Autofluorescence Imaging Research Articles

Analysis of near-infrared autofluorescence imaging for detection of inadvertently resected parathyroid glands after endoscopic thyroidectomy

BackgroundPreserving parathyroid function during thyroidectomy is crucial, but remains challenging. Real-time near-infrared autofluorescence (NIRAF) aids surgeons in intraoperative parathyroid gland (PTG) identification. However, its role in detecting PTGs unintentionally removed during surgery is unclear. Study designThis prospective study included adult patients undergoing endoscopic thyroidectomy. Surgeons identified and documented PTGs visually. Excised specimens underwent visual inspection and NIRAF imaging (PDE-Neo II). All fluorescent tissues were dissected and pathologically evaluated (reference standard). One scanned image per lobe was chosen to quantify autofluorescence (AF) intensity. ResultsOverall, 95 patients underwent endoscopic thyroidectomies, with NIRAF imaging applied to 152 excised lobes. Of these, 19 lobes displayed a total of 23 spots with increased intensity. 175 specimens were sent for pathological evaluation, and 7 were confirmed to be parathyroid tissue. NIRAF demonstrated 100.0 % sensitivity and 90.5 % specificity for predicting parathyroid tissue, with 30.4 % positive predictive value, 100.0 % negative predictive value of and 90.9 % accuracy. Quantitatively normalized, the AF signal intensity was significantly higher in NIRAF-positive tissues than negative (4.3 vs 1.2 times, p < 0.0001). Additionally, the AF signal intensity in regions pathologically confirmed of parathyroid tissue was higher than non-parathyroid tissue (9.1 vs 2.1 times, p < 0.0001). ConclusionThis study suggests that NIRAF has high sensitivity and specificity for detecting inadvertently resected PTGs after endoscopic thyroidectomy, contributing to preservation efforts. However, NIRAF-positive tissues still require additional confirmation through multiple methods, emphasizing other examinations to verify that they are indeed parathyroid tissues. Further research is warranted to refine NIRAF imaging parameters.

Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks

PurposeTo classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD).MethodsA retrospective study of 170 eyes from 85 consecutive patients with confirmed STGD. Fundus autofluorescence...

Are the Hypo-Reflective Clumps Associated With Age-Related Macular Degeneration in Adaptive Optics Ophthalmoscopy Autofluorescent?

Hypo-reflective clumps (HRCs) are structures associated with age-related macular degeneration (AMD) that were identified using flood-illumination adaptive optics ophthalmoscopy (FIAO) and hypothesized to be either macrophages that have accumulated melanin through the phagocytosis of retinal pigmented epithelial (RPE) cell organelles or transdifferentiated RPE cells. HRCs may be autofluorescent (AF) in the near infrared (NIR) but clinical NIR autofluorescence imaging lacks the resolution to answer this question definitively. Here, we used near infrared autofluorescence (NIRAF) imaging in fluorescence adaptive optics scanning laser ophthalmoscopy (AOSLO) to determine whether HRCs are AF. Patients with AMD and HRCs underwent imaging with FIAO, optical coherence tomography (OCT), and multi-modal AOSLO (confocal, NIRAF, and non-confocal multi-offset detection using a fiber bundle). HRCs were segmented on FIAO and images, co-registered across modalities, and HRC morphometry and AF were quantified. Eight patients participated (mean age = 79 years, standard deviation [SD] = 5.7, range = 69-89 years, and 5 female patients). Most HRCs (86%, n = 153/178) were autofluorescent on AOSLO. HRC AF signal varied but most uniformly dark HRCs on FIAO showed corresponding AF on AOSLO, whereas heterogeneous HRCs showed a smaller AF area or no AF. These findings are consistent with the hypothesis that HRCs contain AF RPE organelles. A small proportion of HRCs were not AF; these may represent macrophages that have not yet accumulated enough organelles to become AF. HRCs may have clinical significance but further study is needed to understand the interplay among HRCs, RPE cells, and macrophages, and their relationship to geographic atrophy (GA) progression in AMD.

