SCREENES: Enhancing non-uniform sampling reconstruction for symmetrical NMR spectroscopy

BACKGROUND

Symmetrical NMR spectroscopy, such as Total Correlation Spectroscopy (TOCSY) and other homonuclear spectroscopy, displays symmetry in chemical shift but are generally not symmetrical in terms of intensity, which constitutes a pivotal branch of multidimensional NMR spectroscopy and offers a robust tool for elucidating the structures and dynamics of complex samples, particularly in the context of biological macromolecules. Non-Uniform Sampling (NUS) stands as a critical technique for accelerating multidimensional NMR experiments. However, symmetrical NMR spectroscopy inherently presents dynamic peak intensities, where cross peaks tend to be substantially weaker compared to diagonal peaks. Recovering these weaker cross peaks from NUS data poses a significant challenge, often resulting in compromised data quality.

RESULTS

We enhance the reconstruction quality of NUS symmetrical NMR spectroscopy based on the assumption that the asymmetry in intensity is mild. Regarding the sampling schedule, we employ the symmetrical sampling structure integrated with Poisson sampling schedule to enhance the efficiency of data acquisition. In term of the reconstruction algorithm, we propose the new method by incorporating hard and soft symmetrical constraints into our recently developed L1-norm-based Compressed Sensing (CS) method known as Sparse Complex-valued REconstruction Enabled by Newton method (SCREEN). Additionally, we propose a two-step reconstruction strategy that separately addresses diagonal and cross peaks. In this two-step strategy, cross peaks are effectively reconstructed by excluding the stronger diagonal peaks. Extensive experimental results validate the effectiveness of our proposed methodology.

SIGNIFICANCE

This method enhances the overall quality of the reconstructed NUS symmetrical NMR spectra, especially in terms of cross peaks, thereby enriching the interpretation of spectral information. Furthermore, it boosts the robustness towards regularization parameters, facilitating a user-friendly experience.

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

BACKGROUND

The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

RESULTS

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

CONCLUSIONS

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

RSV-GenoScan: An automated pipeline for whole-genome human respiratory syncytial virus (RSV) sequence analysis

BACKGROUND

Advances in high-throughput sequencing (HTS) technologies and reductions in sequencing costs have revolutionised the study of genomics and molecular biology by making whole-genome sequencing (WGS) accessible to many laboratories. However, the analysis of WGS data requires significant computational effort, which is the major drawback in implementing WGS as a routine laboratory technique.

OBJECTIVE

Automated pipelines have been developed to overcome this issue, but they do not exist for all organisms. This is the case for human respiratory syncytial virus (RSV), which is a leading cause of lower respiratory tract infections in infants, the elderly, and immunocompromised adults.

RESULTS

We present RSV-GenoScan, a fast and easy-to-use pipeline for WGS analysis of RSV generated by HTS on Illumina or Nanopore platforms. RSV-GenoScan automates the WGS analysis steps directly from the raw sequence data. The pipeline filters the sequence data, maps the reads to the RSV reference genomes, generates a consensus sequence, identifies the RSV subgroup, and lists amino acid mutations, insertions and deletions in the F and G viral genes. This enables the rapid identification of mutations in these coding genes that are known to confer resistance to monoclonal antibodies.

AVAILABILITY

RSV-GenoScan is freely available at https://github.com/AlexandreD-bio/RSV-GenoScan.

Measuring and monitoring the transition to the circular economy of universities: CExUNV

The circular economy (CE) has been identified as a critical strategy to contribute to sustainable development and the achievement of the Sustainable Development Goals of the 2030 Agenda, being one of the biggest challenges for society in general and organisations in particular. Hence, various methods have been developed in recent years to measure the level of circularity of organisations, such as methodologies, indicators, metrics and CE tools. Universities, as education, research and community service organisations, play a key role in the promotion of and transition to the CE. Therefore, they require methods that enable them to quantify and monitor their level of circularity. However, it has been shown that CE methods developed for organisations are not suitable for such complex institutions, and, to date, no sector-specific methods have been developed. In this context, it has become necessary to develop a useful method to quantify and monitor the current level of circularity, assess its progress and facilitate decision-making on circularity that addresses the specific needs of this type of organisation. Thus, a set of 82 CE indicators and their metrics, specific to universities, has been proposed. They have been implemented in an Excel spreadsheet tool called CExUNV, in order to promote and facilitate their use. In addition, 41 improvement actions have been suggested to guide and assist universities in their progress towards CE.

ECGMiner: A flexible software for accurately digitizing ECG

BACKGROUND AND OBJECTIVE

The electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but store the numerical data in a proprietary format that is not interpretable and is not therefore useful for automatic diagnosis. There have been many efforts to digitize paper-based ECGs. The main limitations of previous works in ECG digitization are that they require manual selection of the regions of interest, only partly provide signal digitization, and offer limited accuracy.

METHODS

We have developed the ECGMiner, an open-source software to digitize ECG images. It is precise, fast, and simple to use. This software digitizes ECGs in four steps: 1) recognizing the image composition; 2) removing the gridline; 3) extracting the signals; 4) post-processing and storing the data.

RESULTS

We have evaluated the ECGMiner digitization capabilities using the Pearson Correlation Coefficient (PCC) and the Root Mean Square Error (RMSE) measures, and we consider ECG from two large, public, and widely used databases, LUDB and PTB-XL. The actual and digitized values of signals in both databases have been compared. The software's ability to correctly identify the location of characteristic waves has also been validated. Specifically, the PCC values are between 0.971 and 0.995, and the RMSE values are between 0.011 and 0.031 mV.

CONCLUSIONS

The ECGMiner software presented in this paper is open access, easy to install, easy to use, and capable of precisely recovering the paper-based/digital ECG signal data, regardless of the input format and signal complexity. ECGMiner outperforms existing digitization algorithms in terms of PCC and RMSE values.

