Lightweight Raman spectroscope using time-correlated photon-counting detection.

Raman spectroscopy is an important tool in understanding chemical components of various materials. However, the excessive weight and energy consumption of a conventional CCD-based Raman spectrometer forbids its applications under extreme conditions, including unmanned aircraft vehicles (UAVs) and Mars/Moon rovers. In this article, we present a highly sensitive, shot-noise-limited, and ruggedized Raman signal acquisition using a time-correlated photon-counting system. Compared with conventional Raman spectrometers, over 95% weight, 65% energy consumption, and 70% cost could be removed through this design. This technique allows space- and UAV-based Raman spectrometers to robustly perform hyperspectral Raman acquisitions without excessive energy consumption.

Lensless, ultra-wideband fiber optic rotary joint for biomedical applications.

The demands of optical fiber-based biomedical applications can, in many cases, outstrip the capabilities of lens-based commercially available fiber optic rotary joints. In some circumstances, it is necessary to use very broad spectral bandwidths (near UV to short-wave IR) and specialized optical fibers, such as double-clad fiber, and have the capacity to accommodate high rotational velocities. The broad spectrum, stretching down into the UV, presents two problems: (1) adequate chromatic correction in the lenses across the entire bandwidth and (2) strong UV absorption by the fluids used to lubricate the rotary joint. To accommodate these types of applications, we have developed an ultra-wideband lensless fiber optic rotary joint based on the principle that when two optical fibers are coaligned and placed in contact (or very close), the optical losses at the junction are very low. The advances demonstrated here enable excellent performance (<0.2 dB insertion loss), even down into the UV and spanning a wavelength range of at least 355-1360 nm with single-mode, multimode, and double-clad fibers. We also demonstrate excellent performance, ∼0.38 dB insertion loss, at rotational velocities up to 8800 rpm (146 Hz). To the best of our knowledge, this is the first demonstration of this type of rotary joint capable of such a wide bandwidth and high rotational velocities.

Deconvolution of fluorescence lifetime imaging microscopy by a library of exponentials.

Fluorescence lifetime microscopy imaging (FLIM) is an optic technique that allows a quantitative characterization of the fluorescent components of a sample. However, for an accurate interpretation of FLIM, an initial processing step is required to deconvolve the instrument response of the system from the measured fluorescence decays. In this paper, we present a novel strategy for the deconvolution of FLIM data based on a library of exponentials. Our approach searches for the scaling coefficients of the library by non-negative least squares approximations plus Thikonov/l(2) or l(1) regularization terms. The parameters of the library are given by the lower and upper bounds in the characteristic lifetimes of the exponential functions and the size of the library, where we observe that this last variable is not a limiting factor in the resulting fitting accuracy. We compare our proposal to nonlinear least squares and global non-linear least squares estimations with a multi-exponential model, and also to constrained Laguerre-base expansions, where we visualize an advantage of our proposal based on Thikonov/l(2) regularization in terms of estimation accuracy, computational time, and tuning strategy. Our validation strategy considers synthetic datasets subject to both shot and Gaussian noise and samples with different lifetime maps, and experimental FLIM data of ex-vivo atherosclerotic plaques and human breast cancer cells.

Volumetric structured illumination microscopy enabled by a tunable-focus lens.

We present a mechanical-scan-free method for volumetric imaging of biological tissue. The optical sectioning is provided by structured illumination, and the depth of the imaging plane is varied using an electrically tunable-focus lens. We characterize and evaluate the ability of this axial-scanning mechanism in structured illumination microscopy and demonstrate its ability to perform subcellular resolution imaging in oral mucosa ex vivo. The proposed mechanism can potentially convert any wide-field microscope to a 3D-imaging platform without the need for mechanical scanning of imaging optics and/or sample.

Estimation of the number of fluorescent end-members for quantitative analysis of multispectral FLIM data.

