Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium

BACKGROUND

The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care.

OBJECTIVES

This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date.

METHODS

CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals.

RESULTS

The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%).

CONCLUSIONS

This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.

Long-term monitoring of huddling behavior in mice using online image processing

Many animal species, including mice, form societies of numerous individuals for survival. Understanding the interactions between individual animals is crucial for elucidating group behavior. One such behavior in mice is huddling, yet its analysis has been limited. In this study, we propose a cost-effective method for monitoring long-term huddling behavior in mice using online image processing with OpenCV. This method treats a single mouse or a group of mice as a cluster of pixels (a 'blob') in video images, extracting and saving only essential information such as areas, coordinates, and orientations. This approach reduces data storage needs to 1/200000th of what would be required if the video were recorded in its compressed form, thereby enabling long-term behavioral analysis. To validate the performance of our algorithm, ~2000 video frames were randomly chosen. We manually counted the number of clusters of mice in these frames and compared them with the number of blobs automatically detected by the algorithm. The results indicated a high level of consistency, exceeding 90% across the selected video frames. Initial observations of both male and female groups suggested some variations in huddling behavior among male and female groups; however, these results should be interpreted cautiously due to a small sample. Group behavior is known to be disrupted in several neuropsychiatric disorders, such as autism. Various mouse models of these disorders have been proposed. Our measurement system, when combined with drug or genetic modification screening, could provide a valuable tool for high-throughput analyses of huddling behavior.

Contemporary diagnostic visual and automated polysomnographic REM sleep without atonia thresholds in isolated REM sleep behavior disorder

STUDY OBJECTIVES

Accurate diagnosis of isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is crucial due to its injury potential and neurological prognosis. We aimed to analyze visual and automated REM sleep without atonia (RSWA) diagnostic thresholds applicable in varying clinical presentations in a contemporary cohort of patients with iRBD using submentalis (SM) and individual bilateral flexor digitorum superficialis (FDS) and anterior tibialis electromyography limb recordings during polysomnography.

METHODS

We analyzed RSWA in 20 patients with iRBD and 20 age-, REM-, apnea-hypopnea index-matched controls between 2017 and 2022 for phasic burst durations, density of phasic, tonic, and "any" muscle activity (number of 3-second mini-epochs containing phasic or tonic muscle activity divided by the total number of REM sleep 3-second mini-epochs), and automated Ferri REM atonia index (RAI). Group RSWA metrics were comparatively analyzed. Receiver operating characteristic curves determined optimized area under the curve (AUC) and maximized specificity and sensitivity diagnostic iRBD RSWA thresholds.

RESULTS

All mean RSWA metrics were higher in patients with iRBD than in controls (P < .05), except for selected anterior tibialis measures. Optimized, maximal specificity AUC diagnostic cutoffs for coprimary outcomes were: SM "any" 6.5%, 14.0% (AUC = 92.5%) and combined SM+FDS "any" 15.1%, 27.4% (AUC = 95.8%), while SM burst durations were 0.72, and 0.72 seconds (AUC 90.2%) and FDS RAI = 0.930, 0.888 (AUC 92.8%).

CONCLUSIONS

This study provides evidence for current quantitative RSWA diagnostic thresholds in chin and individual 4 limb muscles applicable in different iRBD clinical settings and confirms the key value of SM or SM+FDS to assure accurate iRBD diagnosis. Evolving iRBD recognition underscores the necessity of continuous assessment with future large, prospective, well-harmonized, multicenter polysomnographic analyses.

CITATION

Leclair-Visonneau L, Feemster JC, Bibi N, et al. Contemporary diagnostic visual and automated polysomnographic REM sleep without atonia thresholds in isolated REM sleep behavior disorder. J Clin Sleep Med. 2024;20(2):279-291.

Automated quantitative evaluation of fetal atrioventricular annular plane systolic excursion before and after intrauterine blood transfusion in pregnancies affected by red blood cell alloimmunization

INTRODUCTION

Maternal red blood cell alloimmunization during pregnancy can lead to hemolysis and various degrees of fetal anemia, which can be treated with intrauterine blood transfusion (IUT) to prevent adverse outcomes. Knowledge about fetal myocardial function and adaptation is limited. The aim of the present study was to measure fetal atrioventricular plane displacement before and after IUT and compare these measurements with previously established reference ranges.

MATERIAL AND METHODS

An observational study was conducted on pregnant women affected by red blood cell alloimmunization. Fetal echocardiography was performed before and after IUT. The atrioventricular plane displacement of the left and right ventricular walls and interventricular septum, described as mitral, septal, and tricuspid annular plane systolic excursion (MAPSE, SAPSE, and TAPSE, respectively), was assessed using color tissue Doppler imaging with automated analysis software. A Mann-Whitney U test was used to compare the z scores to the normal mean before and after IUT.

RESULTS

Twenty-seven fetuses were included. The mean z score for pre-IUT MAPSE was significantly increased compared with the reference ranges, +0.46 (95% confidence interval [CI] +0.17 to +0.75; p = 0.039), while the mean z scores for post-IUT SAPSE and TAPSE were significantly decreased, -0.65 (95% CI -1.11 to -0.19; p < 0.001) and -0.60 (95% CI -1.04 to -0.17; p = 0.003), respectively. The difference in atrioventricular plane displacement z scores before and after IUT was statistically significant in all three locations. The median difference between the pre-IUT and post-IUT z scores was -0.66 (95% CI -1.03 to -0.33, p < 0.001) for MAPSE, -1.05 (95% CI -1.43 to -0.61, p < 0.001) for SAPSE, and -0.60 (95% CI -1.19 to -0.01, p = 0.046) for TAPSE.

CONCLUSIONS

This study suggests that atrioventricular plane displacement, when determined using automated analysis software, may represent a quantitative parameter, describing fetal myocardial function and adaptation before and after IUT.

Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.

AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation

Spinal-pelvic parameters are utilized in orthopedics for assessing patients' curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs. This study aims to develop an artificial intelligence (AI)-based deep learning approach for the automated segmentation, alignment, and measurement of spinal-pelvic parameters through whole-spine lateral radiographs. We conducted the study on 932 annotated images from various spinal pathologies. Using a deep learning (DL) model, anatomical landmarks of the cervical, thoracic, lumbar vertebrae, sacrum, and femoral head were automatically distinguished. The algorithm was designed to measure 13 radiographic alignment and spinal-pelvic parameters from the whole-spine lateral radiographs. Training data comprised 748 digital radiographic (DR) X-ray images, while 90 X-ray images were used for validation. Another set of 90 X-ray images served as the test set. Inter-rater reliability between orthopedic spine specialists, orthopedic residents, and the DL model was evaluated using the intraclass correlation coefficient (ICC). The segmentation accuracy for anatomical landmarks was within an acceptable range (median error: 1.7-4.1 mm). The inter-rater reliability between the proposed DL model and individual experts was fair to good for measurements of spinal curvature characteristics (all ICC values > 0.62). The developed DL model in this study demonstrated good levels of inter-rater reliability for predicting anatomical landmark positions and measuring radiographic alignment and spinal-pelvic parameters. Automated segmentation and analysis of whole-spine lateral radiographs using deep learning offers a promising tool to enhance accuracy and efficiency in orthopedic diagnostics and treatments.