Extent and Topography of Subretinal Drusenoid Deposits Associate With Rod-Mediated Vision in Aging and AMD: ALSTAR2 Baseline.

In AMD, rod-mediated dark adaptation (RMDA) at 5° eccentricity is slower in eyes with subretinal drusenoid deposits (SDDs) than in eyes without. Here we quantified SDD burden using supervised deep learning for comparison to vision and photoreceptor topography. In persons ≥60 years from the Alabama Study on Early Age-Related Macular Degeneration 2, normal, early AMD, and intermediate AMD eyes were classified by the AREDS nine-step system. A convolutional neural network was trained on 55°-wide near-infrared reflectance images for SDD segmentation. Trained graders annotated ground truth (SDD yes/no). Predicted and true datasets agreed (Dice coefficient, 0.92). Inference was manually proofread using optical coherence tomography. The mean SDD area (mm2) was compared among diagnostic groups (linear regression) and to vision (age-adjusted Spearman correlations). Fundus autofluorescence images were used to mask large vessels in SDD maps. In 428 eyes of 428 persons (normal, 218; early AMD, 120; intermediate AMD, 90), the mean SDD area differed by AMD severity (P < 0.0001): 0.16 ± 0.87 (normal), 2.48 ± 11.23 (early AMD), 11.97 ± 13.33 (intermediate AMD). Greater SDD area was associated with worse RMDA (r = 0.27; P < 0.0001), mesopic (r = -0.13; P = 0.02) and scotopic sensitivity (r = -0.17; P < 0.001). SDD topography peaked at 5° superior, extended beyond the Early Treatment of Diabetic Retinopathy Study grid and optic nerve, then decreased. SDD area is associated with degraded rod-mediated vision. RMDA 5° (superior retina) probes where SDD is maximal, closer to the foveal center than the rod peak at 3 to 6 mm (10.4°-20.8°) superior and the further eccentric peak of rod:cone ratio. Topographic data imply that factors in addition to rod density influence SDD formation.

Virtual multiplexed immunofluorescence staining from non-antibody-stained fluorescence imaging for gastric cancer prognosis

Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements. Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms. Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining. The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow. Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

The use of artificial intelligence to detect parathyroid tissue on ex vivo specimens during thyroidectomy and parathyroidectomy procedures using near-infrared autofluorescence signals

In thyroidectomy and parathyroidectomy procedures, diagnostic dilemmas related to whether an index tissue is of parathyroid or nonparathyroid origin frequently arise. Current options of frozen section and parathyroid aspiration are time-consuming. Parathyroid glands appear brighter than surrounding tissues on near-infrared autofluorescence imaging. The aim of this study was to develop an artificial intelligence model differentiating parathyroid tissue on surgical specimens based on near-infrared autofluorescence. With institutional review board approval, an image library of exvivo specimens obtained in thyroidectomy and parathyroidectomy procedures was created between November 2019 and April 2023 at a single academic center. Exvivo autofluorescence images of surgically removed parathyroid glands, thyroid glands, lymph nodes, and thymic tissue were uploaded into an artificial intelligence platform. Two different models were trained, with the first model using autofluorescence images from all specimens, including thyroid, and the second model excluding thyroid, to prevent the effect of specimen size on the results. Deep-learning models were trained to detect autofluorescence signals specific to parathyroid glands. Randomly chosen 80% of data were used for training, 10% for validation, and 10% for testing. Recall, precision, and area under the curve of models were calculated. Surgical procedures included 377 parathyroidectomies, 239 total thyroidectomies, 97 thyroid lobectomies, and 32 central neck dissections. For the development of the model, 1151 images from a total of 678 procedures were used. The dataset comprised 648 parathyroid, 379 thyroid, 104 lymph node, and 20 thymic tissue images. The overall precision, recall, and area under the curve of the model to detect parathyroid tissue were 96.5%, 96.5%, and 0.985, respectively. False negatives were related to dark and large parathyroid glands. The visual deep-learning model developed to identify parathyroid tissue in exvivo specimens during thyroidectomy and parathyroidectomy demonstrated a high sensitivity and positive predictive value. This suggests potential utility of near-infrared autofluorescence imaging to improve intraoperative efficiency by reducing the need for frozen sections and parathyroid hormone aspirations to confirm parathyroid tissue.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3500 Inherited Retinal Disease Patients from the United Kingdom.