Multispectral fluorescence lifetime imaging (m-FLIM) can potentially allow identifying the endogenous fluorophores present in biological tissue. Quantitative description of such data requires estimating the number of components in the sample, their characteristic fluorescent decays, and their relative contributions or abundances. Unfortunately, this inverse problem usually requires prior knowledge about the data, which is seldom available in biomedical applications. This work presents a new methodology to estimate the number of potential endogenous fluorophores present in biological tissue samples from time-domain m-FLIM data. Furthermore, a completely blind linear unmixing algorithm is proposed. The method was validated using both synthetic and experimental m-FLIM data. The experimental m-FLIM data include in-vivo measurements from healthy and cancerous hamster cheek-pouch epithelial tissue, and ex-vivo measurements from human coronary atherosclerotic plaques. The analysis of m-FLIM data from in-vivo hamster oral mucosa identified healthy from precancerous lesions, based on the relative concentration of their characteristic fluorophores. The algorithm also provided a better description of atherosclerotic plaques in term of their endogenous fluorophores. These results demonstrate the potential of this methodology to provide quantitative description of tissue biochemical composition.

Flexible endoscope for continuous in vivo multispectral fluorescence lifetime imaging.

Fluorescence lifetime imaging (FLIM) offers a noninvasive approach for characterizing the biochemical composition of biological tissue. There has been an increasing interest in the application of multispectral FLIM for medical diagnosis. Central to the clinical translation of FLIM technology is the development of compact and high-speed endoscopy systems. Unfortunately, the predominant multispectral FLIM approaches suffer from limitations that impede the development of endoscopy systems that are suitable for in vivo tissue imaging. We present a compact wide-field time-gated FLIM flexible endoscope capable of continuous lifetime imaging of up to three fluorescence emission bands simultaneously. This endoscope design will facilitate the evaluation of FLIM for in vivo applications.

Direct frequency domain fluorescence lifetime imaging using simultaneous ultraviolet and visible excitation.

Due to the complexity, limited practicality, and cost of conventional fluorescence lifetime imaging/microscopy (FLIM) instrumentation, FLIM adoption has been mostly limited to academic settings. We present a novel point scanning frequency-domain (FD) FLIM instrumentation design capable of simultaneous multi-wavelength excitation, simultaneous multispectral detection, and sub-nanosecond to nanosecond fluorescence lifetime estimation. Fluorescence excitation is implemented using intensity-modulated CW diode lasers that are available in a selection of wavelengths spanning the UV-VI-NIR range (375-1064 nm). Digital laser intensity modulation was adopted to enable simultaneous frequency interrogation at the fundamental frequency and corresponding harmonics. Time-resolved fluorescence detection is implemented using low-cost, fixed-gain, narrow bandwidth (100 MHz) avalanche photodiodes, thus, enabling cost-effective fluorescence lifetime measurements at multiple emission spectral bands simultaneously. Synchronized laser modulation and fluorescence signal digitization (250 MHz) is implemented using a common field-programmable gate array (FPGA). This synchronization reduces temporal jitter, which simplifies instrumentation, system calibration, and data processing. The FPGA also allows for the implementation of the real-time processing of the fluorescence emission phase and modulation at up to 13 modulation frequencies (processing rate matching the sampling rate of 250 MHz). Rigorous validation experiments have demonstrated the capabilities of this novel FD-FLIM implementation to accurately measure fluorescence lifetimes in the range of 0.5-12 ns. In vivo endogenous, dual-excitation (375nm/445nm), multispectral (four bands) FD-FLIM imaging of human skin and oral mucosa at 12.5 kHz pixel rate and room-light conditions was also successfully demonstrated. This versatile, simple, compact, and cost-effective FD-FLIM implementation will facilitate the clinical translation of FLIM imaging and microscopy.

Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis.

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.

OBJECTIVE: To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. METHODS: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. RESULTS: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. CONCLUSION: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.

Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.