Potential and challenges of high-speed (4D) body scanning for mobility analysis of firefighter clothing: a methodical case study

In this study, protective clothing for firefighters is analyzed using 4D body scanning and 3D hand scanning, with a focus on the experimental analysis of ergonomic comfort. In particular, German firefighting clothing is examined to discuss the possibilities and limitations of current scanning technologies for capturing firefighting clothing. For this purpose, various movements are recorded in the 4D scanner. In addition, a method for determining position changes of protective clothing at identified limits is presented. The initial results illustrated that the analysis of protective clothing for firefighters using 4D scanning is problematic due to specific materials, reflections, and surface properties. Improvements in the scanning process and optimization of algorithms are required to achieve more detailed and precise results. Concerning the ergonomic comfort related to the mobility under firefighting clothing use conditions, this methodical case study highlights the limits of current approaches, with a focus on the limitations of 4D scanning and potential improvements.

Comparison of category and letter fluency tasks through automated analysis

Introduction

Category and letter fluency tasks are commonly used neuropsychological tasks to evaluate lexical retrieval.

Methods

This study used validated automated methods, which allow for more expansive investigation, to analyze speech production of both category ("Animal") and letter ("F") fluency tasks produced by healthy participants (n = 36) on an online platform. Recordings were transcribed and analyzed through automated pipelines, which utilized natural language processing and automatic acoustic processing tools. Automated pipelines calculated overall performance scores, mean inter-word response time, and word start time; errors were excluded from analysis. Each word was rated for age of acquisition (AoA), ambiguity, concreteness, frequency, familiarity, word length, word duration, and phonetic and semantic distance from its previous word.

Results

Participants produced significantly more words on the category fluency task relative to the letter fluency task (p < 0.001), which is in line with previous studies. Wilcoxon tests also showed tasks differed on several mean speech measures of words, and category fluency was associated with lower mean AoA (p<0.001), lower frequency (p < 0.001), lower semantic ambiguity (p < 0.001), lower semantic distance (p < 0.001), lower mean inter-word RT (p = 0.03), higher concreteness (p < 0.001), and higher familiarity (p = 0.02), compared to letter fluency. ANOVAs significant interactions for fluency task on total score and lexical measures showed that lower category fluency scores were significantly related to lower AoA and higher prevalence, and this was not observed for letter fluency scores. Finally, word-characteristics changed over time and significant interactions were noted between the tasks, including word familiarity (p = 0.019), semantic ambiguity (p = 0.002), semantic distance (p=0.001), and word duration (p<0.001).

Discussion

These findings showed that certain lexical measures such as AoA, word familiarity, and semantic ambiguity were important for understanding how these tasks differ. Additionally, it found that acoustic measures such as inter-word RT and word duration are also imperative to analyze when comparing the two tasks. By implementing these automated techniques, which are reproducible and scalable, to analyze fluency tasks we were able to quickly detect these differences. In future clinical settings, we expect these methods to expand our knowledge on speech feature differences that impact not only total scores, but many other speech measures among clinical populations.

Long-Term Prognostic Value of Automated Measurements in Nuclear Cardiology: Comparisons with Expert Scoring

Background and Objectives: Automated methods for the analysis of myocardial perfusion studies have been incorporated into clinical practice, but they are currently used as adjuncts to the visual interpretation. We aimed to investigate the role of automated measurements of summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) as long-term prognostic markers of morbidity and mortality, in comparison to the prognostic value of expert reading. Materials and Methods: The study was conducted at the Nuclear Medicine Laboratory of the University of Thessaly, in Larissa, Greece. A total of 378 consecutive patients with known or suspected coronary artery disease were enrolled in the study. All participants were referred to our laboratory for the performance of stress/rest myocardial perfusion single photon emission computed tomography. Automated measurements of SSS, SRS, and SDS were obtained by Emory Cardiac Toolbox (ECTb (Version 3.0), Emory University, Atlanta, GA, USA), Myovation (MYO, Xeleris version 3.05, GE Healthcare, Chicago, IL, USA), and Quantitative Perfusion SPECT (QPS (Version 4.0), Cedars-Sinai Medical Center, Los Angeles, CA, USA) software packages. Follow-up data were recorded after phone contacts, as well as through review of hospital records. Results: Expert scoring of SSS and SDS had significantly greater prognostic ability in comparison to all software packages (p < 0.001 for all comparisons). Similarly, ECTb-obtained SRS measurements had significantly lower prognostic ability in comparison to expert scoring (p < 0.001), while expert scoring of SRS showed significantly higher prognostic ability compared to MYO (p = 0.018) and QPS (p < 0.001). Conclusions: Despite the useful contribution of automated analyses in the interpretation of myocardial perfusion studies, expert reading should continue to have a crucial role, not only in clinical decision making, but also in the assessment of prognosis.

ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ

ModularImageAnalysis (MIA) is an ImageJ plugin providing a code-free graphical environment in which complex automated analysis workflows can be constructed and distributed. The broad range of included modules cover all stages of a typical analysis workflow, from image loading through image processing, object detection, extraction of measurements, measurement-based filtering, visualisation and data exporting. MIA provides out-of-the-box compatibility with many advanced image processing plugins for ImageJ including Bio-Formats, DeepImageJ, MorphoLibJ and TrackMate, allowing these tools and their outputs to be directly incorporated into analysis workflows. By default, modules support spatially calibrated 5D images, meaning measurements can be acquired in both pixel and calibrated units. A hierarchical object relationship model allows for both parent-child (one-to-many) and partner (many-to-many) relationships to be established. These relationships underpin MIA's ability to track objects through time, represent complex spatial relationships (e.g. topological skeletons) and measure object distributions (e.g. count puncta per cell). MIA features dual graphical interfaces: the 'editing view' offers access to the full list of modules and parameters in the workflow, while the simplified 'processing view' can be configured to display only a focused subset of controls. All workflows are batch-enabled by default, with image files within a specified folder being processed automatically and exported to a single spreadsheet. Beyond the included modules, functionality can be extended both internally, through integration with the ImageJ scripting interface, and externally, by developing third-party Java modules that extend the core MIA framework. Here we describe the design and functionality of MIA in the context of a series of real-world example analyses.

Validation of natural language processing methods capturing semantic incoherence in the speech of patients with non-affective psychosis

Background

Impairments in speech production are a core symptom of non-affective psychosis (NAP). While traditional clinical ratings of patients' speech involve a subjective human factor, modern methods of natural language processing (NLP) promise an automatic and objective way of analyzing patients' speech. This study aimed to validate NLP methods for analyzing speech production in NAP patients.

Methods

Speech samples from patients with a diagnosis of schizophrenia or schizoaffective disorder were obtained at two measurement points, 6 months apart. Out of N = 71 patients at T1, speech samples were also available for N = 54 patients at T2. Global and local models of semantic coherence as well as different word embeddings (word2vec vs. GloVe) were applied to the transcribed speech samples. They were tested and compared regarding their correlation with clinical ratings and external criteria from cross-sectional and longitudinal measurements.

