A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods.

Accurate quantification of amyloid positron emission tomography (PET) is essential for early detection of and intervention in Alzheimer's disease (AD) but there is still a lack of studies comparing the performance of various automated methods. This study compared the PET-only method and PET-and-MRI-based method with a pre-trained deep learning segmentation model. A large sample of 1180 participants in the Catholic Aging Brain Imaging (CABI) database was analyzed to calculate the regional standardized uptake value ratio (SUVR) using both methods. The logistic regression models were employed to assess the discriminability of amyloid-positive and negative groups through 10-fold cross-validation and area under the receiver operating characteristics (AUROC) metrics. The two methods showed a high correlation in calculating SUVRs but the PET-MRI method, incorporating MRI data for anatomical accuracy, demonstrated superior performance in predicting amyloid-positivity. The parietal, frontal, and cingulate importantly contributed to the prediction. The PET-MRI method with a pre-trained deep learning model approach provides an efficient and precise method for earlier diagnosis and intervention in the AD continuum.

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates.

INTRODUCTION: The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy. OBJECTIVES: To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals. METHODS: An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix. RESULTS: The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated. CONCLUSIONS: The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

Semi-automated quantification of vitreal hyperreflective foci in SD-OCT and their relevance in patients with peripheral retinal breaks.

BACKGROUND: Retinal breaks (RB) are emergencies that require treatment to prevent progression of rhegmatogenous retinal detachment. Vitreal hyperreflective foci (VHF) representing migration of RPE cell clusters or interphotoreceptor matrix from the RB are potential biomarkers. The aim of this study is to investigate VHF in RB-patients using SD-OCT. METHODS: The retrospective cross-sectional study included RB patients from our Department of Ophthalmology, HSK Wiesbaden who underwent macular SD-OCT (SPECTRALIS®, Heidelberg Engineering, Germany) on both eyes. VHF, defined and quantified as foci that differ markedly in size and reflectivity from the background speckle pattern, were assessed for presence and frequency. The RB-affected eyes were the study group (G1), the partner eyes the control group (G2). RESULTS: 160 consecutive patients with RB were included. Age was 60 ± 10.2 years (52% female). 89.4% of G1 and 87.5% of G2 were phakic (p = 0.73). 94.4% (n = 151) were symptomatic. Symptom duration was 8.0 ± 10.1 days in G1, 94.4% (n = 151) showed VHF versus 5.6% (p < 0.0001) in G2, of which 75% (n = 6) showed asymptomatic lattice degenerations. Detectable VHF showed a strong association of OR = 320 (95% CI, 110-788, p < 0.0001)) with respect to symptomatic RB. Sensitivity and specificity were 94.7% and 94.7%, respectively. CONCLUSIONS: Most eyes with symptomatic RB show vitreal VHF in SD-OCT. Detected VHF are strongly associated with RB, and our semi-automated greyscale reflectivity analysis indicates that VHF likely originate from photoreceptor complexes torn out of the RB area that migrate into the vitreous cavity. The presence of VHF may indicate RB and should lead to a thorough fundus examination in both symptomatic and asymptomatic cases.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®.

OBJECTIVES: QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. METHODS: Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. RESULTS: The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. CONCLUSIONS: QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. KEY POINTS: • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry.

BACKGROUND: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment. METHODS: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared. RESULTS: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003). CONCLUSIONS: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity.