To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients. Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients. Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire MEH imaging dataset. Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year. We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Longitudinal assessment of female carriers of choroideremia using multimodal retinal imaging

Background/aimsFemale choroideremia carriers present with a spectrum of disease severity. Unlike in men, the rate of disease progression has not been well characterised in carriers. This longitudinal study aimed to...

PDE6A-Associated Retinitis Pigmentosa, Clinical Characteristics, Genetics, and Natural History

To analyze the genetics, clinical characteristics, and natural history of PDE6A-associated retinitis pigmentosa. Retrospective, longitudinal, observational cohort study. Patients with molecularly confirmed PDE6A-associated retinal dystrophy in a single tertiary referral center. Review of medical records and retinal imaging, including fundus autofluorescence (FAF) imaging and spectral-domain OCT. Genetic results were reviewed, and the detected variants were assessed. Molecular genetic testing, clinical findings including best-corrected visual acuity (BCVA), qualitative and quantitative retinal imaging analysis. Sixteen patients (32 eyes) were identified and evaluated longitudinally. Genetic analysis identified 14 variants in the PDE6A gene, including 8 novel variants. The mean age (± standard deviation, range) was 34.8 years (± 17.4, 12-76) at baseline, with a mean follow-up time of 4.8 years. Best-corrected visual acuity was 0.45 ± 0.45 logarithm of the minimum angle of resolution (logMAR) (range 0.0-1.6) at baseline and 0.65 ± 0.7 logMAR (range 0.0-2.3) at the last visit. Best-corrected visual acuity was similar among eyes in 88% of patients. A hyperautofluorescent ring was observed on FAF in 50% and 43.8% of the eyes at baseline and follow-up visit, respectively, with a mean area of 9.7 ± 4.5 mm2 at baseline and mean of 8.6 ± 4.8 mm2 at the follow-up visit. Mean horizontal ellipsoid zone width (EZW) at baseline was 1765 ± 1093 μm, which decreased to 1580 ± 1077 μm at follow-up. Eighteen eyes exhibited cystoid macular edema at baseline (56%), and 17 eyes (53%) at follow-up. There were statistically significant changes during the follow-up period in terms of BCVA, hyperautofluorescent ring area, and the EZW. This study highlights the natural history of PDE6A-retinopathy. Most patients in this cohort had mild BCVA loss, and slowly progressive disease, based on FAF and OCT measurements. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Current and Future Directions in Fluorescence Imaging-Guided Debridement.

Significance: Sterility and reduction of the bioburden are crucial for healing in chronic wounds such as diabetic foot ulcers. Although there are methods for measuring bioburdens, such as semiquantitative analysis of swab/biopsy samples, microbiological sampling, and molecular diagnostics, these tools are less accessible owing to costs or not being as quick as other methods. These methods are also dependent on clinical assessment by the clinician, and high bacterial burden may appear asymptomatic. Recent Advances: Autofluorescence (AF) imaging is a novel technology for identifying and quantifying chronic inhibitory bacterial load in chronic wounds. Eighty-seven percent of bacteria that frequent chronic wounds have fluorophores that fluoresce under violet light as red or cyan, depending on the type of fluorophore. Therefore, AF image-guided treatment is becoming increasingly effective for physicians to implement wound dressing changes and debridement because bacterial burdens are difficult to locate clinically. Critical Issue: Products such as the commercially available MolecuLight i:X and MolecuLight DX function as handheld cameras for physicians to use as a reference but require additional work to ensure that the photograph will be taken with adequate lighting. Future Directions: Designs for Vision Inc. introduced a device called REVEAL, an AF imaging form factor that allows the device to be worn on top of a pair of glasses, which the physician would wear intraoperatively. The benefits of this form factor include not requiring certain lighting conditions and not having to interpret the results using a handheld camera, allowing the device to be used during active surgical debridement.

