Semi-automated Image Analysis Research Articles

Distribution of MRI-derived T2 values as a biomarker for in vivo rapid screening of phenotype severity in mdx mice

Background The pathology in Duchenne muscular dystrophy (DMD) is characterized by degenerating muscle fibers, inflammation, fibro-fatty infiltrate, and edema, and these pathological processes replace normal healthy muscle tissue. The mdx mouse model is one of the most commonly used preclinical models to study DMD. Mounting evidence has emerged illustrating that muscle disease progression varies considerably in mdx mice, with inter-animal differences as well as intra-muscular differences in pathology in individual mdx mice. This variation is important to consider when conducting assessments of drug efficacy and in longitudinal studies. We developed a magnetic resonance imaging (MRI) segmentation and analysis pipeline to rapidly and non-invasively measure the severity of muscle disease in mdx mice. Methods Wildtype and mdx mice were imaged with MRI and T2 maps were obtained axially across the hindlimbs. A neural network was trained to rapidly and semi-automatically segment the muscle tissue, and the distribution of resulting T2 values was analyzed. Interdecile range and Pearson Skew were identified as biomarkers to quickly and accurately estimate muscle disease severity in mice. Results The semiautomated segmentation tool reduced image processing time approximately tenfold. Measures of Pearson skew and interdecile range based on that segmentation were repeatable and reflected muscle disease severity in healthy wildtype and diseased mdx mice based on both qualitative observation of images and correlation with Evans blue dye uptake. Conclusion Use of this rapid, non-invasive, semi-automated MR image segmentation and analysis pipeline has the potential to transform preclinical studies, allowing for pre-screening of dystrophic mice prior to study enrollment to ensure more uniform muscle disease pathology across treatment groups, improving study outcomes.

Characterization of drusen formation in a primary porcine tissue culture model of dry AMD

Age-related macular degeneration (AMD) affects millions of individuals worldwide and is a leading cause of blindness in the elderly. In dry AMD, lipoproteinaceous deposits called drusen accumulate between the retinal pigment epithelium (RPE) and Bruch's membrane, leading to impairment of oxygen and nutrient trafficking to the neural retina, and degeneration of the overlying photoreceptor cells. Owing to key differences in human and animal ocular anatomy and the slowly progressing nature of the disease, AMD is not easily modeled invivo. In this study, we further characterize a "drusen-in-a-dish" primary porcine RPE model system by employing vital lipid staining to monitor sub-RPE deposition over time in monolayers of cells cultured on porous transwell membranes. We demonstrate for the first time using a semi-automated image analysis pipeline that the number and size of sub-RPE deposits increases gradually but significantly over time and confirm that sub-RPE deposits grown in culture immunostain positive for multiple known components found in human drusen. As a result, we propose that drusen-in-a-dish cell culture models represent a high-throughput and cost-scalable alternative to animal models in which to study the pathobiology of drusen accumulation and may serve as useful tools for screening novel therapeutics aimed at treating dry AMD.

Reduced Expression of REG4 as a Sign of Altered Goblet Cell Function in Necrotizing Enterocolitis.

Defective Goblet cells have been proposed to be involved in necrotizing enterocolitis (NEC). The aim was to study the expression of the Goblet cell marker REG4 and its potential involvement in NEC in preterm infants with and without NEC. Seventy histologically intact intestinal biopsies were studied: 43 were collected during surgery due to NEC (NEC group: 26.5 ± 3.0 weeks' gestational age [wGA]), and 27 from individuals who underwent surgery due to other conditions (Control group; 36.1 ± 4.5 wGA). The tissue samples were immunohistochemically stained for REG4. REG4 expression was quantified with a semiautomated digital image analysis and with clinical data compared between the groups. REG4 expression was lower in the NEC group than in the Control group (p = 0.035). Low REG4 expression correlated to the risk of NEC (p = 0.023). In a multivariable logistic regression analysis including GA and REG4 expression for NEC risk, only GA (p < 0.001) and not REG4 expression (p = 0.206) was associated with NEC risk. This study concludes that Goblet cell dysfunction may be involved in NEC development, as low expression of the Goblet cell marker REG4 was related to an increased NEC risk in preterm infants. Maturity could however not be excluded as a potential confounder for REG4 expression. · REG4 is a specific Goblet cell marker not yet studied in NEC.. · REG4 was quantified in intestinal biopsies from infants with and without NEC.. · REG4 expression was lower in infants with NEC, and expression seems to be maturity dependent..