Special issue on software citation, indexing, and discoverability

Software plays a fundamental role in research as a tool, an output, or even as an object of study. This special issue on software citation, indexing, and discoverability brings together five papers examining different aspects of how the use of software is recorded and made available to others. It describes new work on datasets that enable large-scale analysis of the evolution of software usage and citation, that presents evidence of increased citation rates when software artifacts are released, that provides guidance for registries and repositories to support software citation and findability, and that shows there are still barriers to improving and formalising software citation and publication practice. As the use of software increases further, driven by modern research methods, addressing the barriers to software citation and discoverability will encourage greater sharing and reuse of software, in turn enabling research progress.

CORRELATION OF THE SAGITTAL BALANCE WITH POSTURAL ANALYSIS OF THE PELVIS AND LUMBAR SPINE

Objective

Evaluate and correlate the sagittal balance parameters with the postural of the pelvis and lumbar spine.

Methods

80 individuals of both sexes, aged between 20 and 35 years, were evaluated. Biophotogrammetry was done with the SAPO software program. Measurements of the sagittal balance parameters were obtained by analyzing a lateral view panoramic radiography of the vertebral column, in which the anatomical points of reference were digitally marked. The calculation of the angles was done automatically by the Keops program.

Results

In Keops assessment, 17.5% of the sample had high pelvic incidence angles (> 60°), 31.5% had low pelvic incidence angles (< 45°), and 51.2% had medium pelvic incidence angles (between 46° and 59°). SAPO showed 12,5% lordosis, 40% retroversion, and 47,5% normal curvature. In the right lateral view, pelvic incidence angle had a moderate and positive correlation with vertical alignment of the trunk and with vertical alignment of the body, and a negative and moderate correlation with horizontal alignment of the pelvis.

Conclusion

Differences were found between vertical alignment measurements from the postural evaluation system (SAPO). A positive correlation was found between PI from Keops and pelvic anteversion from SAPO. Level of Evidence II; Prospective Study.

Integration and usability of a digital cancer risk stratification tool to optimize identification of patients at risk for hereditary cancers: A pilot study

BACKGROUND

Patients with a personal or family history of cancer may have elevated risk of developing future cancers, which often remains unrecognized due to lapses in screening. This pilot study assessed the usability and clinical outcomes of a cancer risk stratification tool in a gynecologic oncology clinic.

METHODS

New gynecologic oncology patients were prompted to complete a commercially developed personal and family history-based risk stratification tool to assess eligibility for genetic testing using National Comprehensive Cancer Network criteria and estimated lifetime breast cancer risk using the Tyrer-Cuzick model. After use of the risk stratification tool, usability was assessed via completion rate and the System Usability Scale, and health literacy was assessed using the BRIEF Health Literacy Screening Tool.

RESULTS

130 patients were prompted to complete the risk stratification tool; 93 (72%) completed the tool. Race and ethnicity and insurance type were not associated with tool completion. The median System Usability Scale score was 83 out of 100 (interquartile range, 60-95). Health literacy positively correlated with perceived usability. Public insurance and race or ethnicity other than non-Hispanic White was associated with lower perceived usability. Sixty (65%) patients met eligibility criteria for genetic testing, and 21 (38% of 56 eligible patients) were candidates for enhanced breast cancer screening based on an estimated lifetime breast cancer risk of ≥20%.

CONCLUSIONS

A majority of patients completed the digital cancer risk stratification tool. Older age, lower health literacy, public insurance, and race or ethnicity other than non-Hispanic White were associated with lower perceived tool usability.

Objective comparison of commonly used computed tomography body composition analysis software

OBJECTIVES

Sarcopenia is defined as an age-related, involuntary loss of skeletal muscle mass and strength. This condition is increasingly gaining clinical attention, as it has proved a predictor of complications and unfavorable outcomes in several diseases. For analysis of body composition on computed tomography images, several different software packages are used. Extensive research is being conducted globally to establish general cutoff values for different patient groups by combining the results of different studies with meta-analysis. Therefore, it is important that the measurements are independent of the software used. However, clinical software comparisons suggest there are differences between analysis packages, which would complicate establishment of cutoff values. For this study, we compared the eight most used analysis software programs in an objective manner, using a phantom image, to assess if their results can be readily compared.

METHODS

Eight software packages (sliceOmatic, OsiriX, ImageJ/Fiji, Mimics, CoreSlicer, SarcoMeas, 3D Slicer, and Aquarius iNtuition) were objectively evaluated, by performing measurements in a standardized synthetic image, containing fixed muscle and fat compartments with homogeneous radiodensities. For all programs, the measured areas and radiodensities of the regions of interest were assessed.

RESULTS

For sliceOmatic, OsiriX, ImageJ/Fiji, Mimics, CoreSlicer, SarcoMeas, and 3D Slicer, identical results were found, all reporting correct values for muscle and fat areas as well as correct radiodensity values, whereas values reported by Aquarius iNtuition deviated ≤ 5% for area measurements and had slight variation in radiodensity measurements.

CONCLUSIONS

Seven of eight software packages (sliceOmatic, OsiriX, ImageJ/Fiji, Mimics, CoreSlicer, SarcoMeas, and 3D Slicer) perform identically, so their results can be readily compared and combined when assessing body composition in computed tomography images. Area measurements acquired with Aquarius iNtuition may differ slightly (≤ 5%) from the other packages.

An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

Real-time magnetic resonance imaging (rtMRI) is a technique that provides high-contrast videographic data of human anatomy in motion. Applied to the vocal tract, it is a powerful method for capturing the dynamics of speech and other vocal behaviours by imaging structures internal to the mouth and throat. These images provide a means of studying the physiological basis for speech, singing, expressions of emotion, and swallowing that are otherwise not accessible for external observation. However, taking quantitative measurements from these images is notoriously difficult. We introduce a signal processing pipeline that produces outlines of the vocal tract from the lips to the larynx as a quantification of the dynamic morphology of the vocal tract. Our approach performs simple tissue classification, but constrained to a researcher-specified region of interest. This combination facilitates feature extraction while retaining the domain-specific expertise of a human analyst. We demonstrate that this pipeline generalises well across datasets covering behaviours such as speech, vocal size exaggeration, laughter, and whistling, as well as producing reliable outcomes across analysts, particularly among users with domain-specific expertise. With this article, we make this pipeline available for immediate use by the research community, and further suggest that it may contribute to the continued development of fully automated methods based on deep learning algorithms.