AutoMitoNetwork: Software for analyzing mitochondrial networks in autofluorescence images to enable label-free cell classification.

High-resolution mitochondria imaging in combination with image analysis tools have significantly advanced our understanding of cellular function in health and disease. However, most image analysis tools for mitochondrial studies have been designed to work with fluorescently labeled images only. Additionally, efforts to integrate features describing mitochondrial networks with machine learning techniques for the differentiation of cell types have been limited. Herein, we present AutoMitoNetwork software for image-based assessment of mitochondrial networks in label-free autofluorescence images using a range of interpretable morphological, intensity, and textural features. To demonstrate its utility, we characterized unstained mitochondrial networks in healthy retinal cells and in retinal cells exposed to two types of treatments: rotenone, which directly inhibited mitochondrial respiration and ATP production, and iodoacetic acid, which had a milder impact on mitochondrial networks via the inhibition of anaerobic glycolysis. For both cases, our multi-dimensional feature analysis combined with a support vector machine classifier distinguished between healthy cells and those treated with rotenone or iodoacetic acid. Subtle changes in morphological features were measured including increased fragmentation in the treated retinal cells, pointing to an association with metabolic mechanisms. AutoMitoNetwork opens new options for image-based machine learning in label-free imaging, diagnostics, and mitochondrial disease drug development.

Single cell autofluorescence imaging reveals immediate metabolic shifts of neutrophils with activation across biological systems.

Neutrophils, the most abundant leukocytes in human peripheral circulation, are crucial for the innate immune response. They are typically quiescent but rapidly activate in response to infection and inflammation, performing diverse functions such as oxidative burst, phagocytosis, and NETosis, which require significant metabolic adaptation. Deeper insights into such metabolic changes will help identify regulation of neutrophil functions in health and diseases. Due to their short lifespan and associated technical challenges, the metabolic processes of neutrophils are not completely understood. This study uses optical metabolic imaging (OMI), which entails optical redox ratio and fluorescence lifetime imaging microscopy of intrinsic metabolic coenzymes NAD(P)H and FAD to assess the metabolic state of single neutrophils. Primary human neutrophils were imaged in vitro under a variety of activation conditions and metabolic pathway inhibitors, while metabolic and functional changes were confirmed with mass spectrometry, oxidative burst, and NETosis measurements. Our findings show that neutrophils undergo rapid metabolic remodeling to a reduced redox state, followed by a shift to an oxidized redox state during activation. Additionally, single cell analysis reveals a heterogeneous metabolic response across neutrophils and donors to live pathogen infection ( Pseudomonas aeruginosa and Toxoplasma gondii ). Finally, consistent metabolic changes were validated with neutrophils in vivo using zebrafish larvae. This study demonstrates the potential of OMI as a versatile tool for studying neutrophil metabolism and underscores its use across different biological systems, offering insights into neutrophil metabolic activity and function at a single cell level.

Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.

AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. Here, we explore 12 distinct AI architecture combinations to determine the most effective approach for GA segmentation. We investigated various AI architectures, each with distinct combinations of encoders and decoders. The architectures included three decoders-FPN (Feature Pyramid Network), UNet, and PSPNet (Pyramid Scene Parsing Network)-and serve as the foundation framework for segmentation task. Encoders including EfficientNet, ResNet (Residual Networks), VGG (Visual Geometry Group) and Mix Vision Transformer (mViT) have a role in extracting optimum latent features for accurate GA segmentation. Performance was measured through comparison of GA areas between human and AI predictions and Dice Coefficient (DC). The training dataset included 601 FAF images from AREDS2 study and validation included 156 FAF images from the GlaxoSmithKline study. The mean absolute difference between grader measured and AI predicted areas ranged from -0.08 (95% CI = -1.35, 1.19) to 0.73 mm2 (95% CI = -5.75,4.29) and DC between 0.884-0.993. The best-performing models were UNet and FPN frameworks with mViT, and the least-performing models were PSPNet framework. The choice of AI architecture impacts GA segmentation performance. Vision transformers with FPN and UNet architectures demonstrate stronger suitability for this task compared to Convolutional Neural Network- and PSPNet-based models. Selecting an AI architecture must be tailored to the specific goals of the project, and developers should consider which architecture is ideal for their project.

Multimodal imaging analysis of autosomal recessive bestrophinopathy: Case series.

Autosomal recessive bestrophinopathy (ARB) is a subtype of bestrophinopathy caused by biallelic mutations of the BEST1 gene, which affect the retinal pigment epithelium (RPE). Studying RPE abnormalities through imaging is essential for understanding ARB. This case series involved the use of multimodal imaging techniques, namely autofluorescence (AF) imaging at 488 nm [short-wavelength AF] and 785 nm [near-infrared AF (NIR-AF)] and polarization-sensitive optical coherence tomography (PS-OCT), to investigate RPE changes in 2 siblings with ARB. Two Japanese siblings (Case 1: male, followed for 20-23 years; Case 2: female, followed for 13-17 years) carried compound heterozygous mutations of the BEST1 gene. Both siblings were diagnosed with ARB. Multimodal imaging techniques were used to evaluate RPE changes. Both siblings had funduscopic changes similar to those seen in the vitelliruptive stage of Best vitelliform macular dystrophy during the follow-up period. NIR-AF imaging showed hypo-AF of the entire macular lesion in both cases, and this hypo-AF remained stable over time. PS-OCT confirmed reduced RPE melanin content in these hypo-AF areas. Additionally, hyper-NIR-AF dots were observed within hypo-NIR-AF areas. Concomitant identification of focally thickened RPE melanin on PS-OCT imaging and hyper-AF on short-wavelength AF imaging at the sites containing hyper-NIR-AF dots indicated that the hyper-NIR-AF dots had originated from either stacked RPE cells or RPE dysmorphia. We confirmed RPE abnormalities in ARB, including diffuse RPE melanin damage in the macula alongside evidence of RPE activity-related changes. This case series demonstrates that multimodal imaging, particularly NIR-AF and PS-OCT, provides detailed insights into RPE alterations in ARB.

Non-syndromic OTX2-associated pattern dystrophy: a 10-year multimodal imaging study

PurposeTo report novel multimodal imaging features and long-term follow-up of Orthodenticle Homeobox 2 (OTX2)-associated pattern Gdystrophy.MethodsA 14-year-old boy referred with glaucoma suspect and macular pigmentation underwent fundus autofluorescence imaging, optical coherence tomography, fluorescein and indocyanine green angiography, visual field test, microperimetry and electrophysiology over a ten-year period. Next-generation sequencing panel identified a de novo heterozygous likely pathogenic OTX2 variant, c.259G>A, [p.(Glu87Lys)].ResultsVisual acuity was 20/40 OD and 20/30 OS. Examination showed bilateral enlarged optic nerve heads and increased disc cupping, multiple cilioretinal arteries, a pigmentary maculopathy with stellate-shaped region of hypoautofluorescence, shallow serous macular detachment, subretinal deposits and temporal avascular retina. Angiography showed no source of leakage and absence of retinal neovascularisation despite extensive peripheral non perfusion. Electrophysiological assessments demonstrated mild progressive rod and cone pathway abnormalities, reduced light-adapted b:a ratio, and reduced Arden ratio on electro-oculogram. Ten-year follow-up confirmed a stable disease course despite persistent submacular fluid. There was no associated pituitary structural abnormality or dysfunction.ConclusionsThis case study contributes to further understanding of OTX2-associated pattern dystrophy, highlighting its stability over 10 years. Further investigation into inter-individual and intrafamilial variability is warranted.