Effects of emphysema on physiological and prognostic characteristics of lung function in idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: Combined pulmonary fibrosis and emphysema (CPFE) is characterized by preserved lung volume and slower lung function decline. However, it is unclear at what extent emphysema begins to impact respiratory physiology and prognostic characteristics in idiopathic pulmonary fibrosis (IPF). We estimated the extent of emphysema that could be used to define CPFE in IPF. METHODS: The extent of emphysema was observed on high-resolution computed tomography scans and measured by a texture-based automated quantification system in 209 IPF patients. We analysed the impact of differences in the extent of emphysema on the annual decline rate and prognostic significance of lung function parameters. RESULTS: The extent of emphysema was ≥5% in 53 patients (25%), ≥10% in 23 patients (11%) and ≥15% in 12 patients (6%). Patients with emphysema to an extent of ≥5% were more frequently men and ever-smokers; they had more preserved lung volume and lower forced vital capacity (FVC) decline rates than those with no or trivial emphysema. The FVC decline rate was a significant predictor of mortality in patients with no or trivial emphysema (hazard ratio (HR): 0.933, P < 0.001) and in patients with an extent of emphysema ≥5% (HR: 0.906, P < 0.001). However, diffusing capacity of the lung for carbon monoxide (DLCO ) was the most significant prognostic factor in those patients with an extent of emphysema ≥10% (HR: 0.972, P = 0.040) and ≥15% (HR: 0.942, P = 0.023). A 10% cut-off value for the extent of emphysema created the most significant difference in the annual FVC decline rate in IPF patients. CONCLUSION: In IPF, emphysema to an extent of ≥10% affects both the annual decline rate and the prognostic significance of FVC. This extent could be used to define CPFE.

Automated qualitative and quantitative assessment of posterior capsule opacification by Automated Quantification of After-Cataract II (AQUA II) system.

BACKGROUND: The present study aims to investigate an automated qualitative and quantitative assessment system (Automated Quantification of After-Cataract [AQUA II]) of posterior capsule opacification (PCO) in high-resolution digital retroillumination images and consequently reduce observer bias and increase accuracy of PCO grading. METHODS: A data set of 100 eyes with no to severe PCO was analysed. Ten eyes were consecutively photographed twice and ten images were rotated to give a total of 120 images for PCO assessment. Validity was determined by including subjective grading and repeatability was determined by evaluating the 20 additional images. Evaluation of posterior capsular opacification (EPCO), posterior capsule opacity (POCO) and AQUA I methods were included for comparative analysis of the data. RESULTS: The system developed proved to classify six types of PCO. Validity was confirmed by a Pearson correlation coefficient of r = 0.95 (EPCO r = 0.93; POCO r = 0.72 and AQUA I r = 0.94). Repeatability was better in AQUA II (95% confidence interval [CI] for mean difference: 0.5 ± 1.2) than in subjective grading (95% CI for mean difference: 0.6 ± 1.7), in EPCO grading (95% CI for mean difference: - 0.2 ± 1.5), in POCO grading (95% CI for mean difference: 1.6 ± 2.7) and in AQUA I (95% CI for mean difference: - 1.1 ± 1.9). CONCLUSIONS: AQUA II is a system that for the first time not only objectively quantifies PCO, but also qualitatively assesses PCO in an automated manner with texture classification. AQUA II showed an excellent validity and repeatability.

In vitro myelin formation using embryonic stem cells.

Myelination in the central nervous system is the process by which oligodendrocytes form myelin sheaths around the axons of neurons. Myelination enables neurons to transmit information more quickly and more efficiently and allows for more complex brain functions; yet, remarkably, the underlying mechanism by which myelination occurs is still not fully understood. A reliable in vitro assay is essential to dissect oligodendrocyte and myelin biology. Hence, we developed a protocol to generate myelinating oligodendrocytes from mouse embryonic stem cells and established a myelin formation assay with embryonic stem cell-derived neurons in microfluidic devices. Myelin formation was quantified using a custom semi-automated method that is suitable for larger scale analysis. Finally, early myelination was followed in real time over several days and the results have led us to propose a new model for myelin formation.

AxonTracer: a novel ImageJ plugin for automated quantification of axon regeneration in spinal cord tissue.