Prompting and Modeling of Coping Strategies during Childbirth

There is ample evidence to suggest that upright positions and mobility during labor improve birth outcome, including shorter duration of childbirth and reduced risk of cesarean section. The use of nonpharmacological interventions for pain management during childbirth are recommended by major health-care institutions and medical providers, however, the current methodologies for training coping strategies for use during labor have not shown to be effective on mobility or birth outcome. The purpose of this study was to apply an in-vivo teaching technology to the current childbirth model to prompt an imitative repertoire of empirically demonstrated labor coping strategies. Results of this study concluded that the introduction of a software using immediate prompting and video modeling increased the frequency and variability of labor behaviors during unmedicated labor for birthing persons and their partners.

Setting up a biomodeling, virtual planning, and three-dimensional printing service in Uruguay

Virtual surgical planning and three-dimensional (D) printing are rapidly becoming essential for challenging and complex surgeries around the world. An Ibero-American survey reported a lack of awareness of technology benefits and scarce financial resources as the two main barriers to widespread adoption of 3-D technologies. The Pereira Rossell Hospital Center is a publicly funded maternal and pediatric academic clinical center in Uruguay, a low-resource Latin American country, that successfully created and has been running a 3-D unit for 4 years. The present work is a step-by-step review of the 3-D technology implementation process in a hospital with minimal financial investment. References to training, software, hardware, and the management of human resources are included. Difficulties throughout the process and future challenges are also discussed.

Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information

Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that have been proven successful in many computer vision tasks, and have been previously applied to cryo-EM micrograph filtering. In this work, we demonstrate that two strategies, fine-tuning models from pretrained weights and including the power spectrum of micrographs as input, can greatly improve the attainable prediction accuracy of CNN models. The resulting software package, Miffi, is open-source and freely available for public use (https://github.com/ando-lab/miffi).

A step forward in fishway engineering: Validation and implementation of advanced algorithms for effective stepped fishway design, modeling, and retrofitting

Stepped fishways are the main alternative to allow fish migration in river obstacles. Their design is a multidisciplinary process, where civil engineering meets biology. This can bias the fishway design towards one discipline, which may cause low efficiencies or inadequate solutions. Likewise, it is often challenging to incorporate new discoveries into well-established design principles. To solve these problems, we have developed a novel tool named "Escalas". Escalas is a multipurpose platform for the assisted design, 1D simulation, assessment, and correction of stepped fishways. Escalas architecture allows fishway assessment during different hydraulic scenarios in the river (i.e., different water levels and discharges in the river), automatic dimensioning considering fish's physical needs, the study of any type of stepped fishway, to test solutions for malfunctioning or to assess fishway retrofitting. This is achieved by a modular variable definition during fishway design or definition, which allows multiple combinations of connections within and/or between cross-walls and independent discharge equation definition. This work aims to introduce Escalas to the research and engineering community, describe its algorithms, and show and validate its performance by its use in real and practical cases. Among others, results demonstrate how the tool can reproduce uniform and non-uniform performances on stepped fishways and allows fishway retrofitting to make hydraulic conditions compatible with fish usage during different river scenarios. Therefore, this work represents a step forward in the fishway engineering discipline by applying methods of engineering informatics and providing a technical and scientific base to make engineering decision-making more reliable and accessible as well as to incorporate new advances in fishway research into the engineering design process.

Rapid and sensitive detection of genome contamination at scale with FCS-GX

Assembled genome sequences are being generated at an exponential rate. Here we present FCS-GX, part of NCBI's Foreign Contamination Screen (FCS) tool suite, optimized to identify and remove contaminant sequences in new genomes. FCS-GX screens most genomes in 0.1-10 min. Testing FCS-GX on artificially fragmented genomes demonstrates high sensitivity and specificity for diverse contaminant species. We used FCS-GX to screen 1.6 million GenBank assemblies and identified 36.8 Gbp of contamination, comprising 0.16% of total bases, with half from 161 assemblies. We updated assemblies in NCBI RefSeq to reduce detected contamination to 0.01% of bases. FCS-GX is available at https://github.com/ncbi/fcs/ or https://doi.org/10.5281/zenodo.10651084 .

LASTU: A psycholinguistic search tool for Finnish lexical stimuli

LASTU is a tool for searching for Finnish language stimulus words for psycholinguistic studies. The tool allows the user to query a number of properties, including forms, lemmas, frequencies, and morphological features. It also includes two new measures for quantifying lemma and form ambiguity. The tool is written in Python and is available for Windows and macOS platforms. It is available at https://osf.io/j8v6b/ . Included with the tool is a database based on a massive corpus of dependency-parsed Finnish language data crawled from the Internet (over 5 billion tokens). While LASTU has been developed for researchers working on the Finnish language, the openly available implementation can also be applied to other languages.

A quality improvement study of the implementation and initial results of a pragmatic clinical decision support system in the community pharmacy setting

BACKGROUND

A six year collaboration between academics, community pharmacists and informaticians, led to the development of nine guidelines for a clinical decision support system, enhancing community pharmacists' ability to address drug-related problems and improve care.

AIM

The objective of this study was to assess the effectiveness of clinical decision support system rules in enhancing medication management within the community pharmacy setting. This was achieved through retrospective monitoring of real-world usage and measuring the pharmacotherapeutic impact of the rules.

METHOD

In 2019, a retrospective observational evaluation appraised the acceptance rate of the clinical decision support system components in 490 Belgian pharmacies. Among these, 51 pharmacies underwent a longitudinal analysis involving (i) co-prescription of methotrexate and folic acid, (ii) gastroprotection with non-steroidal anti-inflammatory drugs, and (iii) drug combinations causing QT prolongation. The study period spanned one year pre-launch, one year post-launch, and two years post-launch.

RESULTS

Of the targeted pharmacies, 80% used 7 of the 9 rules. After four years, methotrexate-folic acid co-prescription increased 4%, reaching 79.8%. Gastroprotection improved by 3% among older patients and 7.47% in younger individuals (< 70 year) with multiple risk factors. The QT prolongation rules faced implementation difficulties.