Results

Results did not show differences for global vs. local coherence models and found more significant correlations between word2vec models and clinically relevant outcome variables than for GloVe models. Exploratory analysis of longitudinal data did not yield significant correlation with coherence scores.

Conclusion

These results indicate that natural language processing methods need to be critically validated in more studies and carefully selected before clinical application.

Lung ultrasound compared to computed tomography detection and automated quantification of systemic sclerosis-associated interstitial lung disease: preliminary study

Lung ultrasound (LUS) is a promising tool for detecting systemic sclerosis-associated interstitial lung disease (SSc-ILD). Currently, consensus on the best LUS findings and execution technique is lacking.

OBJECTIVES

To compare qualitative and quantitative assessment of B-lines and pleural line (PL) alterations in SSc-ILD with chest computed tomography (CT) analysis.

METHODS

During 2021-2022, consecutive SSc patients according to 2013 ACR/EULAR classification criteria underwent pulmonary functional tests (PFTs). On the same day, if a CT was performed over a ± 6 months period, LUS was performed by two certified blinded operators using a 14-scans method. The ≥10 B-lines cut-off proposed by Tardella and the Fairchild's PL criteria fulfilment were selected as qualitative findings. As quantitative assessment, total B-lines number and the quantitative PL score adapted from the semi-quantitative Pinal-Fernandez score were collected. CT scans were evaluated by two thoracic radiologists for ILD presence, with further processing by automated texture analysis software (qCT).

RESULTS

29 SSc patients were enrolled. Both qualitative LUS scores were significantly associated to ILD presence on CT, with Fairchild's PL criteria resulting in slightly more accuracy. Results were confirmed on multivariate analysis. All qualitative and quantitative LUS findings were found to be significantly associated with qCT ILD extension and radiological abnormalities. Mid and basal PL quantitative score correlated with mid and basal qCT ILD extents. Both B-lines and PL alterations differently correlated with PFTs and clinical variables.

CONCLUSION

This preliminary study suggests the utility of a comprehensive LUS assessment for SSc-ILD detection compared with CT and qCT.

A Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model's effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively.

Automated image quality assessment of mammography phantoms: a systematic review

BACKGROUND

Computerized image analysis is a viable technique for evaluating image quality as a complement to human observers.

PURPOSE

To systematically review the image analysis software used in the assessment of 2D image quality using mammography phantoms.

MATERIAL AND METHODS

A systematic search of multiple databases was performed from inception to July 2020 for articles that incorporated computerized analysis of 2D images of physical mammography phantoms to determine image quality.

RESULTS

A total of 26 studies were included, 12 were carried out using direct digital imaging and 14 using screen film mammography. The ACR phantom (model-156) was the most frequently evaluated phantom, possibly due to the lack of accepted standard software. In comparison to the inter-observer variations, the computerized image analysis was more consistent in scoring test objects. The template matching method was found to be one of the most reliable algorithms, especially for high-contrast test objects, while several algorithms found low-contrast test objects to be harder to distinguish due to the smaller contrast variations between test objects and their backgrounds. This was particularly true for small object sizes.

CONCLUSION

Image analysis software was in agreement with human observers but demonstrated higher consistency and reproducibility of quality evaluation. Additionally, using computerized analysis, several quantitative metrics such as contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) could be used to complement the conventional scoring method. Implementing a computerized approach for monitoring image quality over time would be crucial to detect any deteriorating mammography system before clinical images are impacted.

QUANTITATIVE ASSESSMENT OF AUTOMATED OPTICAL COHERENCE TOMOGRAPHY IMAGE ANALYSIS USING A HOME-BASED DEVICE FOR SELF-MONITORING NEOVASCULAR AGE-RELATED MACULAR DEGENERATION

PURPOSE

To evaluate a prototype home optical coherence tomography device and automated analysis software for detection and quantification of retinal fluid relative to manual human grading in a cohort of patients with neovascular age-related macular degeneration.

METHODS

Patients undergoing anti-vascular endothelial growth factor therapy were enrolled in this prospective observational study. In 136 optical coherence tomography scans from 70 patients using the prototype home optical coherence tomography device, fluid segmentation was performed using automated analysis software and compared with manual gradings across all retinal fluid types using receiver-operating characteristic curves. The Dice similarity coefficient was used to assess the accuracy of segmentations, and correlation of fluid areas quantified end point agreement.

RESULTS

Fluid detection per B-scan had area under the receiver-operating characteristic curves of 0.95, 0.97, and 0.98 for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid, respectively. On a per volume basis, the values for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid were 0.997, 0.998, and 0.998, respectively. The average Dice similarity coefficient values across all B-scans were 0.64, 0.73, and 0.74, and the coefficients of determination were 0.81, 0.93, and 0.97 for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid, respectively.

CONCLUSION

Home optical coherence tomography device images assessed using the automated analysis software showed excellent agreement to manual human grading.

Automated quantification of measurable residual disease in chronic lymphocytic leukemia using an artificial intelligence-assisted workflow

Detection of measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is an important prognostic marker. The most common CLL MRD method in current use is multiparameter flow cytometry, but availability is limited by the need for expert manual analysis. Automated analysis has the potential to expand access to CLL MRD testing. We evaluated the performance of an artificial intelligence (AI)-assisted multiparameter flow cytometry (MFC) workflow for CLL MRD. We randomly selected 113 CLL MRD FCS files and divided them into training and validation sets. The training set (n = 41) was gated by expert manual analysis and used to train the AI model. We then compared the validation set (n = 72) MRD results obtained by the AI-assisted analysis versus those by expert manual analysis using the Pearson correlation coefficient and Bland-Altman plot method. In the validation set, the AI-assisted analysis correctly categorized cases as MRD-negative versus MRD-positive in 96% of cases. When comparing the AI-assisted analysis versus the expert manual analysis, the Pearson r was 0.8650, mean bias was 0.2237 log10 units, and the 95% limit of agreement (LOA) was ±1.0282 log10 units. The AI-assisted analysis performed sub-optimally in atypical immunophenotype CLL and in cases lacking residual normal B cells. When excluding these outlier cases, the mean bias improved to 0.0680 log10 units and the 95% LOA to ±0.2926 log10 units. An automated AI-assisted workflow allows for the quantification of MRD in CLL with typical immunophenotype. Further work is required to improve performance in atypical immunophenotype CLL.

A deep learning system for quantitative assessment of microvascular abnormalities in nailfold capillary images

OBJECTIVES

Nailfold capillaroscopy is key to timely diagnosis of systemic sclerosis (SSc), but is often not used in rheumatology clinics because the images are difficult to interpret. We aimed to develop and validate a fully automated image analysis system to fill this gap.