A Flexible Semi-automated Assay for Assessing Radiation-sensitization and Toxicity in the Mouse Intestine.

The aim of this study was to develop an enhanced intestinal toxicity assay with three outputs assessing proliferation, villi morphology and DNA damage after irradiation. Whole 5 cm jejunal lengths were collected from mice following total body x-ray irradiation (0-15 Gy) at 0-84 h. Tissues were wrapped into swirls for cryopreservation and immunohistochemically stained for EdU, CD31, and γH2AX. A semi-automated image analysis was developed for the proliferation, villi morphology, and DNA damage models. Proliferation assessed via EdU staining varied with cycles of damage repair, hyperproliferation, and homeostasis after radiation, with the time to onset of each cycle variable based on radiation dose. An analysis model evaluating the amount of proliferation per unit length of jejunum analyzed was developed, with a dose-response curve identified at 48 h post treatment. The villi length model measured the length of intact and healthy CD31-stained capillary beds between the crypts and villi tips at 3.5 days post treatment within a 0-10 Gy dose range. The DNA damage model evaluated the intensity of γH2AX staining within cellular nuclei, with a useful dose-response identified at 1 h post-radiation treatment. This assay demonstrates flexibility for assessing radiation-induced damage, with analysis of proliferation, villi length, or direct DNA damage achievable at defined time points and within useful radiation dose curves. The software-assisted image analysis allows for rapid, comprehensive, and objective data generation with an assay turnover time of days instead of weeks on samples that are representative of most of the treated jejunum.

Prognostic value of the international association for the study of lung cancer grading system and its association with the tumor microenvironment in stage I EGFR-muted lung adenocarcinoma

IntroductionThe International Association for the Study of Lung Cancer (IASLC) grading system predicts early lung adenocarcinoma outcomes. MethodsThe purpose of this study is to examine prognostic value of the IASLC grading system and its association with the tumor microenvironment (TME) in Stage I EGFR-muted lung adenocarcinoma. Based on the IASLC grading system, we compared the clinicopathological characteristics of EGFR-mutated lung adenocarcinoma (n = 296). In addition, we examined the expression level of E-cadherin in tumor cells and counted the number of tumor-infiltrating lymphocytes (TILs; CD8, CD20, CD138, and Foxp3), tumor-associated macrophages (TAMs; CD204), and cancer-associated fibroblasts (CAFs; podoplanin) using semi-automatic digital pathology image analysis. ResultsRecurrence-free survival (RFS) curve showed that survival of grade 3 was significantly shorter than that of grade 1 (P < 0.01) and grade 2 (P = 0.03). Multivariate analysis of RFS revealed the invasive size, lymphatic permeation, and grade 3 (P < 0.01) as independent poor prognostic factors. The number of CD204 +TAMs and PDPN+CAFs was significantly higher in grade 3 than in grade 1 or 2 (all P < 0.01). Among the intermediate grade by the predominant subtype based classification, cases classified as grade 3 by the new classification had higher number of CD204 +TAMs (P < 0.01) and PDPN+CAFs (P = 0.02) than those classified as grade 2. ConclusionThe IASLC grading system correlated with the outcomes of EGFR-mutated lung adenocarcinoma. Grade 3 was found to have the TME that most contributes to tumor progression, which probably explained their poor prognosis.

Identifying diversity of patient anatomy through automated image analysis of clinical ultrasounds.