CONCLUSION

Pharmacists' acceptance of the developed rules was high and coincided with a decline in drug-related problems, holding potential public health impact. This real-world data can inform the future implementation of such systems, as it demonstrated the need for more detailed data-gathering and more intensive training of pharmacists in the handling of more complex problems such as QT prolongation.

Inter-software and inter-threshold reliability of quantitative paraspinal muscle segmentation

PURPOSE

Changes in the cross-sectional area (CSA) and functional cross-sectional area (FCSA) of the lumbar multifidus (MF) and erector spinae muscles (ES) are factors that can contribute to low back pain. For the assessment of muscle CSA and composition there are various software and threshold methods used for tissue segmentation in quantitative analysis. However, there is currently no gold standard for software as well as muscle segmentation. This study aims to analyze the measurement error between different image processing software and different threshold methods for muscle segmentation.

METHODS

Magnetic resonance images (MRI) of 60 patients were evaluated. Muscle CSA and FCSA measurements were acquired from axial T2-weighted MRI of the MF and ES at L4/L5 and L5/S1. CSA, FCSA, and FCSA/CSA ratio were measured independently by two observers. The MRI images were measured using two different software programs (ImageJ and Amira) and with two threshold methods (Circle/Overlap method) for each software to evaluate FCSA and FCSA/CSA ratio.

RESULTS

Inter-software comparisons revealed high inter-rater reliability. However, poor inter-rater reliability were obtained with different threshold methods. CSA, FCSA, and FCSA/CSA showed excellent inter-software agreement of 0.75-0.99 regardless of the threshold segmentation method. The inter-rater reliability between the two observers ranged between 0.75 and 0.99. Comparison of the two segmentation methods revealed agreement between 0.19 and 0.84. FCSA and FCSA/CSA measured via the Overlap method were significantly higher than those measured via the Circle method (P < 0.01).

CONCLUSION

The present study showed a high degree of reliability with very good agreement between the two software programs. However, study results based on different threshold methods should not be directly compared.

eCerto-versatile software for interlaboratory data evaluation and documentation during reference material production

The statistical tool eCerto was developed for the evaluation of measurement data to assign property values and associated uncertainties of reference materials. The analysis is based on collaborative studies of expert laboratories and was implemented using the R software environment. Emphasis was put on comparability of eCerto with SoftCRM, a statistical tool based on the certification strategy of the former Community Bureau of Reference. Additionally, special attention was directed towards easy usability from data collection through processing, archiving, and reporting. While the effects of outlier removal can be flexibly explored, eCerto always retains the original data set and any manipulation such as outlier removal is (graphically and tabularly) documented adequately in the report. As a major reference materials producer, the Bundesanstalt für Materialforschung und -prüfung (BAM) developed and will maintain a tool to meet the needs of modern data processing, documentation requirements, and emerging fields of RM activity. The main features of eCerto are discussed using previously certified reference materials.

CT angiography prior to endovascular procedures: can artificial intelligence improve reporting?

CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for evaluation of aortic dimensions and access sites. A detailed report is crucial to a proper planning. We assessed Artificial Intelligence (AI)-algorithm accuracy to measure vessels diameters at CT prior to transcatheter aortic valve implantation (TAVI). CT scans of 50 patients were included. Two Radiologists with experience in vascular imaging together manually assessed diameters at nine landmark positions according to the American Heart Association guidelines: 450 values were obtained. We implemented TOST (Two One-Sided Test) to determine whether the measurements were equivalent to the values obtained from the AI algorithm. When the equivalence bound was a range of ± 2 mm the test showed equivalence for every point; if the range was equal to ± 1 mm the two measurements were not equivalent in 6 points out of 9 (p-value > 0.05), close to the aortic valve. The time for automatic evaluation (average 1 min 47 s) was significantly lower compared with manual measurements (5 min 41 s) (p < 0.01). In conclusion, our results indicate that AI-algorithms can measure aortic diameters at CT prior to endovascular surgery with high accuracy. AI-assisted reporting promises high efficiency, reduced inter-reader variabilities and time saving. In order to perform optimal TAVI procedure planning aortic root analysis could be improved, including annulus dimensions.

Impact of different CAD software programs on marginal and internal fit of provisional crowns: An in vitro study

Statement of the problem

Different CAD software programs used for designing crowns show variations in marginal and internal fit. Marginal and internal discrepancies may cause poorly fitting crowns.

Purpose

The aim of this study was to compare the marginal and internal fit of single crown temporary restorations designed using three different CAD software programs.

Materials and methods

Dentbird, Exocad and Inlab 20 were used to design temporary single crowns using the same cement gap. Three experimental groups (n = 10/group) were formed based on the CAD software used. Geomagic Control X three-dimensional analysis software was used to compare the marginal and internal fit among the groups. Measurements were obtained at nine different thickness points. IBM SPSS Statistics, version 22 was used for all statistical analyses.

Results

Among the CAD software programs tested, Dentbird produced the best internal fit on the buccal surface and the best marginal fit on both buccal and mesial surfaces. Exocad achieved the best values on the distal surface for both internal and marginal fit, while Inlab showed the best values on the mesial surface for internal fit and on the palatal surface for marginal fit.

Conclusions

The Dentbird CAD software program provided the most accurate fit values that closely matched the design. The marginal and internal fit oftemporary crowns may vary depending on the CAD software used.

PoMeLo: a systematic computational approach to predicting metabolic loss in pathogen genomes

BACKGROUND

Genome streamlining, the process by which genomes become smaller and encode fewer genes over time, is a common phenomenon among pathogenic bacteria. This reduction is driven by selection for minimized energy expenditure in a nutrient-rich environment. As pathogens evolve to become more reliant on the host, metabolic genes and resulting capabilities are lost in favor of siphoning metabolites from the host. Characterizing genome streamlining, gene loss, and metabolic pathway degradation can be useful in assessing pathogen dependency on host metabolism and identifying potential targets for host-directed therapeutics.