METHODS

We mimicked the image interpretation strategies of SSc experts, using deep learning networks to detect each capillary in the distal row of vessels and make morphological measurements. We combined measurements from multiple fingers to give a subject-level probability of SSc.We trained the system using high-resolution images from 111 subjects (Group A) and tested on images from subjects not in the training set: 132 imaged at high-resolution (Group B); 66 imaged with a low-cost digital microscope (Group C). Roughly half of each group had confirmed SSc, half were healthy controls or had primary Raynaud's phenomenon ('normal'). We also estimated the performance of SSc experts.

RESULTS

We compared automated SSc probabilities with the known clinical status of patients (SSc versus 'normal'), generating receiver operating characteristic curves (ROCs). For Group B, the area under the ROC (AUC) was 97% [94% - 99%] (median [90% confidence interval]), with equal sensitivity/specificity 91% [86% - 95%]. For Group C, AUC was 95% [88% - 99%], with equal sensitivity/specificity 89% [82% - 95%]. SSc expert consensus achieved sensitivity 82%, specificity 73%.

CONCLUSION

Fully automated analysis using deep learning can achieve diagnostic performance at least as good as SSc experts, and is sufficiently robust to work with low-cost digital microscope images.

High-resolution analysis of the cytosolic Ca2+ events in β cell collectives in situ

The release of peptide hormones is predominantly regulated by a transient increase in cytosolic Ca2+ concentration ([Ca2+]c). To trigger exocytosis, Ca2+ ions enter the cytosol from intracellular Ca2+ stores or from the extracellular space. The molecular events of late stages of exocytosis, and their dependence on [Ca2+]c, were extensively described in isolated single cells from various endocrine glands. Notably, less work has been done on endocrine cells in situ to address the heterogeneity of [Ca2+]c events contributing to a collective functional response of a gland. For this, β cell collectives in a pancreatic islet are particularly well suited as they are the smallest, experimentally manageable functional unit, where [Ca2+]c dynamics can be simultaneously assessed on both cellular and collective level. Here, we measured [Ca2+]c transients across all relevant timescales, from a subsecond to a minute time range, using high-resolution imaging with a low-affinity Ca2+ sensor. We quantified the recordings with a novel computational framework for automatic image segmentation and [Ca2+]c event identification. Our results demonstrate that under physiological conditions the duration of [Ca2+]c events is variable, and segregated into three reproducible modes, subsecond, second, and tens of seconds time range, and are a result of a progressive temporal summation of the shortest events. Using pharmacological tools we show that activation of intracellular Ca2+ receptors is both sufficient and necessary for glucose-dependent [Ca2+]c oscillations in β cell collectives, and that a subset of [Ca2+]c events could be triggered even in the absence of Ca2+ influx across the plasma membrane. In aggregate, our experimental and analytical platform was able to readily address the involvement of intracellular Ca2+ receptors in shaping the heterogeneity of [Ca2+]c responses in collectives of endocrine cells in situ.NEW & NOTEWORTHY Physiological glucose or ryanodine stimulation of β cell collectives generates a large number of [Ca2+]c events, which can be rapidly assessed with our newly developed automatic image segmentation and [Ca2+]c event identification pipeline. The event durations segregate into three reproducible modes produced by a progressive temporal summation. Using pharmacological tools, we show that activation of ryanodine intracellular Ca2+ receptors is both sufficient and necessary for glucose-dependent [Ca2+]c oscillations in β cell collectives.

Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.

STAGETOOL, a Novel Automated Approach for Mouse Testis Histological Analysis

Spermatogenesis is a complex differentiation process that takes place in the seminiferous tubules. A specific organization of spermatogenic cells within the seminiferous epithelium enables a synchronous progress of germ cells at certain steps of differentiation on the spermatogenic pathway. This can be observed in testis cross-sections where seminiferous tubules can be classified into distinct stages of constant cellular composition (12 stages in the mouse). For a detailed analysis of spermatogenesis, these stages have to be individually observed from testis cross-sections. However, the recognition of stages requires special training and expertise. Furthermore, the manual scoring is laborious considering the high number of tubule cross-sections that have to be analyzed. To facilitate the analysis of spermatogenesis, we have developed a convolutional deep neural network-based approach named "STAGETOOL." STAGETOOL analyses histological images of 4',6-diamidine-2'-phenylindole dihydrochloride (DAPI)-stained mouse testis cross-sections at ×400 magnification, and very accurately classifies tubule cross-sections into 5 stage classes and cells into 9 categories. STAGETOOL classification accuracy for stage classes of seminiferous tubules of a whole-testis cross-section is 99.1%. For cellular level analysis the F1 score for 9 seminiferous epithelial cell types ranges from 0.80 to 0.98. Furthermore, we show that STAGETOOL can be applied for the analysis of knockout mouse models with spermatogenic defects, as well as for automated profiling of protein expression patterns. STAGETOOL is the first fluorescent labeling-based automated method for mouse testis histological analysis that enables both stage and cell-type recognition. While STAGETOOL qualitatively parallels an experienced human histologist, it outperforms humans time-wise, therefore representing a major advancement in male reproductive biology research.

Assessing the corneal sub-basal nerve plexus by in vivo confocal microscopy in patients with blepharoptosis

BACKGROUND

To assess in vivo confocal microscopy features of corneal sub-basal nerve plexus in patients with congenital or aponeurogenic blepharoptosis using a fully automated software (ACCMetrics).

PATIENTS AND METHODS

This prospective study included 33 patients with blepharoptosis and 17 normal controls. The corneal sub-basal nerve plexus was assessed using in vivo confocal microscopy, and the ocular surface status was evaluated by tear break-up times.

RESULTS

The mean age of 33 patients with blepharoptosis and 17 normal controls were 38.77 ± 22.81 years and 48.35 ± 17.15 years, respectively. The mean duration of blepharoptosis was 16.42 ± 15.60 years. In 13 patients with unilateral blepharoptosis, there was no significant difference between affected eyes and contralateral eyes (all ps > .05), except for wider corneal nerve fibre width (CNFW) in affected eyes (0.024 ± 0.001 versus 0.023 ± 0.001 mm/mm², p = .021). In 20 patients with bilateral blepharoptosis, there was no significant difference between the eyes. No significant difference was detected between 19 cases with congenital blepharoptosis and 14 cases with aponeurogenic blepharoptosis. When compared with normal controls, eyes with both unilateral and bilateral blepharoptosis had significantly wider CNFW. But from the point of aetiology, only eyes with congenital blepharoptosis presented with wider CNFW (p = .001), rather than the eyes with aponeurogenic blepharoptosis (p = .093). Besides, four young patients with congenital blepharoptosis revealed very sparse sub-basal nerve plexus.

CONCLUSIONS

These data suggested that corneal confocal microscopy demonstrated no significant changes in patients with blepharoptosis as compared with normal controls, except for relatively wider CNFW in congenital affected eyes. However, in some children and young adults with congenital blepharoptosis, the density of corneal sub-basal nerve plexus was evidently decreased, which needs to be cautioned when ones with congenital blepharoptosis want to take corneal surgeries or wear contact lens.Key messagesWhen compared with normal controls, no significant effect was found in the influence of blepharoptosis on the most of corneal nerve parameters, except for corneal nerve fibre width (CNFW) in the group of congenital blepharoptosis.The age of onset of blepharoptosis may influence corneal nerve fibres, so timely surgical treatment of congenital blepharoptosis is not only conducive to the development of normal vision, but also beneficial to the reduction of corneal nerve lesions to some extent.We noted that some young blepharoptosis patients revealed sparse corneal nerve, which should be taken precaution when ones with congenital blepharoptosis who want to take corneal surgeries or wear contact lens.

Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients

Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intraobserver variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field.

Automated quantitative evaluation of fetal atrioventricular annular plane systolic excursion

OBJECTIVES

The primary aim of this study was to evaluate the feasibility of automated measurement of fetal atrioventricular (AV) plane displacement (AVPD) over several cardiac cycles using myocardial velocity traces obtained by color tissue Doppler imaging (cTDI). The secondary objectives were to establish reference ranges for AVPD during the second half of normal pregnancy, to assess fetal AVPD in prolonged pregnancy in relation to adverse perinatal outcome and to evaluate AVPD in fetuses with a suspicion of intrauterine growth restriction (IUGR).

METHODS

The population used to develop the reference ranges consisted of women with an uncomplicated singleton pregnancy at 18-42 weeks of gestation (n = 201). The prolonged-pregnancy group comprised women with an uncomplicated singleton pregnancy at ≥ 41 + 0 weeks of gestation (n = 107). The third study cohort comprised women with a singleton pregnancy and suspicion of IUGR, defined as an estimated fetal weight < 2.5^th centile or an estimated fetal weight < 10^th centile and umbilical artery pulsatility index > 97.5^th centile (n = 35). Cineloops of the four-chamber view of the fetal heart were recorded using cTDI. Regions of interest were placed at the AV plane in the left and right ventricular walls and the interventricular septum, and myocardial velocity traces were integrated and analyzed using an automated algorithm developed in-house to obtain mitral (MAPSE), tricuspid (TAPSE) and septal (SAPSE) annular plane systolic excursion. Gestational-age specific reference ranges were constructed and normalized for cardiac size. The correlation between AVPD measurements obtained using cTDI and those obtained by anatomic M-mode were evaluated, and agreement between these two methods was assessed using Bland-Altman analysis. The mean Z-scores of fetal AVPD in the cohort of prolonged pregnancies were compared between cases with normal and those with adverse outcome using Mann-Whitney U-test. The mean Z-scores of fetal AVPD in IUGR fetuses were compared with those in the normal reference population using Mann-Whitney U-test. Inter- and intraobserver variability for acquisition of cTDI recordings and offline analysis was assessed by calculating coefficients of variation (CV) using the root mean square method.

RESULTS

Fetal MAPSE, SAPSE and TAPSE increased with gestational age but did not change significantly when normalized for cardiac size. The fitted mean was highest for TAPSE throughout the second half of gestation, followed by SAPSE and MAPSE. There was a significant correlation between MAPSE (r = 0.64; P < 0.001), SAPSE (r = 0.72; P < 0.001) and TAPSE (r = 0.84; P < 0.001) measurements obtained by M-mode and those obtained by cTDI. The geometric means of ratios between AVPD measured by cTDI and by M-mode were 1.38 (95% limits of agreement (LoA), 0.84-2.25) for MAPSE, 1.00 (95% LoA, 0.72-1.40) for SAPSE and 1.20 (95% LoA, 0.92-1.57) for TAPSE. In the prolonged-pregnancy group, the mean ± SD Z-scores for MAPSE (0.14 ± 0.97), SAPSE (0.09 ± 1.02) and TAPSE (0.15 ± 0.90) did not show any significant difference compared to the reference ranges. Twenty-one of the 107 (19.6%) prolonged pregnancies had adverse perinatal outcome. The AVPD Z-scores were not significantly different between pregnancies with normal and those with adverse outcome in the prolonged-pregnancy cohort. The mean ± SD Z-scores for SAPSE (-0.62 ± 1.07; P = 0.006) and TAPSE (-0.60 ± 0.89; P = 0.002) were significantly lower in the IUGR group compared to those in the normal reference population, but the differences were not significant when the values were corrected for cardiac size. The interobserver CVs for the automated measurement of MAPSE, SAPSE and TAPSE were 28.1%, 17.7% and 15.3%, respectively, and the respective intraobserver CVs were 33.5%, 15.0% and 17.9%.

CONCLUSIONS

This study showed that fetal AVPD can be measured automatically by integrating cTDI velocities over several cardiac cycles. Automated analysis of AVPD could potentially help gather larger datasets to facilitate use of machine-learning models to study fetal cardiac function. The gestational-age associated increase in AVPD is most likely a result of increasing cardiac size, as the AVPD normalized for cardiac size did not change significantly between 18 and 42 weeks. A decrease was seen in TAPSE and SAPSE in IUGR fetuses, but not after correction for cardiac size. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Development of a Digital Case Management Tool for Community Based Inclusive Development Program

Disability inclusive development practices require reliable data to identify people with disabilities, their barriers to participation and support needs. Although several tools are available for measuring different components of disability, it is often difficult for program teams in low resource settings, including lay community workers of community based inclusive development (CBID) programs, to collect and analyze data for program monitoring and evaluation. This paper presents the development of a digital CBID Modular Tool with automated data analysis to support routine case management processes and monitoring of a CBID program in Laos PDR. The tool was developed in different phases involving stakeholder consultations, auditing of existing tools, content development for the different modules for disability assessment and support needs, software development and testing. The tool was developed in a participatory process including people with disabilities. The tool measures needs and support requirements of people with disabilities in health, functioning, economic, education and caregiver support domains, and enables intervention planning. The content included is both context specific and universal as derived from the widely used validated tools. This unique digital CBID Modular Tool can support data collection by lay community workers and support reliable data collection to measure disability inclusion in a development program.

Automated Fiber Diameter and Porosity Measurements of Plasma Clots in Scanning Electron Microscopy Images

Scanning Electron Microscopy (SEM) is a powerful, high-resolution imaging technique widely used to analyze the structure of fibrin networks. Currently, structural features, such as fiber diameter, length, density, and porosity, are mostly analyzed manually, which is tedious and may introduce user bias. A reliable, automated structural image analysis method would mitigate these drawbacks. We evaluated the performance of DiameterJ (an ImageJ plug-in) for analyzing fibrin fiber diameter by comparing automated DiameterJ outputs with manual diameter measurements in four SEM data sets with different imaging parameters. We also investigated correlations between biophysical fibrin clot properties and diameter, and between clot permeability and DiameterJ-determined clot porosity. Several of the 24 DiameterJ algorithms returned diameter values that highly correlated with and closely matched the values of the manual measurements. However, optimal performance was dependent on the pixel size of the images—best results were obtained for images with a pixel size of 8–10 nm (13–16 pixels/fiber). Larger or smaller pixels resulted in an over- or underestimation of diameter values, respectively. The correlation between clot permeability and DiameterJ-determined clot porosity was modest, likely because it is difficult to establish the correct image depth of field in this analysis. In conclusion, several DiameterJ algorithms (M6, M5, T3) perform well for diameter determination from SEM images, given the appropriate imaging conditions (13–16 pixels/fiber). Determining fibrin clot porosity via DiameterJ is challenging.