Central venous catheterization (CVC) carries inherent risks which can be mitigated through the use of appropriate ultrasound-guidance during needle insertion. This study aims to comprehensively understand patient anatomy as it is visualized during CVC by employing a semi-automated image analysis method to track the internal jugular vein and carotid artery throughout recorded ultrasound videos. The ultrasound visualization of 50 CVC procedures were recorded at Penn State Health Milton S. Hershey Medical Center. The developed algorithm was used to detect the vessel edges, calculating metrics such as area, position, and eccentricity. Results show typical anatomical variations of the vein and artery, with the artery being more circular and posterior to the vein in most cases. Notably, two cases revealed atypical artery positions, emphasizing the algorithm's precision in detecting anomalies. Additionally, dynamic vessel properties were analyzed, with the vein compressing on average to 13.4%of its original size and the artery expanding by 13.2%. This study provides valuable insights which can be used to increase the accuracy of training simulations, thus enhancing medical education and procedural expertise. Furthermore, the novel approach of employing automated data analysis techniques to clinical recordings showcases the potential for continual assessment of patient anatomy, which could be useful in future advancements.

Label-free imaging diagnosis and collagen-optical evaluation of endometrioid adenocarcinoma with multiphoton microscopy.

The assessment of tumor grade and pathological stage plays a pivotal role in determining the treatment strategy and predicting the prognosis of endometrial cancer. In this study, we employed multiphoton microscopy (MPM) to establish distinctive optical pathological signatures specific to endometrioid adenocarcinoma (EAC), while also assessing the diagnostic sensitivity, specificity, and accuracy of MPM for this particular malignancy. The MPM technique exhibits robust capability in discriminating between benign hyperplasia and various grades of cancer tissue, with statistically significant differences observed in nucleocytoplasmic ratio and second harmonic generation/two-photon excited fluorescence intensity. Moreover, by utilizing semi-automated image analysis, we identified notable disparities in six collagen signatures between benign and malignant endometrial stroma. Our study demonstrates that MPM can differentiate between benign endometrial hyperplasia and EAC without labels, while also quantitatively assessing changes in the tumor microenvironment by analyzing collagen signatures in the endometrial stromal tissue.

Nile red staining for rapid screening of plastic-suspect particles in edible seafood tissues

Concerns regarding microplastic (MP) contamination in aquatic ecosystems and its impact on seafood require a better understanding of human dietary MP exposure including extensive monitoring. While conventional techniques for MP analysis like infrared or Raman microspectroscopy provide detailed particle information, they are limited by low sample throughput, particularly when dealing with high particle numbers in seafood due to matrix-related residues. Consequently, more rapid techniques need to be developed to meet the requirements of large-scale monitoring. This study focused on semi-automated fluorescence imaging analysis after Nile red staining for rapid MP screening in seafood. By implementing RGB-based fluorescence threshold values, the need for high operator expertise to prevent misclassification was addressed. Food-relevant MP was identified with over 95% probability and differentiated from natural polymers with a 1% error rate. Comparison with laser direct infrared imaging (LDIR), a state-of-the-art method for rapid MP analysis, showed similar particle counts, indicating plausible results. However, highly variable recovery rates attributed to inhomogeneous particle spiking experiments highlight the need for future development of certified reference material including sample preparation. The proposed method demonstrated suitability of high throughput analysis for seafood samples, requiring 0.02–0.06 h/cm2 filter surface compared to 4.5–14.7 h/cm with LDIR analysis. Overall, the method holds promise as a screening tool for more accurate yet resource-intensive MP analysis methods such as spectroscopic or thermoanalytical techniques.Graphical

Comparison of the Visual Scoring Method and Semi-Automatic Image Analysis for Evaluating Staining Intensity of Human Cartilage Sections

Comparison of the Visual Scoring Method and Semi-Automatic Image Analysis for Evaluating Staining Intensity of Human Cartilage Sections

A versatile, semi-automated image analysis workflow for time-lapse camera trap image classification