Advanced optical imaging techniques for bladder cancer detection and diagnosis: a systematic review.

To systematically assess the current available literature concerning advanced optical imaging methods for the detection and diagnosis of bladder cancer (BCa), focusing particularly on the sensitivity and specificity of these techniques. First a scoping search was performed to identify all available optical techniques for BCa detection and diagnosis. The optical imaging techniques used for detecting BCa are: the Storz professional image enhancement system (IMAGE1 S), narrow-band imaging (NBI), photoacoustic imaging (PAI), autofluorescence imaging (AFI), photodynamic diagnosis (PDD), and scanning fibre endoscopy (SFE). The staging and grading techniques for BCa are: optical coherence tomography (OCT), confocal laser endomicroscopy (CLE), Raman spectroscopy, endocytoscopy, and non-linear optical microscopy (NLO). Then a systematic literature search was conducted using MEDLINE, EMBASE and Web of Science from inception to 21 November 2023. Articles were screened and selected by two independent reviewers. Inclusion criteria were: reporting on both the sensitivity and specificity of a particular technique and comparison to histopathology, and in the case of a detection technique comparison to white light cystoscopy (WLC). Out of 6707 articles, 189 underwent full-text review, resulting in 52 inclusions. No articles met criteria for IMAGE1 S, PAI, SFE, Raman spectroscopy, and endocytoscopy. All detection techniques showed higher sensitivity than WLC, with NBI leading (87.8-100%). Overall, detection technique specificity was comparable to WLC, with PDD being most specific (23.3-100%). CLE and OCT varied in sensitivity and specificity, with OCT showing higher specificity for BCa diagnosis, notably for carcinoma in situ (97-99%) compared to CLE (62.5-81.3%). NLO demonstrated high sensitivity and specificity (90-97% and 77-100%, respectively) based on limited data from two small ex vivo studies. Optical techniques with the most potential are PDD for detecting and OCT for staging and grading BCa. Further research is crucial to validate their integration into routine practice and explore the value of other imaging techniques.

In vivo and in situ robust quantitative optical biopsy of hepatocellular carcinoma and metastasis based on two-photon autofluorescence imaging.

Two-photon autofluorescence (TPAF) imaging is able to offer precise cellular metabolic information with high spatiotemporal resolution, making it a promising biopsy tool. The technique is greatly hampered by the complexity of either the optical system or data processing. Here, the excitation wavelength was optimized to simultaneously excite both flavin adenine dinucleotide and nicotinamide adenine dinucleotide and eliminate the unexpected TPAF. The optical redox ratio (ORR) images were robustly achieved without additional calibration under the optimized single-wavelength excitation. The in vitro, ex vivo, and in vivo biopsy by the TPAF method were systematically studied and compared using hepato-cellular carcinoma and metastasis as examples. It was demonstrated that the proposed TPAF method simplified the optical system, improved the robustness of ORR, and enabled early-stage cancer diagnosis, showing distinguished advantages as compared with previous methods.

Metabolic changes in Toxoplasma gondii-infected host cells measured by autofluorescence imaging.