Automated identification of maximal differential cell populations in flow cytometry data

We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice‐based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor.

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data

A major challenge in the analysis of highly multiplexed imaging data is the assignment of cells to a priori known cell types. Existing approaches typically solve this by clustering cells followed by manual annotation. However, these often require several subjective choices and cannot explicitly assign cells to an uncharacterized type. To help address these issues we present Astir, a probabilistic model to assign cells to cell types by integrating prior knowledge of marker proteins. Astir uses deep recognition neural networks for fast inference, allowing for annotations at the million-cell scale in the absence of a previously annotated reference. We apply Astir to over 2.4 million cells from suspension and imaging datasets and demonstrate its scalability, robustness to sample composition, and interpretable uncertainty estimates. We envision deployment of Astir either for a first broad cell type assignment or to accurately annotate cells that may serve as biomarkers in multiple disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Computer-Aided Fracture Size Measurement in Orbital Fractures-An Alternative to Manual Evaluation

Objective

We aimed to present a novel semiautomated tool for orbital fracture size measurement and to compare the variability of the proposed method with traditional manual measurements.

Methods

Maximal anteroposterior (AP) and mediolateral (ML) dimensions of orbital fractures from computed tomography images were measured for 15 patients with unilateral orbital fractures by 2 surgeons manually and with a semiautomatic software. Variability was assessed with Bland-Altman limits of agreement plots and intra-class correlation coefficients (ICCs).

Results

The intra-observer ICCs in manual and automatic measurements were high, >0.9. The inter-observer ICCs in manual measurements were 0.926 (AP) and 0.631 (ML) and in automatic measurements 0.989 (AP) and 0.989 (ML). The ICCs for manual and semiautomated variability were 0.899 (AP) and 0.669 (ML). The differences were thus particularly pronounced in the ML dimensions. In addition, with the semiautomated technique, a total fracture area could be measured and compared with the total area of the bony orbit and a 3-dimensional reformatted image could be generated.

Conclusions

Intra- and inter-observer variability proved to be very low for measuring fracture maximal AP length and ML width, making both the manual and the semiautomatic methods feasible clinically. The semiautomatic fracture size analysis allows better observer-independent repeatability for fracture size measurements and provides the possibility for total fracture area measurements at any orbital bony site, even in challenging nonplanar topography.

A semiautomated measurement of muscle fiber size using the Imaris software

The size and shape of skeletal muscle fibers are affected by various physiological and pathological conditions, such as muscle atrophy, hypertrophy, regeneration, and dystrophies. Hence, muscle fiber cross-sectional area (CSA) is an important determinant of muscle health and plasticity. We adapted the Imaris software to automatically segment muscle fibers based on fluorescent labeling of the plasma membrane and measure muscle fiber CSA. Analysis of muscle cross sections by the Imaris semiautomated and manual approaches demonstrated a similar decrease in CSA of atrophying muscles from fasted mice compared with fed controls. In addition, we previously demonstrated that downregulation of the Ca2+-specific protease calpain-1 attenuates muscle atrophy. Accordingly, both the Imaris semiautomated and manual approaches showed a similar increase in CSA of fibers expressing calpain-1 shRNA compared with adjacent nontransfected fibers in the same muscle cross section. Although both approaches seem valid for measurements of muscle fiber size, the manual marking method is less preferable because it is highly time-consuming, subjective, and limits the number of cells that can be analyzed. The Imaris semiautomated approach is user-friendly, requires little training or optimization, and can be used to efficiently and accurately mark thousands of fibers in a short period. As a novel addition to the commonly used statistics, we also describe statistical tests that quantify the strength of an effect on fiber size, enabling detection of significant differences between skewed distributions that would otherwise not be detected using typical methods.

DebarcodeR increases fluorescent cell barcoding capacity and accuracy

Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs. DebarcodeR addresses these unmet needs by providing a framework for computational debarcoding augmented by improvements to experimental methods. Adaptive regression modeling accounted for differential dye uptake between different cell types and Gaussian mixture modeling provided a robust method to probabilistically assign cells to samples. Assignment tolerance parameters are available to allow users to balance high cell recovery with accurate assignments. Improvements to experimental methods include: (1) inclusion of an "external standard" control where a pool of all cells was stained a single level of each barcoding dyes and (2) an "internal standard" where each cell is stained with a single level of a separate dye. DebarcodeR significantly improved speed, accuracy, and reproducibility of FCB while avoiding selective loss of unusual cell subsets when debarcoding microtiter plates of cell lines and heterogenous mixtures of primary cells. DebarcodeR is available on Github as an R package that works with flowCore and Cytoverse packages at github.com/cytolab/DebarcodeR.

An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.

Automated Bowel Sound Analysis: An Overview

Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.

Quantitative Assessment of Experimental Ocular Inflammatory Disease

Ocular inflammation imposes a high medical burden on patients and substantial costs on the health-care systems that mange these often chronic and debilitating diseases. Many clinical phenotypes are recognized and classifying the severity of inflammation in an eye with uveitis is an ongoing challenge. With the widespread application of optical coherence tomography in the clinic has come the impetus for more robust methods to compare disease between different patients and different treatment centers. Models can recapitulate many of the features seen in the clinic, but until recently the quality of imaging available has lagged that applied in humans. In the model experimental autoimmune uveitis (EAU), we highlight three linked clinical states that produce retinal vulnerability to inflammation, all different from healthy tissue, but distinct from each other. Deploying longitudinal, multimodal imaging approaches can be coupled to analysis in the tissue of changes in architecture, cell content and function. This can enrich our understanding of pathology, increase the sensitivity with which the impacts of therapeutic interventions are assessed and address questions of tissue regeneration and repair. Modern image processing, including the application of artificial intelligence, in the context of such models of disease can lay a foundation for new approaches to monitoring tissue health.

Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two-step pipeline for the analysis of high-resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for the detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from the amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open-source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape, and defect presence, enabling the detection of correlations between these features.

Gap19, a Cx43 Hemichannel Inhibitor, Acts as a Gating Modifier That Decreases Main State Opening While Increasing Substate Gating

Cx43 hemichannels (HCs) are electrically and chemically gated transmembrane pores with low open probability and multiple conductance states, which makes kinetic studies of channel gating in large datasets challenging. Here, we developed open access software, named HemiGUI, to analyze HC gating transitions and investigated voltage-induced HC opening based on up to ≈4000 events recorded in HeLa-Cx43-overexpressing cells. We performed a detailed characterization of Cx43 HC gating profiles and specifically focused on the role of the C-terminal tail (CT) domain by recording the impact of adding an EGFP tag to the Cx43 CT end (Cx43-EGFP) or by supplying the Cx43 HC-inhibiting peptide Gap19 that interferes with CT interaction with the cytoplasmic loop (CL). We found that Gap19 not only decreased HC opening activity to the open state (≈217 pS) but also increased the propensity of subconductance (≈80 pS) transitions that additionally became slower as compared to the control. The work demonstrates that large sample transition analysis allows detailed investigations on Cx43 HC gating and shows that Gap19 acts as a HC gating modifier by interacting with the CT that forms a crucial gating element.