Camera traps are a powerful, practical, and non-invasive method used widely to monitor animal communities and evaluate management actions. However, camera trap arrays can generate thousands to millions of images that require significant time and effort to review. Computer vision has emerged as a tool to accelerate this image review process. We propose a multi-step, semi-automated workflow which takes advantage of site-specific and generalizable models to improve detections and consists of (1) automatically identifying and removing low-quality images in parallel with classification into animals, humans, vehicles, and empty, (2) automatically cropping objects from images and classifying them (rock, bait, empty, and species), and (3) manually inspecting a subset of images. We trained and evaluated this approach using 548,627 images from 46 cameras in two regions of the Arctic: “Finnmark” (Finnmark County, Norway) and “Yamal” (Yamalo-Nenets Autonomous District, Russia). The automated steps yield image classification accuracies of 92% and 90% for the Finnmark and Yamal sets, respectively, reducing the number of images that required manual inspection to 9.2% of the Finnmark set and 3.9% of the Yamal set. The amount of time invested in developing models would be offset by the time saved from automation after 960 thousand images have been processed. Researchers can modify this multi-step process to develop their own site-specific models and meet other needs for monitoring and surveying wildlife, balancing the acceptable levels of false negatives and positives.

A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement.

Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.

Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation

BackgroundMicroorganisms must respond to changes in their environment. Analysing the robustness of functions (i.e. performance stability) to such dynamic perturbations is of great interest in both laboratory and industrial settings. Recently, a quantification method capable of assessing the robustness of various functions, such as specific growth rate or product yield, across different conditions, time frames, and populations has been developed for microorganisms grown in a 96-well plate. In micro-titer-plates, environmental change is slow and undefined. Dynamic microfluidic single-cell cultivation (dMSCC) enables the precise maintenance and manipulation of microenvironments, while tracking single cells over time using live-cell imaging. Here, we combined dMSCC and a robustness quantification method to a pipeline for assessing performance stability to changes occurring within seconds or minutes.ResultsSaccharomyces cerevisiae CEN.PK113-7D, harbouring a biosensor for intracellular ATP levels, was exposed to glucose feast-starvation cycles, with each condition lasting from 1.5 to 48 min over a 20 h period. A semi-automated image and data analysis pipeline was developed and applied to assess the performance and robustness of various functions at population, subpopulation, and single-cell resolution. We observed a decrease in specific growth rate but an increase in intracellular ATP levels with longer oscillation intervals. Cells subjected to 48 min oscillations exhibited the highest average ATP content, but the lowest stability over time and the highest heterogeneity within the population.ConclusionThe proposed pipeline enabled the investigation of function stability in dynamic environments, both over time and within populations. The strategy allows for parallelisation and automation, and is easily adaptable to new organisms, biosensors, cultivation conditions, and oscillation frequencies. Insights on the microbial response to changing environments will guide strain development and bioprocess optimisation.Graphical