BACKGROUND: Quantification of axon regeneration in spinal cord tissue sections is a fundamental step to adequately determine if an applied treatment leads to an anatomical benefit following spinal cord injury. Recent advances have led to the development of therapies that can promote regeneration of thousands of injured axons in vivo. Axon labeling methods and in the application of regeneration-enabling stem cell grafts have increased the number of detectable regenerating axons by orders of magnitudes. Manual axon tracing in such cases is challenging and laborious, and as such there is a great need for automated algorithms that can perform accurate tracing and quantification in axon-dense tissue sections. RESULTS: We developed "AxonTracer", a fully automated software algorithm that traces and quantifies regenerating axons in spinal cord tissue sections. AxonTracer is an open source plugin for the freely available image-processing program ImageJ. The plugin identifies transplanted cells grafts or other regions of interest (ROIs) based on immunohistological staining and quantifies regenerating axons within the ROIs. Individual images or groups of images (batch mode) can be analyzed sequentially. In batch mode, a unique algorithm identifies a reference image for normalization, as well as a suitable image for defining detection parameters. An interactive user interface allows for adjustment of parameters defining ROI size, axon detection sensitivity and debris cleanup. Automated quantification of regenerating axons by AxonTracer correlates strongly with semi-manual quantification by the widely-used ImageJ plugin NeuronJ. However, quantification with AxonTracer is automated and reduces the need for user input compared to alternative methods. CONCLUSIONS: AxonTracer is a freely available open-source tool for automated analysis of regenerating axons in the injured nervous system. An interactive user interface provides detection-parameter adjustment, and usage does not require prior image analysis experience. Raw data as well as normalized results are stored in spreadsheet format and axon tracings are superimposed on raw images allowing for subjective visual verification. This software allows for automated, unbiased analysis of hundreds of axon-dense images, thus providing a useful tool in enabling in vivo screens of axon regeneration following spinal cord injury.

Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy.

BACKGROUND: Hard exudates (HEs) are the classical sign of diabetic retinopathy (DR) which is one of the leading causes of blindness, especially in developing countries. Accordingly, disease screening involves examining HEs qualitatively using fundus camera. However, for monitoring the treatment response, quantification of HEs becomes crucial and hence clinicians now seek to measure the area of HEs in the digital colour fundus (CF) photographs. Against this backdrop, we proposed an algorithm to quantify HEs using CF images and compare with previously reported technique using ImageJ. METHODS: CF photographs of 30 eyes (20 patients) with diabetic macular edema were obtained. A robust semi-automated algorithm was developed to quantify area covered by HEs. In particular, the proposed algorithm, a two pronged methodology, involved performing top-hat filtering, second order statistical filtering, and thresholding of the colour fundus images. Subsequently, two masked observers performed HEs measurements using previously reported ImageJ-based protocol and compared with those obtained through proposed method. Intra and inter-observer grading was performed for determining percentage area of HEs identified by the individual algorithm. RESULTS: Of the 30 subjects, 21 were males and 9 were females with a mean age of the 50.25 ± 7.80 years (range 33-66 years). The correlation between the two measurements of semi-automated and ImageJ were 0.99 and 0.99 respectively. Previously reported method detected only 0-30% of the HEs area in 9 images, 30-60% in 12 images and 60-90% in remaining images, and more than 90% in none. In contrast, proposed method, detected 60-90% of the HEs area in 13 images and 90-100% in remaining 17 images. CONCLUSION: Proposed method semi-automated algorithm achieved acceptable accuracy, qualitatively and quantitatively, on a heterogeneous dataset. Further, quantitative analysis performed based on intra- and inter-observer grading showed that proposed methodology detects HEs more accurately than previously reported ImageJ-based technique. In particular, we proposed algorithm detect faint HEs also as opposed to the earlier method.

Three-dimensional echocardiographic measurements using automated quantification software for big data processing.