Catalysis-in-a-Box: Robotic Screening of Catalytic Materials in the Time of COVID-19 and Beyond

This work describes the design and implementation of an automated device for catalytic materials testing by direct modifications to a gas chromatograph (GC). The setup can be operated as a plug-flow isothermal reactor and enables the control of relevant parameters such as reaction temperature and reactant partial pressures directly from the GC. High-quality kinetic data (including reaction rates, product distributions, and activation barriers) can be obtained at almost one-tenth of the fabrication cost of analogous commercial setups. With these key benefits including automation, low cost, and limited experimental equipment instrumentation, this implementation is intended as a high-throughput catalyst screening reactor that can be readily utilized by materials synthesis researchers to assess the catalytic properties of their synthesized structures in vapor-phase chemistries.

Toward the Systematic Identification of Microplastics in the Environment: Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis

Microplastics (MP) are ubiquitous within the environment, but the approaches to analysis of this contaminant are currently quite diverse, with a number of analytical methods available. The comparability of results is hindered as even for a single analytical method such as Fourier transform infrared spectroscopy (FT-IR) the different instruments currently available do not allow a harmonized analysis. To overcome this limitation, a new free of charge software tool, allowing the systematic identification of MP in the environment (siMPle) was developed. This software tool allows a rapid and harmonized analysis of MP across FT-IR systems from different manufacturers (Bruker Hyperion 3000, Agilent Cary 620/670, PerkinElmer Spotlight 400, and Thermo Fischer Scientific Nicolet iN10). Using the same database and the automated analysis pipeline in siMPle, MP were identified in samples that were analyzed with instruments with different detector systems as well as optical resolutions and the results discussed.

Biomarkers of seizure severity derived from wearable devices

Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high-risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic-clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.

Segregation of Dispersed Silica Nanoparticles in Microfluidic Water-in-Oil Droplets: A Kinetic Study

Dispersed negatively charged silica nanoparticles segregate inside microfluidic water-in-oil (W/O) droplets that are coated with a positively charged lipid shell. We report a methodology for the quantitative analysis of this self-assembly process. By using real-time fluorescence microscopy and automated analysis of the recorded images, kinetic data are obtained that characterize the electrostatically-driven self-assembly. We demonstrate that the segregation rates can be controlled by the installment of functional moieties on the nanoparticle's surface, such as nucleic acid and protein molecules. We anticipate that our method enables the quantitative and systematic investigation of the segregation of (bio)functionalized nanoparticles in microfluidic droplets. This could lead to complex supramolecular architectures on the inner surface of micrometer-sized hollow spheres, which might be used, for example, as cell containers for applications in the life sciences.

Automated quality control in nuclear medicine using the structured noise index

Purpose

Daily flood‐field uniformity evaluation serves as the central element of nuclear medicine (NM) quality control (QC) programs. Uniformity images are traditionally analyzed using pixel value‐based metrics, that is, integral uniformity (IU), which often fail to capture subtle structure and patterns caused by changes in gamma camera performance, requiring visual inspections which are subjective and time demanding. The goal of this project was to implement an advanced QC metrology for NM to effectively identify nonuniformity issues, and report issues in a timely manner for efficient correction prior to clinical use. The project involved the implementation of the program over a 2‐year period at a multisite major medical institution.

Methods

Using a previously developed quantitative uniformity analysis metric, the structured noise index (SNI) [Nelson et al. (2014), \textit{J Nucl Med.}, \textbf{55}:169—174], an automated QC process was developed to analyze, archive, and report on daily NM QC uniformity images. Clinical implementation of the newly developed program ran in parallel with the manufacturer’s reported IU‐based QC program. The effectiveness of the SNI program was evaluated over a 21‐month period using sensitivity and coefficient of variation statistics.

Results

A total of 7365 uniformity QC images were analyzed. Lower level SNI alerts were generated in 12.5% of images and upper level alerts in 1.7%. Intervention due to image quality issues occurred on 26 instances; the SNI metric identified 24, while the IU metric identified eight. The SNI metric reported five upper level alerts where no clinical engineering intervention was deemed necessary.

Conclusion

An SNI‐based QC program provides a robust quantification of the performance of gamma camera uniformity. It operates seamlessly across a fleet of multiple camera models and, additionally, provides effective workflow among the clinical staff. The reliability of this process could eliminate the need for visual inspection of each image, saving valuable time, while enabling quantitative analysis of inter‐ and intrasystem performance.

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.

Quantitative Analysis of Ellipsoid Zone in Acute Posterior Multifocal Placoid Pigment Epitheliopathy

Purpose:

Quantitative end points for uveitis are needed. Here we quantify the rate of ellipsoid zone (EZ) recovery on optical coherence tomography (OCT) and correlate it with visual acuity (VA) improvement in patients with acute posterior multifocal placoid pigmented epitheliopathy (APMPPE). We use automated and manually graded EZ area analysis to assess EZ recovery in APMPPE.

Methods:

We performed a retrospective review of 9 APMPPE cases (18 eyes) that had characteristic clinical examination and fluorescein angiography findings, outer retinal disruption on spectral-domain OCT, and treatment with systemic steroids after an unambiguous laboratory workup. The EZ was delineated using custom software to perform automated analysis and manual grading by 2 independent physicians. Quantitation of EZ changes was performed in ImageJ (National Institutes of Health). EZ maps were compared with equivalent findings from EZ en face OCT segmentation.

Results:

The 9 cases in our study were followed for an average of 198 days. Symptomatic improvement occurred in all eyes. VA recovery occurred in 83% of eyes and depended on presenting foveal involvement. Positive slopes of EZ area over time demonstrated recovery. EZ recovery profiles determined by manual and automated software demonstrated high Pearson correlation coefficients (0.78-0.94). Slab en face EZ analysis demonstrated moderate agreement.

Conclusions:

EZ recovery correlates with symptomatic and VA recovery. Automated EZ analysis shows strong agreement with manually graded EZ analysis in APMPPE. EZ recovery in patients with APMPPE provides a biomarker for recovery and may be applied to other diseases affecting the outer retina.

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods often do not capture the richness inherent to the natural behavior. Here, we provide detailed step-by-step instructions with visualizations of our recent methodology, DeepBehavior. The DeepBehavior toolbox uses deep learning frameworks built with convolutional neural networks to rapidly process and analyze behavioral videos. This protocol demonstrates three different frameworks for single object detection, multiple object detection, and three-dimensional (3D) human joint pose tracking. These frameworks return cartesian coordinates of the object of interest for each frame of the behavior video. Data collected from the DeepBehavior toolbox contain much more detail than traditional behavior analysis methods and provides detailed insights to the behavior dynamics. DeepBehavior quantifies behavior tasks in a robust, automated, and precise way. Following the identification of behavior, post-processing code is provided to extract information and visualizations from the behavioral videos.