Carotid plaque characteristics in the CREST-2 trial

BackgroundThe modest stroke prevention from surgery for asymptomatic carotid disease has prompted a search for predictors that may improve risk stratification beyond luminal stenosis. Plaque disruption and atheroembolization are associated with unique anatomical and histological changes. The Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial (CREST-2) collects information on duplex ultrasound (DUS) plaque biomarkers with the goal of evaluating their relationship to the periprocedural and long-term risks of stroke. In this study, we examine the reliability with which carotid plaque features can be measured from DUS images, and report baseline carotid DUS-derived stenosis and plaque features using semiautomated digital image analysis of patients enrolled in CREST-2. MethodsWe studied the first 503 patients in CREST-2. Patients underwent standardized carotid DUS evaluation pre-enrollment. B-Mode images were scaled linearly to normalize brightness. Plaques were outlined manually. Dedicated software automatically measured the longitudinal sectional area (mm2), grayscale median (GSM), Gray-Weale classification, and tissue composition (mm2) of intraplaque hemorrhage (IPH), lipid, fibrous tissue, muscle, and calcium. We present the mean, standard deviation, median, interquartile range, minimum and maximum range, and proportions of carotid peak systolic velocities (PSVs) and plaque morphological features. We tested for autocorrelation among plaque features and computed the proportion of potentially unstable plaques in the cohort. Reliability of the image analysis techniques was tested in 100 patients using Bland-Altman plots and intraclass and interclass correlation coefficients. ResultsMost patients were male (58.4%), older (mean age of 69.3 years), White (87.5%), and had a PSV of ≥230 cm/s (98.6%); the 1.4% with a PSV of <230 cm/s were enrolled based on catheter angiography. Plaques in this study were large; the mean longitudinal sectional area was 62 ± 37 mm2 (range, 6.2–256.5 mm2). The mean GSM was 58 ± 30 (unitless) (range, 0–168) and Gray-Weale classification was 3.5 ± 0.9 (range, 1–5). The mean areas of tissue types were IPH 5.3 ± 8.9 mm2, lipid 9.3 ± 8.6 mm2, fibrous tissue 10 ± 10 mm2, muscle 17 ± 12 mm2, and calcium 1.6 ± 4.1 mm2. The PSV of patients showed poor correlation with plaque features. The proportion of plaques with a GSM of ≤35 was 22.8%, IPH of ≥5 mm2 was 30.0%, and a lipid-rich necrotic core of ≥40% was 3.0% of the cohort. Plaque measurements could be performed with high reliability with good interobserver and intraobserver correlations. ConclusionsSite-generated, core laboratory-interpreted ultrasound examination provides a reliable way of characterizing carotid plaque morphological features across studies performed at many CREST-2 sites. The initial cohort of patients randomized in CREST-2 had heterogeneity of plaque features despite causing high-grade stenosis. Completion of the trial will provide an opportunity to assess whether plaque heterogeneity interacts with response to revascularization and medical management.

The modified Polsby-Popper score, a novel quantitative histomorphological biomarker and its potential to predict lymph node positivity and cancer-specific survival in oral tongue squamous cell carcinoma.

The significance of different histological spreading patterns of tumor tissue in oral tongue squamous cell carcinoma (TSCC) is well known. Our aim was to construct a numeric parameter on a continuous scale, that is, the modified Polsby-Popper (MPP) score, to describe the aggressiveness of tumor growth and infiltration, with the potential to analyze hematoxylin and eosin-stained whole slide images (WSIs) in an automated manner. We investigated the application of the MPP score in predicting survival and cervical lymph node metastases as well as in determining patients at risk in the context of different surgical margin scenarios. We developed a semiautomated image analysis pipeline to detect areas belonging to the tumor tissue compartment. Perimeter and area measurements of all detected tissue regions were derived, and a specific mathematical formula was applied to reflect the perimeter/area ratio in a comparable, observer-independent manner across digitized WSIs. We demonstrated the plausibility of the MPP score by correlating it with well-established clinicopathologic parameters. We then performed survival analysis to assess the relevance of the MPP score, with an emphasis on different surgical margin scenarios. Machine learning models were developed to assess the relevance of the MPP score in predicting survival and occult cervical nodal metastases. The MPP score was associated with unfavorable tumor growth and infiltration patterns, the presence of lymph node metastases, the extracapsular spread of tumor cells, and higher tumor thickness. Higher MPP scores were associated with worse overall survival (OS) and tongue carcinoma-specific survival (TCSS), both when assessing all pT-categories and pT1-pT2 categories only; moreover, higher MPP scores were associated with a significantly worse TCSS in cases where a cancer-free surgical margin of <5 mm could be achieved on the main surgical specimen. This discriminatory capacity remained constant when examining pT1-pT2 categories only. Importantly, the MPP score could successfully define cases at risk in terms of metastatic disease in pT1-pT2 cancer where tumor thickness failed to exhibit a significant predictive value. Machine learning (ML) models incorporating the MPP score could predict the 5-year TCSS efficiently. Furthermore, we demonstrated that machine learning models that predict occult cervical lymph node involvement can benefit from including the MPP score. We introduced an objective, quantifiable, and observer-independent parameter, the MPP score, representing the aggressiveness of tumor growth and infiltration in TSCC. We showed its prognostic relevance especially in pT1-pT2 category TSCC, and its possible use in ML models predicting TCSS and occult lymph node metastases.