OBJECTIVE: To compare a full-automated software to quantify 3D transthoracic echocardiography namely, 3DE-HM (three-dimensional echocardiography HeartModel, Philips Healthcare) with the traditional manual quantitative method (3DE-manual) for assessing volumes of left atrial and ventricular volumes, and left ventricular ejection fraction (LVEF). METHODS: 3D full volume images acquired from 156 subjects were collected and divided into 3 groups, which include 70 normal control cases (Group A), 17 patients with left ventricular remodeling after acute myocardial infarction (AMI) (Group B), and 69 patients with left atrial remodeling secondary to hypertension (Group C). The 3DE-HM method was used to quantify left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left atrial end-systolic volume (LAESV), and left ventricular ejection fraction (LVEF), respectively. The results were compared with those obtained with the 3DE-manual method for correlation and consistency analyses. The reproducibility of the 3DE-HM method was also evaluated. RESULTS: There was a high correlation between LVEDV, LVESV, LAESV and LVEF values obtained with the 3DE-HM method and those obtained using the 3DE-manual method (r = 0.72 to 0.97). The correlation was strongest for Group B, patients with left ventricular remodeling post-AMI also demonstrated the greatest degree of morphologic changes. There was a significant difference in all parameters measured with the 3DE-HM method in different groups (P < 0.05). The difference in the measurements of LVEDV and LVESV between the two methods was greatest in patients in Group B compared with patients with hypertension-induced left ventricular remodeling (Group C) and in normal controls (Group A) (P < 0.05). Lastly, the difference in the measurement of LAESV between the two methods was greater in patients with hypertension-induced left ventricular remodeling (Group C) than that in the control group (Group A) (P < 0.05). The post-processing time of the 3DE-HM data was significantly shorter than that using the 3DE-manual method (P < 0.05). There was no significant variability in repeated measurements at different time points using the 3DE-HM method either between subjects in different groups or within the same subject. CONCLUSION: 3DE-HM is a quick and feasible method for left ventricular quantification and is clinically applicable for evaluating patients with left atrial and left ventricular remodeling.

A semi-automated method to assess intraepidermal nerve fibre density in human skin biopsies.

AIMS: Evaluation of intraepidermal nerve fibres (IENFs) in skin biopsies is used in the diagnosis of small-fibre neuropathies. The number of IENFs is assessed manually under a microscope, with an inter-rater variability of ~25%. Unless the images are digitized, there is no documentation. Our aim was to develop a method for standardized semi-automated quantification (SAQ) and documentation of IENF density. METHODS AND RESULTS: We analysed samples from four different university centres that were immunostained according to local protocols. Images were acquired through the Z-plane with a whole slide scanner. orbit image analysis software was used to create an analysable image and develop a reliable algorithm for IENF detection. Rebuilt images revealed well-contrasted nerves, allowing detection of IENFs (automated). The software presented the detected nerves for confirmation by the operator (manual). As compared with the conventional microscopy count, the SAQ achieved correlation coefficients of 0.99 and 0.96 and interfacility variabilities of 19% and 23%, respectively. We found better reproducibility with fluorescence-stained specimens than with bright-field images. CONCLUSIONS: The new semi-automated method has high experimenter-independent reproducibility when based on nerve detection by fluorescence and is easy to perform, even by untrained users. The IENF counting is electronically well documented.

An open-source computational tool to automatically quantify immunolabeled retinal ganglion cells.

A fully automated and robust method was developed to quantify ß-III-tubulin-stained retinal ganglion cells, combining computational recognition of individual cells by CellProfiler and a machine-learning tool to teach phenotypic classification of the retinal ganglion cells by CellProfiler Analyst. In animal models of glaucoma, quantification of immunolabeled retinal ganglion cells is currently performed manually and remains time-consuming. Using this automated method, quantifications of retinal ganglion cell images were accelerated tenfold: 1800 images were counted in 3 h using our automated method, while manual counting of the same images took 72 h. This new method was validated in an established murine model of microbead-induced optic neuropathy. The use of the publicly available software and the method's user-friendly design allows this technique to be easily implemented in any laboratory.

A freely available semi-automated method for quantifying retinal ganglion cells in entire retinal flatmounts.