RESULTS

PoMeLo (Predictor of Metabolic Loss) is a novel evolutionary genomics-guided computational approach for identifying metabolic gaps in the genomes of pathogenic bacteria. PoMeLo leverages a centralized public database of high-quality genomes and annotations and allows the user to compare an unlimited number of genomes across individual genes and pathways. PoMeLo runs locally using user-friendly prompts in a matter of minutes and generates tabular and visual outputs for users to compare predicted metabolic capacity between groups of bacteria and individual species. Each pathway is assigned a Predicted Metabolic Loss (PML) score to assess the magnitude of genome streamlining. Optionally, PoMeLo places the results in an evolutionary context by including phylogenetic relationships in visual outputs. It can also initially compute phylogenetically-weighted mean genome sizes to identify genome streamlining events. Here, we describe PoMeLo and demonstrate its use in identifying metabolic gaps in genomes of pathogenic Treponema species.

CONCLUSIONS

PoMeLo represents an advance over existing methods for identifying metabolic gaps in genomic data, allowing comparison across large numbers of genomes and placing the resulting data in a phylogenetic context. PoMeLo is freely available for academic and non-academic use at https://github.com/czbiohub-sf/pomelo .

Clinical validation of a deep-learning-based bone age software on healthy Korean children

Purpose

Bone age is needed to assess developmental status and growth disorders. We aimed to evaluate the clinical performance of a deep learning-based bone age software on the chronological age of healthy Korean children.

Methods

This retrospective study included 371 healthy children (217 boys, 154 girls), aged between 4 and 17 years, who visited the department of Pediatrics for health check-ups between January 2017 and December 2018. A total of 553 left-hand radiographs of 371 healthy Korean children were evaluated using a commercial deep learning-based bone age software (BoneAge, Vuno, Seoul, Korea). The clinical performance of the deep learning software was determined using the concordance rate and Bland-Altman analysis via comparison with the chronological age.

Results

A two-sample t-test (P < 0.001) and Fisher's exact test (P = 0.011) showed a significant difference between the normal chronological age and the bone age estimated by the deep learning software. There was a good correlation between the two variables (r = 0.96, P < 0.001); however, the root mean square error was 15.4 months. With a 12-month cut-off, the concordance rate was 58.8%. The Bland-Altman plot showed that the deep learning software tended to underestimate the bone age compared with the chronological age, especially in children under the age of 8.3 years.

Conclusion

The deep learning-based bone age software showed a low concordance rate and a tendency to underestimate the bone age in healthy Korean children.

RiboGraph: An interactive visualization system for ribosome profiling data at read length resolution

Ribosome profiling is a widely-used technique for measuring ribosome occupancy at nucleotide resolution. However, the need to analyze this data at nucleotide resolution introduces unique challenges in data visualization and analyses. In this study, we introduce RiboGraph, a dedicated visualization tool designed to work with .ribo files, a specialized and efficient format for ribosome occupancy data. Unlike existing solutions that rely on large alignment files and time-consuming preprocessing steps, RiboGraph operates on a purpose designed compact file type and eliminates the need for data preprocessing. This efficiency allows for interactive, real-time visualization at ribosome-protected fragment length resolution. By providing an integrated toolset, RiboGraph empowers researchers to conduct comprehensive visual analysis of ribosome occupancy data.

Availability and Implementation

Source code, step-by-step installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling/ribograph. On the same page, we provide test files and a step-by-step tutorial highlighting the key features of RiboGraph.

A Novel Adductomics Workflow Incorporating FeatureHunter Software: Rapid Detection of Nucleic Acid Modifications for Studying the Exposome

Exposure to the physicochemical agents that interact with nucleic acids (NA) may lead to modification of DNA and RNA (i.e., NA modifications), which have been associated with various diseases, including cancer. The emerging field of NA adductomics aims to identify both known and unknown NA modifications, some of which may also be associated with proteins. One of the main challenges for adductomics is the processing of massive and complex data generated by high-resolution tandem mass spectrometry (HR-MS/MS). To address this, we have developed a software called "FeatureHunter", which provides the automated extraction, annotation, and classification of different types of key NA modifications based on the MS and MS/MS spectra acquired by HR-MS/MS, using a user-defined feature list. The capability and effectiveness of FeatureHunter was demonstrated by analyzing various NA modifications induced by formaldehyde or chlorambucil in mixtures of calf thymus DNA, yeast RNA and proteins, and by analyzing the NA modifications present in the pooled urines of smokers and nonsmokers. The incorporation of FeatureHunter into the NA adductomics workflow offers a powerful tool for the identification and classification of various types of NA modifications induced by reactive chemicals in complex biological samples, providing a valuable resource for studying the exposome.

Evaluation of a new predictive equation for automated calculation of size-specific dose estimate (SSDE) in CT imaging

INTRODUCTION

The adoption of size-specific dose estimate (SSDE) in clinical practice is still limited owing to the tedious and complex manual measurement of individual patient size for the clinical calculation of SSDE. Thus, the automation of SSDE is imperative. This study aims to evaluate a predictive equation for the automated calculation of SSDE.

METHODS

A user-friendly software was developed to accurately predict the individual size-specific dose estimation of paediatric patients undergoing computed tomography (CT) scans of the head, thorax, and abdomen. The software includes a calculation equation developed based on a novel SSDE prediction equation that used a population's pre-determined percentage difference between volume-weighted computed tomography dose index (CTDIvol) and SSDE with age. American Association of Physicists in Medicine (AAPM RPT 204) method (manual) and segmentation-based SSDE calculators (indoseCT and XXautocalc) were used to assess the proposed software predictions comparatively.

RESULTS

The results of this study show that the automated equation-based calculation of SSDE and the manual and segmentation-based calculation of SSDE are in good agreement for patients. The differences between the automated equation-based calculation of SSDE and the manual and segmentation-based calculation are less than 3%.

CONCLUSION

This study validated an accurate SSDE calculator that allows users to enter key input values and calculate SSDE.

IMPLICATION FOR PRACTICE

The automated equation-based SSDE software (PESSD) seems a promising tool for estimating individualised CT doses during CT scans.