Evaluation of Meibomian gland structure and appearance after therapeutic Meibomian gland expression

ABSTRACT Clinical Relevance Evaluating how Meibomian glands can change in appearance has the potential to advance the understanding of Meibomian gland health and may lead to enhanced diagnosis and therapy. Background This work aimed to investigate Meibomian gland appearance after therapeutic Meibomian gland expression. Methods Fifteen subjects attended three study visits over a two-week period. Meibography was performed before and after therapeutic Meibomian gland expression, the following day, and 2 weeks after expression. Six central glands were used to calculate Meibomian gland morphological parameters such as gland length ratio, gland width, gland area, gland tortuosity, and gland contrast. A custom semi-automated image analysis software was used to calculate Meibomian gland metrics. Furthermore, a high-resolution imaging system was developed to capture clear images of the Meibomian glands, free of any artefacts, which were used for precise calculations of Meibomian gland contrast. Results The expression procedure had a significant impact on Meibomian gland contrast and length ratio immediately afterwards. The least square mean difference (95% CI) from baseline for Michelson contrast was −0.006 (−0.010, −0.001) and −1.048 (−2.063, −0.033) for simple contrast. The least square mean ratio of the gland length ratio immediately after the expression to baseline was 0.758 (0.618, 0.931). Conclusions Following therapeutic expression, Meibomian glands exhibit reduced brightness and length. However, within 24 h, they appear to recover and return to their baseline state, indicating a relatively short recovery time. This sheds light on whether meibography is solely focused on capturing gland structure or if it also captures acinar activity. The hyperreflective properties of lipids suggest that the decrease in contrast observed after expression could be attributed to a reduction in the visualisation of acini activity. A decrease in Meibomian gland length ratio implies that the loss of gland structure following treatment may be indicative of a temporary structural alteration.

Asynchronous contributions of decapod life history stages to the zooplankton of tropical estuarine, coastal and shelf ecosystems - new insights from semi-automatic image analysis

Decapod crustaceans include many species of socioeconomic importance and are key components of pelagic ecosystems, both as adults and as larval forms. Knowledge on seasonal and spatial synchronicity of planktonic larvae in coastal tropical ecosystems is fundamental to understand the dynamics of these complex ecosystems. The objective of the present study was to quantify the contributions of decapods to the zooplankton in adjacent tropical estuarine, coastal and shelf ecosystems, while considering their seasonal life cycles and probable dispersal strategies. We evaluated the contributions of decapod biomass and biovolume in three distinct ecosystems: Rio Formoso estuary, Tamandaré bay, and Pernambuco continental shelf off Tamandaré, northeastern Brazil. Zooplankton samples were taken bimonthly from June 2013 to May 2015, with a 300 μm mesh net, and analyzed using a ZooScan equipment. Decapods were the second most important organisms (after copepods), in abundance, biomass and biovolume, in all sampling areas. Total decapods contributed on average with 33.6%, 4.4% and 7.1% relative abundance and 30.9%, 30.9%, and 15.2% relative biovolume in estuary, bay, and shelf areas, respectively. Overall, the most relevant decapod taxa and stages found in the three sampling areas were brachyuran crab zoeae and megalopae, penaeid shrimp postlarvae (mostly Penaeus spp.), holoplanktonic luciferids (adults, protozoeae and mysis), anomuran zoeae (mostly Paguridae and Diogenidae hermit crabs), pistol shrimp zoeae (Alpheidae), and porcelain crab zoeae (Porcellanidae). Brachyuran zoeae contributed with up to 81.3% abundance and up to 69% biovolume, in the estuary. Penaeid postlarvae comprised up to 28.1% of total abundance and up to 94.7% of total biovolume, on the shelf. These postlarvae were transported shorewards from the offshore shelf. Decapod contributions were especially relevant during massive larval release events of crab zoeae and during shoreward migration of pre-settlement stages. Seasonal peaks were clearly asynchronous between taxa and areas. Possible functional relationships between copepods and key decapod groups are discussed, as well as the processes triggering and regulating the input of larvae. Our results show the importance of quantitative, semi-automatic approaches and the relevance of decapod larvae for pelagic food webs in tropical coastal areas.

AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.

KairoSight-3.0: A validated optical mapping software to characterize cardiac electrophysiology, excitation-contraction coupling, and alternans.

Cardiac optical mapping is an imaging technique that measures fluorescent signals across a cardiac preparation. Dual optical imaging of voltage-sensitive and calcium-sensitive probes allows for simultaneous recordings of cardiac action potentials and intracellular calcium transients with high spatiotemporal resolution. The analysis of these complex optical datasets is both time intensive and technically challenging; as such, we have developed a software package for semi-automated image processing and analysis. Herein, we report an updated version of our software package (KairoSight-3.0) with features to enhance the characterization of cardiac parameters using optical signals. To test software validity and applicability, we used Langendorff-perfused heart preparations to record transmembrane voltage and intracellular calcium signals from the epicardial surface. Isolated hearts from guinea pigs and rats were loaded with a potentiometric dye (RH237) and/or calcium indicator dye (Rhod-2AM) and fluorescent signals were acquired. We used Python 3.8.5 programming language to develop the KairoSight-3.0 software. Cardiac maps were validated with a user-specified manual mapping approach. Manual maps of action potential duration (30 or 80 % repolarization), calcium transient duration (30 or 80 % reuptake), action potential and calcium transient alternans were constituted to validate the accuracy of software-generated maps. Manual and software maps had high accuracy, with >97 % of manual and software values falling within 10 ms of each other and >75 % within 5 ms for action potential duration and calcium transient duration measurements (n = 1000-2000 pixels). Further, our software package includes additional measurement tools to analyze signal-to-noise ratio, conduction velocity, action potential and calcium transient alternans, and action potential-calcium transient coupling time to produce physiologically meaningful optical maps. KairoSight-3.0 has enhanced capabilities to perform measurements of cardiac electrophysiology, calcium handling, alternans, and the excitation-contraction coupling with satisfactory accuracy.

The impact of dry eye disease on corneal nerve parameters: A systematic review and meta-analysis.

Dry eye disease (DED) is a growing global health problem with a significant impact on the quality of life of patients. While neurosensory abnormalities have been recognised as a contributor to DED pathophysiology, the potential role of in vivo corneal confocal microscopy in detecting nerve loss or damage remains unclear. This systematic review with meta-analysis (PROSPERO registered CRD42022381861) investigated whether DED has an impact on sub-basal corneal nerve parameters. PubMed, Embase and Web of Science Core Collection databases were searched from inception to 9 December 2022. Studies using laser scanning confocal microscopy to compare corneal nerve parameters of DED with healthy eyes were included. Study selection process and data extraction were performed by two independent members of the review team. Twenty-two studies with 916 participants with DED and 491 healthy controls were included, with 21 of these studies included in subsequent meta-analyses. There was a decrease in total corneal nerve length (-3.85 mm/mm2 ; 95% CI -5.16, -2.55), corneal main nerve trunk density (-4.81 number/mm2 ; 95% CI -7.94, -1.68) and corneal nerve branch density (-15.52 number/mm2 ; 95% CI -27.20, -3.84) in DED eyes compared with healthy eyes, with subgroup analysis demonstrating that these differences were more evident in studies using NeuronJ software, a semi-automated procedure. While this review found evidence of loss of corneal nerve parameters in eyes with DED compared with healthy controls, particularly with the use of a semi-automated image analysis method, it is evident that there is substantial heterogeneity between studies in terms of corneal nerve imaging methodology. Standardisation is required in terms of terminology and analysis, with more research needed to potentially improve the clinical applicability and practicality of corneal nerve imaging. Further investigation is also required to confirm the diagnostic accuracy of this imaging modality and its potential for monitoring DED treatment efficacy.

Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.