Glaucomatous optic neuropathies are characterized by progressive loss of retinal ganglion cells (RGCs), the neurons that connect the eye to the brain. Quantification of these RGCs is a cornerstone in experimental optic neuropathy research and commonly performed via manually quantifying parts of the retina. However, this is a time-consuming process subject to inter- and intra-observer variability. Here we present a freely available ImageJ script to semi-automatically quantify RGCs in entire retinal flatmounts after immunostaining for the RGC-specific transcription factor Brn3a. The blob-like signal of Brn3a-immunopositive RGCs is enhanced via eigenvalues of the Hessian matrix and the resulting local maxima are counted as RGCs. After the user has outlined the retinal flatmount area, the total RGC number and retinal area are reported and an isodensity map, showing the RGC density distribution across the retina, is created. The semi-automated quantification shows a very strong correlation (Pearson's r ≥ 0.99) with manual counts for both widefield and confocal images, thereby validating the data generated via the developed script. Moreover, application of this method in established glaucomatous optic neuropathy models such as N-methyl-D-aspartate-induced excitotoxicity, optic nerve crush and laser-induced ocular hypertension revealed RGC loss conform with literature. Compared to manual counting, the described automated quantification method is faster and shows user-independent consistency. Furthermore, as the script detects the RGC number in entire retinal flatmounts, the method allows detection of regional differences in RGC density. As such, it can help advance research investigating the degenerative mechanisms of glaucomatous optic neuropathies and the effectiveness of new neuroprotective treatments. Because the script is flexible and easy to optimize due to a low number of critical parameters, it can potentially be applied in combination with other tissues or alternative labeling protocols.

Advances in Imaging and Automated Quantification of Pulmonary Diseases in Non-neoplastic Diseases.

Histological examination has always been the gold standard for the detection and quantification of lung remodeling. However, this method has some limitations regarding the invasiveness of tissue acquisition. Quantitative imaging methods enable the acquisition of valuable information on lung structure and function without the removal of tissue from the body; thus, they are useful for disease identification and follow-up. This article reviews the various quantitative imaging modalities used currently for the non-invasive study of chronic obstructive pulmonary disease, asthma, and interstitial lung diseases. Some promising computer-aided diagnosis methods are also described.

Assessment of retinal ganglion cell damage in glaucomatous optic neuropathy: Axon transport, injury and soma loss.

Glaucoma is a disease characterized by progressive axonal pathology and death of retinal ganglion cells (RGCs), which causes structural changes in the optic nerve head and irreversible vision loss. Several experimental models of glaucomatous optic neuropathy (GON) have been developed, primarily in non-human primates and, more recently and commonly, in rodents. These models provide important research tools to study the mechanisms underlying glaucomatous damage. Moreover, experimental GON provides the ability to quantify and monitor risk factors leading to RGC loss such as the level of intraocular pressure, axonal health and the RGC population. Using these experimental models we are able to gain a better understanding of GON, which allows for the development of potential neuroprotective strategies. Here we review the advantages and disadvantages of the relevant and most often utilized methods for evaluating axonal degeneration and RGC loss in GON. Axonal pathology in GON includes functional disruption of axonal transport (AT) and structural degeneration. Horseradish peroxidase (HRP), rhodamine-B-isothiocyanate (RITC) and cholera toxin-B (CTB) fluorescent conjugates have proven to be effective reporters of AT. Also, immunohistochemistry (IHC) for endogenous AT-associated proteins is often used as an indicator of AT function. Similarly, structural degeneration of axons in GON can be investigated via changes in the activity and expression of key axonal enzymes and structural proteins. Assessment of axonal degeneration can be measured by direct quantification of axons, qualitative grading, or a combination of both methods. RGC loss is the most frequently quantified variable in studies of experimental GON. Retrograde tracers can be used to quantify RGC populations in rodents via application to the superior colliculus (SC). In addition, in situ IHC for RGC-specific proteins is a common method of RGC quantification used in many studies. Recently, transgenic mouse models that express fluorescent proteins under the Thy-1 promoter have been examined for their potential to provide specific and selective labeling of RGCs for the study of GON. While these methods represent important advances in assessing the structural and functional integrity of RGCs, each has its advantages and disadvantages; together they provide an extensive toolbox for the study of GON.

Feasibility of the Threshold-Based Quantification of Myocardial Fibrosis on Cardiac CT as a Prognostic Marker in Nonischemic Dilated Cardiomyopathy.