Toxoplasma gondii, the causative agent of toxoplasmosis, is an obligate intracellular parasite that infects warm-blooded vertebrates across the world. In humans, seropositivity rates of T. gondii range from 10% to 90% across communities. Despite its prevalence, few studies address how T. gondii infection changes the metabolism of host cells. In this study, we investigate how T. gondii manipulates the host cell metabolic environment by monitoring the metabolic response over time using noninvasive autofluorescence lifetime imaging of single cells, metabolite analysis, extracellular flux analysis, and reactive oxygen species (ROS) production. Autofluorescence lifetime imaging indicates that infected host cells become more oxidized and have an increased proportion of bound NAD(P)H compared to uninfected controls. Over time, infected cells also show decreases in levels of intracellular glucose and lactate, increases in oxygen consumption, and variability in ROS production. We further examined changes associated with the pre-invasion "kiss and spit" process using autofluorescence lifetime imaging, which also showed a more oxidized host cell with an increased proportion of bound NAD(P)H over 48 hours compared to uninfected controls, suggesting that metabolic changes in host cells are induced by T. gondii kiss and spit even without invasion.IMPORTANCEThis study sheds light on previously unexplored changes in host cell metabolism induced by T. gondii infection using noninvasive, label-free autofluorescence imaging. In this study, we use optical metabolic imaging (OMI) to measure the optical redox ratio (ORR) in conjunction with fluorescence lifetime imaging microscopy (FLIM) to noninvasively monitor single host cell response to T. gondii infection over 48 hours. Collectively, our results affirm the value of using autofluorescence lifetime imaging to noninvasively monitor metabolic changes in host cells over the time course of a microbial infection. Understanding this metabolic relationship between the host cell and the parasite could uncover new treatment and prevention options for T. gondii infections worldwide.

Computer-assisted discrimination of cancerous and pre-cancerous from benign oral lesions based on multispectral autofluorescence imaging endoscopy.

Diagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients. Therefore, clinical diagnostic technologies for fast, minimally invasive, and accurate oral cancer screening are urgently needed. This study investigated the use of multispectral autofluorescence imaging endoscopy for the automated and noninvasive discrimination of cancerous and precancerous from benign oral epithelial lesions. In vivo multispectral autofluorescence endoscopic images of clinically suspicious oral lesions were acquired from 67 patients undergoing tissue biopsy examination. The imaged lesions were classified as precancerous (n = 4), cancerous (n = 29), and benign (n = 34) lesions based on histopathology diagnosis. Multispectral autofluorescence intensity feature maps were generated for each oral lesion and used to train and optimize support vector machine (SVM) models for automated discrimination of cancerous and precancerous from benign oral lesions. After a leave-one-patient-out cross-validation strategy, an optimized SVM model developed with four multispectral autofluorescence features yielded levels of sensitivity and specificity of 85% and 71%, respectively and overall accuracy of 78% in the discrimination of cancerous/precancerous versus benign oral lesions. This study demonstrates the potentials of a computer-assisted detection system based on multispectral autofluorescence imaging endoscopy for the early detection of cancerous and precancerous oral lesions.

Machine Learning for Extraction of Image Features Associated with Progression of Geographic Atrophy

Background: Several studies have investigated various features and models in order to understand the growth and progression of the ocular disease geographic atrophy (GA). Commonly assessed features include age, sex, smoking, alcohol consumption, sedentary lifestyle, hypertension, and diabetes. There have been inconsistencies regarding which features correlate with GA progression. Chief amongst these inconsistencies is whether the investigated features are readily available for analysis across various ophthalmic institutions. Methods:In this study, we focused our attention on the association of fundus autofluorescence (FAF) imaging features and GA progression. Our method included feature extraction using radiomic processes and feature ranking by machine learning incorporating the algorithm XGBoost to determine the best-ranked features. This led to the development of an image-based linear mixed-effects model, which was designed to account for slope change based on within-subject variability and inter-eye correlation. Metrics used to assess the linear mixed-effects model included marginal and conditional R2, Pearson’s correlation coefficient (r), root mean square error (RMSE), mean error (ME), mean absolute error (MAE), mean absolute deviation (MAD), the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and loglikelihood. Results: We developed a linear mixed-effects model with 15 image-based features. The model results were as follows: R2 = 0.96, r = 0.981, RMSE = 1.32, ME = −7.3 × 10−15, MAE = 0.94, MAD = 0.999, AIC = 2084.93, BIC = 2169.97, and log likelihood = −1022.46. Conclusions: The advantage of our method is that it relies on the inherent properties of the image itself, rather than the availability of clinical or demographic data. Thus, the image features discovered in this study are universally and readily available across the board.