The stem cell inhibitor salinomycin decreases colony formation potential and tumor-initiating population in docetaxel-sensitive and docetaxel-resistant prostate cancer cells

BACKGROUND

Prostate cancer (PCa) is one of the most frequently diagnosed tumors in men. In general, therapies for localized PCa are curative. However, treatment of advanced PCa is considered palliative since development of therapy resistance occurs rapidly. It has been shown that tumor-initiating cells are likely involved in therapy resistance. They are not eliminated by conventional therapies and thereby lead to tumor progression and relapse. The aim of this study was to evaluate the effects of the known stem cell inhibitor salinomycin on this critical subpopulation of cells.

METHODS

Expression of the cell surface markers CD24 and CD44 was assessed by immunofluorescence and fluorescence-activated cell sorting. Colony formation efficiency and classification of colony types with varying tumor-initiating potential (holoclones, meroclones, and paraclones) were analyzed in an automated way by the newly developed CATCH-colonies software in the absence or presence of salinomycin.

RESULTS

Automated high-resolution colony formation analysis consistently identified the various colony types in a broad range of PCa cell lines. Serial clonogenic assays confirmed that holoclones show the highest colony formation potential and maintain their tumor-initiating capacity over multiple rounds. Furthermore, holoclones showed high expression of CD44, while CD24 was not expressed in these clones, thus representing the well-described tumor-initiating CD24^- /CD44^high population. Salinomycin decreased the CD24^- /CD44^high population in both docetaxel-sensitive PC3 and docetaxel-resistant (DR) PC3-DR. Moreover, treatment of PC3, DU145, PC3-DR, and DU145-DR with salinomycin led to a significant reduction in the colony formation potential by targeting the colonies with high tumor-initiating potential.

CONCLUSIONS

Taken together, we demonstrated that salinomycin specifically targets the tumor-initiating cell population in docetaxel-sensitive and docetaxel-resistant PCa cells and may represent a potential therapeutic approach for the treatment of advanced PCa.

Modeling Incoherent Discourse in Non-Affective Psychosis

BACKGROUND

Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis.

METHODS

Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects.

RESULTS

Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder.

CONCLUSIONS

Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.

Semi-automated analysis of 4D flow MRI to assess the hemodynamic impact of intracranial atherosclerotic disease

PURPOSE

This study evaluated the feasibility of using 4D flow MRI and a semi-automated analysis tool to assess the hemodynamic impact of intracranial atherosclerotic disease (ICAD). The ICAD impact was investigated by evaluating pressure drop (PD) at the atherosclerotic stenosis and changes in cerebral blood flow distribution in patients compared to healthy controls.

METHODS

Dual-venc 4D flow MRI was acquired in 25 healthy volunteers and 16 ICAD patients (ICA, N = 3; MCA, N = 13) with mild (<50%), moderate (50-69%), or severe (>70%) intracranial stenosis. A semi-automated analysis tool was developed to quantify velocity and flow from 4D flow MRI and to evaluate cerebral blood flow redistribution. PD at stenosis was estimated using the Bernoulli equation. The PD calculation was examined by an in vitro phantom study against flow simulations.

RESULTS

Flow analysis in controls indicated symmetry in blood flow rate (FR) and peak velocity (PV) between the brain hemispheres. For patients, PV in the affected hemisphere was significantly (65%) higher than the normal side (P = 0.002). However, FR to both hemispheres of the brain was the same. The PD depicted significant correlation with PV asymmetry in patients (ρ = 0.67 and P = 0.02), and it was significantly higher for severe compared to moderate stenosis (3.73 vs. 2.30 mm Hg, P = 0.02).

CONCLUSION

4D flow MRI quantification enables assessment of the hemodynamic impact of ICAD. The significant difference of the PD between patients with severe and moderate stenosis and its correlation with PV asymmetry suggest that PD may be a pertinent hemodynamic biomarker to evaluate ICAD.

Automated analysis of fetal cardiac function using color tissue Doppler imaging in second half of normal pregnancy

OBJECTIVES

Color tissue Doppler imaging (cTDI) is a promising tool for the assessment of fetal cardiac function. However, the analysis of myocardial velocity traces is cumbersome and time-consuming, limiting its application in clinical practice. The aim of this study was to evaluate fetal cardiac function during the second half of pregnancy and to develop reference ranges using an automated method to analyze cTDI recordings from a cardiac four-chamber view.

METHODS

This was a cross-sectional study including 201 normal singleton pregnancies between 18 and 42 weeks of gestation. During fetal echocardiography, a four-chamber view of the heart was visualized and cTDI was performed. Regions of interest were positioned at the level of the atrioventricular plane in the left ventricular (LV), right ventricular (RV) and septal walls of the fetal heart, to obtain myocardial velocity traces that were analyzed offline using the automated algorithm. Peak myocardial velocities during atrial contraction (Am), ventricular ejection (Sm) and rapid ventricular filling, i.e. early diastole (Em), as well as the Em/Am ratio, mechanical cardiac time intervals and myocardial performance index (cMPI) were evaluated, and gestational age-specific reference ranges were constructed.

RESULTS

At 18 weeks of gestation, the peak myocardial velocities, presented as fitted mean with 95% CI, were: LV Am, 3.39 (3.09-3.70) cm/s; LV Sm, 1.62 (1.46-1.79) cm/s; LV Em, 1.95 (1.75-2.15) cm/s; septal Am, 3.07 (2.80-3.36) cm/s; septal Sm, 1.93 (1.81-2.06) cm/s; septal Em, 2.57 (2.32-2.84) cm/s; RV Am, 4.89 (4.59-5.20) cm/s; RV Sm, 2.31 (2.16-2.46) cm/s; and RV Em, 2.94 (2.69-3.21) cm/s. At 42 weeks of gestation, the peak myocardial velocities had increased to: LV Am, 4.25 (3.87-4.65) cm/s; LV Sm, 3.53 (3.19-3.89) cm/s; LV Em, 4.55 (4.18-4.94) cm/s; septal Am, 4.49 (4.17-4.82) cm/s; septal Sm, 3.36 (3.17-3.55) cm/s; septal Em, 3.76 (3.51-4.03) cm/s; RV Am, 6.52 (6.09-6.96) cm/s; RV Sm, 4.95 (4.59-5.32) cm/s; and RV Em, 5.42 (4.99-5.88) cm/s. The mechanical cardiac time intervals generally remained more stable throughout the second half of pregnancy, although, with increased gestational age, there was an increase in duration of septal and RV atrial contraction, LV pre-ejection and septal and RV ventricular ejection, while there was a decrease in duration of septal postejection. Regression equations used for the construction of gestational age-specific reference ranges for peak myocardial velocities, Em/Am ratios, mechanical cardiac time intervals and cMPI are presented.

CONCLUSION

Peak myocardial velocities increase with gestational age, while the mechanical time intervals remain more stable throughout the second half of pregnancy. Using an automated method to analyze cTDI-derived myocardial velocity traces, it was possible to construct reference ranges, which could be used in distinguishing between normal and abnormal fetal cardiac function. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.