Validation of a built-in software in automatically reconstructing the tomographic images of the levator ani muscle

INTRODUCTION AND HYPOTHESIS

Transperineal ultrasound (TPUS) is an effective tool for evaluating the integrity of the levator ani muscle (LAM). Several operating steps are required to obtain the standard multi-slice image of the LAM, which is experience dependent and time consuming. This study was aimed at evaluating the feasibility and reproducibility of the built-in software, Smart-pelvic™, in reconstructing standard tomographic images of LAM from 3D/4D TPUS volumes.

METHODS

This study was conducted at a tertiary teaching hospital, enrolling women who underwent TPUS. Tomographic images of the LAM were automatically reconstructed by Smart-pelvicTM and rated by two experienced observers as standard or nonstandard. The anteroposterior diameter (APD) of the levator hiatus was also measured on the mid-sagittal plane of the automatically and manually reconstructed images. The APD measurements of each approach were compared using Bland-Altman plots, and interclass correlation coefficient (ICC) was used to evaluate intra- and inter-observer reproducibility. Meanwhile, the time taken for the reconstruction process of both methods was also recorded.

RESULTS

The ultrasound volume of a total of 104 patients were included in this study. Using Smart-pelvicTM, the overall success rate of the tomographic image reconstruction was 98%. Regarding measurements of APD, the ICC between the automatic and manual reconstruction methods was 0.99 (0.98, 0.99). The average time taken for reconstruction per case was 2.65 ± 0.52 s and 22.08 ± 3.45 s, respectively.

CONCLUSIONS

Using Smart-pelvicTM to reconstruct tomographic images of LAM is feasible, and it can promote TPUS by reducing operator dependence and improving examination efficiency in a clinical setting.

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022

OBJECTIVES

To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022.

MATERIALS AND METHODS

Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis.

RESULTS

The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings.

CONCLUSION

The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties.

CLINICAL RELEVANCE STATEMENT

The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products.

KEY POINTS

• The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.

Automated Feeder-Detection Software for Renal Cell Carcinoma Embolization: A Retrospective Evaluation of Detection Rate Using Transarterial Time-Resolved Computed Tomography Angiography

PURPOSE

To evaluate the detection rate of feeding arteries in renal cell carcinoma with automated feeder-detection software and determine the optimal imaging phase for accurate feeder detection with transarterial time-resolved computed tomography angiography.

MATERIALS AND METHODS

The performance of automated feeder-detection software was retrospectively evaluated using transarterial renal time-resolved computed tomography angiography images of 15 renal cell carcinomas (mean size, 22.1 mm); the images were obtained via the renal artery using a hybrid angio-CT system with 320-row computed tomography, across nine phases with 0.5-s intervals over a contrast delay time of 1.0-5.0 s. Automated feeder-detection software was applied to each phase in all tumors (135 image series in total). The feeder-detection rate (i.e., sensitivity) in each phase was evaluated, and the number of false feeders demonstrated by the software was counted for each tumor.

RESULTS

A total of 22 feeders were identified. The feeder-detection rate was the highest (95.5% [21/22]) at delay times of 1.5 s and 2.0 s and lower in later phases. At delay times of 1.0 s and 1.5 s, the software demonstrated no or only a few (≤ 3) false feeders in 93.3% (14/15) of the tumors. In later phases, however, many (≥ 4) false feeders were observed in > 50% of tumors.

CONCLUSION

The automated feeder-detection software showed a favorable feeder-detection rate and may be useful in transarterial embolization for renal cell carcinoma. The optimal delay time to avoid the demonstration of false feeders and achieve a high detection accuracy was 1.5 s.

LEVEL OF EVIDENCE IV

Case Series.

Accuracy of 3-D Surface Rendering of 2-D Ultrasound Images of the Uterus Using a Novel Software in Mapping Uterine Fibroids

OBJECTIVE

Three-dimensional surface rendering of 2-D ultrasound images of the uterus in mapping uterine fibroids is a fast-evolving imaging technique that holds great potential for gynecology. The purpose of this study was to assess the accuracy of 3-D surface rendering of 2-D ultrasound images of the uterus using a new Fibroid Mapping Reviewer Application (FMRA) software for mapping uterine fibroids as compared with the pathological evaluation of uterine fibroids in pre-menopausal women undergoing hysterectomy.

METHODS

We enrolled women aged 35-55 y scheduled for hysterectomy for symptomatic fibroids at a tertiary care hospital from 2019 to 2021. Per pre-set guidelines, we recorded 2-D images and videos of the uterus with fibroids during the transvaginal ultrasound. The recordings were transferred through USB, loaded in the FMRA software and post-processed to generate a 3-D rendered uterus model. An experienced pathologist assessed and documented the gross examination details per a set protocol. We compared the pre-specified dimensions related to the size (L1, L2) and location (X, Y) of fibroids between the 3-D model and the pathologist's assessment of the hysterectomy specimen.

RESULTS

A total of 25 fibroids in 25 women, the single largest per woman, were considered for analysis. The two methods had good correlation with respect to size (for L1, R² = 0.9723, and for L2, R² = 0.9784) and location (for X, R² = 0.9618, and for Y, R² = 0.9753). Inter-observer analysis revealed that measurements from two sonologists were reproducible (Cronbach's α = 0.9 for the L1, L2 and L3 dimensions of fibroids from the 3-D model).

CONCLUSION

The FMRA is a novel tool for mapping fibroids. With its proven accuracy, it will be helpful in planning surgeries and during guided procedures for managing uterine fibroids.

eHealth tools to assess the neurological function for research, in absence of the neurologist - a systematic review, part I (software)

BACKGROUND

Neurological disorders remain a worldwide concern due to their increasing prevalence and mortality, combined with the lack of available treatment, in most cases. Exploring protective and risk factors associated with the development of neurological disorders will allow for improving prevention strategies. However, ascertaining neurological outcomes in population-based studies can be both complex and costly. The application of eHealth tools in research may contribute to lowering the costs and increase accessibility. The aim of this systematic review is to map existing eHealth tools assessing neurological signs and/or symptoms for epidemiological research.

METHODS

Four search engines (PubMed, Web of Science, Scopus & EBSCOHost) were used to retrieve articles on the development, validation, or implementation of eHealth tools to assess neurological signs and/or symptoms. The clinical and technical properties of the software tools were summarised. Due to high numbers, only software tools are presented here.

