols and suppressing excitation light leakage, tissue autofluorescence has not been widely recognized as a fundamental factor limiting sensitivity. We aim to quantitatively determine the sensitivity limit for ICG detection in biological tissues, accounting for background signals from both scattered excitation light and tissue autofluorescence. We combine experiments on tissue phantoms with varying ICG concentrations and Monte Carlo numerical simulation of light transport in media with different optical properties. Human skin autofluorescence was quantified in vivo using a nonfluorescent reference and a model medium with a known ICG concentration. It was established that skin autofluorescence is the dominant source of background, exceeding the scattered light by 4 to 25 times in the imaging system used. The determined ultimate sensitivity for ICG detection in biological tissue is 8 &#xd7; 10 - 12 to 3 &#xd7; 10 - 11 &#x2009;&#x2009; M when accounting for the autofluorescence signal. Tissue autofluorescence is a fundamental factor limiting the sensitivity of ICG FI in the near-infrared range. The developed approach will allow for future optimization of imaging equipment and protocols for ICG and other contrast agents.