Significance: Coronary heart disease has the highest rate of death and morbidity in the Western world. Atherosclerosis is an asymptomatic condition that is considered the primary cause of cardiovascular diseases. The accumulation of low-density lipoprotein triggers an inflammatory process in focal areas of arteries, which leads to the formation of plaques. Lipid-laden plaques containing a necrotic core may eventually rupture, causing heart attack and stroke. Lately, intravascular optical coherence tomography (IV-OCT) imaging has been used for plaque assessment. The interpretation of the IV-OCT images is performed visually, which is burdensome and requires highly trained physicians for accurate plaque identification. Aim: Our study aims to provide high throughput lipid-laden plaque identification that can assist in vivo imaging by offering faster screening and guided decision making during percutaneous coronary interventions. Approach: An A-line-wise classification methodology based on time-series deep learning is presented to fulfill this aim. The classifier was trained and validated with a database consisting of IV-OCT images of 98 artery sections. A trained physician with expertise in the analysis of IV-OCT imaging provided the visual evaluation of the database that was used as ground truth for training and validation. Results: This method showed an accuracy, sensitivity, and specificity of 89.6%, 83.6%, and 91.1%, respectively. This deep learning methodology has the potential to increase the speed of lipid-laden plaques identification to provide a high throughput of more than 100 B-scans/s. Conclusions: These encouraging results suggest that this method will allow for high throughput video-rate atherosclerotic plaque assessment through automated tissue characterization for in vivo imaging by providing faster screening to assist in guided decision making during percutaneous coronary interventions.

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning.

SIGNIFICANCE: Accurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones. AIM: To develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy. APPROACH: We tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy. RESULTS: Classification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered. CONCLUSIONS: Simple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.

Clinical label-free endoscopic imaging of biochemical and metabolic autofluorescence biomarkers of benign, precancerous, and cancerous oral lesions.

Early detection is critical for improving the survival rate and quality of life of oral cancer patients; unfortunately, dysplastic and early-stage cancerous oral lesions are often difficult to distinguish from oral benign lesions during standard clinical oral examination. Therefore, there is a critical need for novel clinical technologies that would enable reliable oral cancer screening. The autofluorescence properties of the oral epithelial tissue provide quantitative information about morphological, biochemical, and metabolic tissue and cellular alterations accompanying carcinogenesis. This study aimed to identify novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer that could be clinically imaged using novel multispectral autofluorescence lifetime imaging (maFLIM) endoscopy technologies. In vivo maFLIM clinical endoscopic images of benign, precancerous, and cancerous lesions from 67 patients were acquired using a novel maFLIM endoscope. Widefield maFLIM feature maps were generated, and statistical analyses were applied to identify maFLIM features providing contrast between dysplastic/cancerous vs. benign oral lesions. A total of 14 spectral and time-resolved maFLIM features were found to provide contrast between dysplastic/cancerous vs. benign oral lesions, representing novel biochemical and metabolic autofluorescence biomarkers of oral epithelial dysplasia and cancer. To the best of our knowledge, this is the first demonstration of clinical widefield maFLIM endoscopic imaging of novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer, supporting the potential of maFLIM endoscopy for early detection of oral cancer.

Development and Characterization of PLGA-Based Multistage Delivery System for Enhanced Payload Delivery to Targeted Vascular Endothelium.

Vascular-targeted drug delivery remains an attractive platform for therapeutic and diagnostic interventions in human diseases. This work focuses on the development of a poly-lactic-co-glycolic-acid (PLGA)-based multistage delivery system (MDS). MDS consists of two stages: a micron-sized PLGA outer shell and encapsulated drug-loaded PLGA nanoparticles. Nanoparticles with average diameters of 76, 119, and 193 nm are successfully encapsulated into 3-6 µm MDS. Sustained in vitro release of nanoparticles from MDS is observed for up to 7 days. Both MDS and nanoparticles arebiocompatible with human endothelial cells. Sialyl-Lewis-A (sLeA ) is successfully immobilized on the MDS and nanoparticle surfaces to enable specific targeting of inflamed endothelium. Functionalized MDS demonstrates a 2.7-fold improvement in endothelial binding compared to PLGA nanoparticles from human blood laminar flow. Overall, the presented results demonstrate successful development and characterization of MDS and suggest that MDS can serve as an effective drug carrier, which can enhance the margination of nanoparticles to the targeted vascular wall.