FINDINGS

A total of 42 tools were retrieved. These captured signs and/or symptoms belonging to four neurological domains: cognitive function, motor function, cranial nerves, and gait and coordination. An additional fifth category of composite tools was added. Most of the tools were available in English and were developed for smartphone device, with the remaining tools being available as web-based platforms. Less than half of the captured tools were fully validated, and only approximately half were still active at the time of data collection.

INTERPRETATION

The identified tools often presented limitations either due to language barriers or lack of proper validation. Maintenance and durability of most tools were low. The present mapping exercise offers a detailed guide for epidemiologists to identify the most appropriate eHealth tool for their research.

FUNDING

The current study was funded by a PhD position at the University of Groningen. No additional funding was acquired.

Providing a framework for evaluation disease registry and health outcomes Software: Updating the CIPROS checklist

BACKGROUND AND AIMS

Properly designed and implemented registry systems play an important role in improving health outcomes and reducing care costs, and can provide a true representation of clinical practice, disease outcomes, safety, and efficacy. Therefore, the aim of this study was to redesign and develop a checklist with items for a patient registry software system (CIPROS) Checklist.

METHOD

The study is descriptive-cross-sectional. The extraction of the data elements of the checklist was first done through a comprehensive review of the texts in PubMed, Science Direct and Scopus databases and receiving articles related to the evaluation of registry systems. Based on the extracted data, a five-point Likert scale questionnaire was created and 30 experts in this field were asked for their opinions using the two-step Delphi method.

RESULTS

A total of 100 information items were determined as a registry software evaluation checklist. This checklist included 12 groups of software architecture factors, development, interfaces and interactivity, semantics and standardization, internationality, data management, data quality and usability, data analysis, security, privacy, organizational, education and public factors.

CONCLUSION

By using the results of this research, it is possible to identify the defects and possible strengths of the registry software and put it at the disposal of the relevant officials to make a decision in this field. In this way, among the designers and developers of these softwares, the best and most appropriate ones are selected with the needs of the registry programs.

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

Shennong: A Python toolbox for audio speech features extraction

We introduce Shennong, a Python toolbox and command-line utility for audio speech features extraction. It implements a wide range of well-established state-of-the-art algorithms: spectro-temporal filters such as Mel-Frequency Cepstral Filterbank or Predictive Linear Filters, pre-trained neural networks, pitch estimators, speaker normalization methods, and post-processing algorithms. Shennong is an open source, reliable and extensible framework built on top of the popular Kaldi speech processing library. The Python implementation makes it easy to use by non-technical users and integrates with third-party speech modeling and machine learning tools from the Python ecosystem. This paper describes the Shennong software architecture, its core components, and implemented algorithms. Then, three applications illustrate its use. We first present a benchmark of speech features extraction algorithms available in Shennong on a phone discrimination task. We then analyze the performances of a speaker normalization model as a function of the speech duration used for training. We finally compare pitch estimation algorithms on speech under various noise conditions.

BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

BACKGROUND AND OBJECTIVES

Electrocardiographic (ECG) and vectorcardiographic (VCG) analyses are used to diagnose current cardiovascular disease and for risk stratification for future adverse cardiovascular events. With increasing use of digital ECGs, research into novel ECG/VCG parameters has increased, but widespread computer-based ECG/VCG analysis is limited because there are no currently available, open-source, and easily customizable software packages designed for automated and reproducible analysis.

METHODS AND RESULTS

We present BRAVEHEART, an open-source, modular, customizable, and easy to use software package implemented in the MATLAB programming language, for scientific analysis of standard 12-lead ECGs acquired in a digital format. BRAVEHEART accepts a wide variety of digital ECG formats and provides complete and automatic ECG/VCG processing with signal denoising to remove high- and low-frequency artifact, non-dominant beat identification and removal, accurate fiducial point annotation, VCG construction, median beat construction, customizable measurements on median beats, and output of measurements and results in numeric and graphical formats.

CONCLUSIONS

The BRAVEHEART software package provides easily customizable scientific analysis of ECGs and VCGs. We hope that making BRAVEHART available will allow other researchers to further the field of ECG/VCG analysis without having to spend significant time and resources developing their own ECG/VCG analysis software and will improve the reproducibility of future studies. Source code, compiled executables, and a detailed user guide can be found at http://github.com/BIVectors/BRAVEHEART. The source code is distributed under the GNU General Public License version 3.

Introduction and acceptability of the Surveillance Outbreak Response Management and Analysis System (SORMAS) during the COVID-19 pandemic in Côte d'Ivoire

BACKGROUND

The Surveillance Outbreak Response Management and Analysis System (SORMAS) has been implemented for various infectious diseases since 2015. 2020, at the beginning of the COVID-19 pandemic, SORMAS was adapted to SARS-CoV2.

METHODS

We assessed the acceptability and usability of SORMAS and accompanied its implementation in two pilot regions of Côte d'Ivoire (Abidjan 2 and Gbêkê) from July/August 2021 to March 2022. We conducted 136 semi-structured interviews to cover knowledge on COVID-19, information on conventional surveillance systems for disease monitoring including COVID-19, acceptability of SORMAS, and impact of SORMAS on epidemic preparedness and surveillance. Scores before and 6-8 months after implementation were compared.

RESULTS

SORMAS was implemented in two pilot regions in Côte d'Ivoire. The conventional software for the surveillance of the COVID-19 pandemic by the company MAGPI was maintained in parallel; the additional time needs to enter and manage the data in SORMAS were the main concern. SORMAS acceptance and satisfaction scores were high after the user training, which was prior to implementation, and after 6-8 months of use. The ability of SORMAS to improve COVID-19 preparedness and early detection of cases and contacts was widely acknowledged. To keep the understanding and skills of users up-to-date, regular refresher trainings were requested. The expectation to be able to make decisions based on data produced by SORMAS was high at baseline and the perceived experience after several months of use of the software was very positive. Unfortunately, the link with the laboratories could not be established in the pilot regions, but it is an existing feature of SORMAS that many users were asking for. Following the positive experience using SORMAS for COVID-19, the pilot regions expanded its use for monitoring and management of measles, yellow fever, meningitis, and cholera.