OBJECTIVE: This study investigated the feasibility and prognostic relevance of threshold-based quantification of myocardial delayed enhancement (MDE) on CT in patients with nonischemic dilated cardiomyopathy (NIDCM). MATERIALS AND METHODS: Forty-three patients with NIDCM (59.3 ± 17.1 years; 21 male) were included in the study and underwent cardiac CT and MRI. MDE was quantified manually and with a threshold-based quantification method using cutoffs of 2, 3, and 4 standard deviations (SDs) on three sets of CT images (100 kVp, 120 kVp, and 70 keV). Interobserver agreement in MDE quantification was assessed using the intraclass correlation coefficient (ICC). Agreement between CT and MRI was evaluated using the Bland-Altman method and the concordance correlation coefficient (CCC). Patients were followed up for the subsequent occurrence of the primary composite outcome, including cardiac death, heart transplantation, heart failure hospitalization, or appropriate use of an implantable cardioverter-defibrillator. The Kaplan-Meier method was used to estimate event-free survival according to MDE levels. RESULTS: Late gadolinium enhancement (LGE) was observed in 29 patients (67%, 29/43), and the mean LGE found with the 5-SD threshold was 4.1% ± 3.6%. The 4-SD threshold on 70-keV CT showed excellent interobserver agreement (ICC = 0.810) and the highest concordance with MRI (CCC = 0.803). This method also yielded the smallest bias with the narrowest range of 95% limits of agreement compared to MRI (bias, -0.119%; 95% limits of agreement, -4.216% to 3.978%). During a median follow-up of 1625 days (interquartile range, 712-1430 days), 10 patients (23%, 10/43) experienced the primary composite outcome. Event-free survival significantly differed between risk subgroups divided by the optimal MDE cutoff of 4.3% (log-rank P = 0.005). CONCLUSION: The 4-SD threshold on 70-keV monochromatic CT yielded results comparable to those of MRI for quantifying MDE as a marker of myocardial fibrosis, which showed prognostic value in patients with NIDCM.

Innovations in Diabetic Macular Edema Management: A Comprehensive Review of Automated Quantification and Anti-vascular Endothelial Growth Factor Intervention.

Diabetic macular edema (DME) poses a significant threat to the vision and quality of life of individuals with diabetes. This comprehensive review explores recent advancements in DME management, focusing on integrating automated quantification techniques and anti-vascular endothelial growth factor (anti-VEGF) interventions. The review begins with an overview of DME, emphasizing its prevalence, impact on diabetic patients, and current challenges in management. It then delves into the potential of automated quantification, leveraging machine learning and artificial intelligence to improve early detection and monitoring. Concurrently, the role of anti-VEGF therapies in addressing the underlying vascular abnormalities in DME is scrutinized. The review synthesizes vital findings, highlighting the implications for the future of DME management. Promising outcomes from recent clinical trials and case studies are discussed, providing insights into the evolving landscape of personalized medicine approaches. The conclusion underscores the transformative potential of these innovations, calling for continued research, collaboration, and integration of these advancements into clinical practice. This review aims to serve as a roadmap for researchers, clinicians, and industry stakeholders, fostering a collective effort to enhance the precision and efficacy of DME management.

An Automated Quantification Tool for Angiogenic Sprouting From Endothelial Spheroids.

The process of sprouting angiogenesis can be measured in vitro using endothelial cells in sprouting assays such as the fibrin bead assay and the spheroid-based assay. While the technical aspects of these sprouting assays have been well-optimized, the analysis aspects have been limited to manual methods, which can be time-consuming and difficult to reproduce. Here, we developed an automated analysis tool called AQuTAS to quantify sprouting parameters from the spheroid-based sprouting assay. We trained and validated the algorithm on two subsets of data, and tested its sensitivity by measuring changes in sprouting parameters over a range of concentrations of pro- and antiangiogenic compounds. Our results demonstrate that the algorithm detects known differences in sprouting parameters in endothelial spheroids treated with pro- and antiangiogenic compounds. Moreover, it is sensitive to biological changes that are ≥40%. Among the five quantified parameters, cumulative sprout length is likely the most discriminative parameter for measuring differences in sprouting behavior because it had the highest effect size (>1.5 Cohen's d). In summary, we have generated an automated tool that quantifies sprouting parameters from the spheroid-based assay in a reproducible and sensitive manner.