End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions.

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis using multispectral autofluorescence lifetime imaging (maFLIM) endoscopy. Our method uses an autoencoder framework jointly trained with a classifier designed to handle overfitting problems with reduced databases, which is often the case in healthcare applications. The autoencoder guides the feature extraction process through the reconstruction loss and enables the potential use of unsupervised data for domain adaptation and improved generalization. The classifier ensures the features extracted are task-specific, providing discriminative information for the classification task. The data-driven feature extraction method automatically generates task-specific features directly from fluorescence decays, eliminating the need for iterative signal reconstruction. We validate our proposed neural network method against support vector machine (SVM) baselines, with our method showing a 6.5%-8.3% increase in sensitivity. Our results show that neural networks that implement data-driven feature extraction provide superior results and enable the capacity needed to target specific issues, such as inter-patient variability and the heterogeneity of oral lesions.Clinical relevance- We improve standard classification algorithms for in vivo diagnosis of oral cancer lesions from maFLIm for clinical use in cancer screening, reducing unnecessary biopsies and facilitating early detection of oral cancer.

Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach.

The deconvolution process is a key step for quantitative evaluation of fluorescence lifetime imaging microscopy (FLIM) samples. By this process, the fluorescence impulse responses (FluoIRs) of the sample are decoupled from the instrument response (InstR). In blind deconvolution estimation (BDE), the FluoIRs and InstR are jointly extracted from a dataset with minimal a priori information. In this work, two BDE algorithms are introduced based on linear combinations of multi-exponential functions to model each FluoIR in the sample. For both schemes, the InstR is assumed with a free-form and a sparse structure. The local perspective of the BDE methodology assumes that the characteristic parameters of the exponential functions (time constants and scaling coefficients) are estimated based on a single spatial point of the dataset. On the other hand, the same exponential functions are used in the whole dataset in the global perspective, and just the scaling coefficients are updated for each spatial point. A least squares formulation is considered for both BDE algorithms. To overcome the nonlinear interaction in the decision variables, an alternating least squares (ALS) methodology iteratively solves both estimation problems based on non-negative and constrained optimizations. The validation stage considered first synthetic datasets at different noise types and levels, and a comparison with the standard deconvolution techniques with a multi-exponential model for FLIM measurements, as well as, with two BDE methodologies in the state of the art: Laguerre basis, and exponentials library. For the experimental evaluation, fluorescent dyes and oral tissue samples were considered. Our results show that local and global perspectives are consistent with the standard deconvolution techniques, and they reached the fastest convergence responses among the BDE algorithms with the best compromise in FluoIRs and InstR estimation errors.

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

Enhanced detection of oral dysplasia by structured illumination fluorescence lifetime imaging microscopy.

We demonstrate that structured illumination microscopy has the potential to enhance fluorescence lifetime imaging microscopy (FLIM) as an early detection method for oral squamous cell carcinoma. FLIM can be used to monitor or detect changes in the fluorescence lifetime of metabolic cofactors (e.g. NADH and FAD) associated with the onset of carcinogenesis. However, out of focus fluorescence often interferes with this lifetime measurement. Structured illumination fluorescence lifetime imaging (SI-FLIM) addresses this by providing depth-resolved lifetime measurements, and applied to oral mucosa, can localize the collected signal to the epithelium. In this study, the hamster model of oral carcinogenesis was used to evaluate SI-FLIM in premalignant and malignant oral mucosa. Cheek pouches were imaged in vivo and correlated to histopathological diagnoses. The potential of NADH fluorescence signal and lifetime, as measured by widefield FLIM and SI-FLIM, to differentiate dysplasia (pre-malignancy) from normal tissue was evaluated. ROC analysis was carried out with the task of discriminating between normal tissue and mild dysplasia, when changes in fluorescence characteristics are localized to the epithelium only. The results demonstrate that SI-FLIM (AUC = 0.83) is a significantly better (p-value = 0.031) marker for mild dysplasia when compared to widefield FLIM (AUC = 0.63).

High-speed multispectral fluorescence lifetime imaging implementation for in vivo applications.

Fluorescence lifetime imaging microscopy (FLIM) offers a noninvasive approach for characterizing the biochemical composition of biological tissue. In recent years, there has been an increasing interest in the application of multispectral FLIM for medical diagnosis. Central to the clinical translation of FLIM technology is the development of robust, fast, and cost-effective FLIM instrumentation suitable for in vivo tissue imaging. Unfortunately, the predominant multispectral FLIM approaches suffer from limitations that impede the development of high-speed instruments for in vivo applications. We present a cost-effective scanning multispectral FLIM implementation capable of achieving pixel rates on the order of tens of kilohertz, which will facilitate the evaluation of FLIM for in vivo applications.

Direct frequency domain fluorescence lifetime imaging using field programmable gate arrays for real time processing.

Frequency domain (FD) fluorescence lifetime imaging (FLIM) involves the excitation of the sample of interest with a modulated light source and digitization of the fluorescence emission for further analysis. Traditional FD-FLIM systems use heterodyne or homodyne detection, where the excitation light source and detector are modulated at specific frequency(s). More recently, FD-FLIM systems that use reflection of the light source as a trigger or phase reference for lifetime calculations have been developed. These detection schemes, however, require extra components that increase the cost and complexity of the FD-FLIM system. Here, we report a novel FD-FLIM detection scheme whereby the light source modulation and emission digitization are implemented using Field Programmable Gate Arrays (FPGAs), and fixed gain avalanche photodiodes are used for fluorescence detection. The reported FD-FLIM system was designed for probing nanosecond lifetime fluorophores (2-10 ns) at three emission bands simultaneously. The system utilizes a 375 nm diode laser for excitation at multiple simultaneous modulation frequencies (between 1 MHz and 83 MHz, bandwidth limited intentionally by using a lowpass filter) and three fixed gain avalanche photodiodes for simultaneous detection of three emission bands: 405/20 nm, 440/40 nm, and 525/50 nm (center/FWHM). Real-time computation of the modulation and phase lifetimes is simply performed by direct application of the discrete Fourier transform (max. of 10 frequencies) to the digitized fluorescence emission signals. The accuracy and sensitivity of this novel FD-FLIM detection scheme was demonstrated by imaging standard fluorophores and ex vivo unfixed human coronary artery tissue samples.

Multispectral autofluorescence dermoscope for skin lesion assessment.

Basal cell carcinoma (BCC) is the most common type of skin cancer. Diagnosis and edge assessment of BCC lesions are based on clinical and dermoscopy evaluation, which are strongly dependent on the expertise and training of the physician. There is a high rate of underdiagnosis because BCC is frequently confused with certain common benign lesions and is often indistinguishable from the surrounding healthy tissue. In the present study, a multispectral fluorescence lifetime imaging (FLIm) dermoscopy system, designed for imaging and analyzing the autofluorescence emission of skin tissue, was used to image thirty-eight patients with diagnosed nodular BCC (nBCC) lesions, using clinically acceptable levels of excitation light exposure. With this system, skin autofluorescence was imaged simultaneously using three emission bands: 390 ±â€¯20 nm, 452 ±â€¯22 nm, and >496 nm, preferentially targeting collagen, NADH, and FAD autofluorescence, respectively. Statistical classifiers based on FLIm features developed to discriminate BCC from healthy tissue showed promising performance (ROC area-under-the-curve of 0.82). This study demonstrates the feasibility of clinically performing multispectral endogenous FLIm dermoscopy providing baseline results indicating the potential of this technology as an image-guided tool to improve the delineation of nBCC during surgical lesion resection.