CONCLUSION

SORMAS was very well accepted by users and decision makers in the two pilot regions of Côte d'Ivoire and its ability to improve epidemic preparedness and surveillance was acknowledged. If the hurdles of maintenance (tablets, server, and maintaining user skills) are handled sustainably, it can serve as a valid tool to identify, surveil and manage future outbreaks of various infectious diseases in Côte d'Ivoire.

Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units

OBJECTIVE

To describe the challenges and perspectives of the automation of pain assessment in the Neonatal Intensive Care Unit.

DATA SOURCES

A search for scientific articles published in the last 10 years on automated neonatal pain assessment was conducted in the main Databases of the Health Area and Engineering Journal Portals, using the descriptors: Pain Measurement, Newborn, Artificial Intelligence, Computer Systems, Software, Automated Facial Recognition.

SUMMARY OF FINDINGS

Fifteen articles were selected and allowed a broad reflection on first, the literature search did not return the various automatic methods that exist to date, and those that exist are not effective enough to replace the human eye; second, computational methods are not yet able to automatically detect pain on partially covered faces and need to be tested during the natural movement of the neonate and with different light intensities; third, for research to advance in this area, databases are needed with more neonatal facial images available for the study of computational methods.

CONCLUSION

There is still a gap between computational methods developed for automated neonatal pain assessment and a practical application that can be used at the bedside in real-time, that is sensitive, specific, and with good accuracy. The studies reviewed described limitations that could be minimized with the development of a tool that identifies pain by analyzing only free facial regions, and the creation and feasibility of a synthetic database of neonatal facial images that is freely available to researchers.

"Instant 3D" Angiography: Novel Technique for Rapid Conversion of 2D Angiograms into 3D Stereoscopic Videos

BACKGROUND

Rotational angiography, often referred to as a "spin", is typically presented in 2D. Since rotational angiograms are composed of images acquired from multiple angles, we took advantage of this property to develop a method for converting any rotational angiogram into a 3 dimensional (3D) video.

METHODS

Our aim was to develop a low cost and easily distributable solution without requiring additional hardware or altering acquisition techniques. Six previously acquired rotational angiograms from our institution were imported using custom-written code and exported as anaglyph (red-cyan) videos.

RESULTS

The resulting 3D videos convey anatomical depth that is not apparent from viewing the 2D images alone. Processing time was 1.3 ± 0.6 s (mean ± SD) per angiogram. The only associated cost was $10 for red-cyan 3D glasses. Using our software, any rotational angiogram with at least 0.3 frames per degree of rotation can be converted into 3D.

CONCLUSIONS

Our solution is an inexpensive and rapid method for generating stereoscopic videos from existing angiograms. It does not require any additional hardware and is readily deployable in low-resource settings. Because the videos are in anaglyph format, they are viewable on any 2 dimensional (2D) display in the interventional suite or operating room, on a mobile device, or at home.

Comparison of two segmentation software tools for deep brain stimulation of the subthalamic and ventro-intermedius nuclei

PURPOSE

Deep brain stimulation (DBS) relies on precise targeting of key structures such as the subthalamic nucleus (STN) for Parkinson's disease (PD) and the ventro-intermedius nucleus of the thalamus (Vim) for essential tremor (ET). Segmentation software, such as GuideXT© and Suretune©, are commercially available for atlas-based identification of deep brain structures. However, no study has compared the concordance of the segmentation results between the two software.

METHODS

We retrospectively compared the concordance of segmentation of GuideXT© and Suretune© software by comparing the position of the segmented key structures with clinically predicted targets obtained using the newly developed RebrAIn© software as a reference.

RESULTS

We targeted the STN in 44 MRI from PD patients (88 hemispheres) and the Vim in 31 MRI from ET patients (62 hemispheres) who were elected for DBS. In 22 STN targeting (25%), the target positioning was not correlating between GuideXT© and Suretune©. Regarding the Vim, targets were located in the segmented Vim in 37%, the posterior subthalamic area (PSA) in 60%, and the STN in 3% of the cases using GuideXT©; the proportions were 34%, 60%, and 6%, respectively, using Suretune©. The mean distance from the centre of the RebrAIn© targeting to the segmented Vim by Suretune© was closer (0.64 mm) than with GuideXT© (0.96 mm; p = 0.0004).

CONCLUSION

While there is some level of concordance in the segmentation results of key structures for DBS treatment among software models, differences persist. Therefore, such software should still be considered as tools and should not replace clinician experience in DBS planning.

A novel geometrical planning method to restore knee joint obliquity in double-level osteotomies

PURPOSE

Precise preoperative planning is mandatory when a double-level osteotomy (DLO) is required to correct a severe knee deformity. Literature does not report a validated planning method regarding DLO that could be performed directly on digital radiographs using simple measurement tools. This study aims to validate a novel DLO planning method called New Mikulicz-Joint Line (NM-JL) based on essential measurement tools, in which the correction angles are induced by the predicted post-operative joint line obliquity (JLO).

METHODS

Twenty-three patients who satisfied the inclusion criteria were enrolled. NM-JL planning method was performed using basic measurement tools to detect corrective angles and gaps. The correction was then simulated using a Virtual Segmentation Software method to obtain the osteotomy fragments. Both planning procedures were performed independently and later repeated by two orthopaedic surgeons to assess the inter and intra-observer reliability.

RESULTS

The intraclass correlation coefficient (ICC) regarding corrective angles and gaps showed a significant positive correlation between the values determined using the two procedures by both raters (p < 0.05). Pearson's correlation analysis revealed a significant correlation between the measured results of the two planning methods. (p < 0.05). Finally, the Bland-Altman analysis showed an excellent agreement (p < 0.05) for all measurements performed.

CONCLUSIONS

The NM-JL method showed high values of intra and inter-rater reliability. The procedure is built up starting from the predicted value of post-operative joint line obliquity, allowing to maintain this parameter fixed. Other advantages include the quickness, adaptability, and possibility to be performed on any Digital Imaging and Communication in Medicine (DICOM) viewer.

LEVEL OF EVIDENCE

Level IV.

GutMap: A New Interface for Analysing Regional Motility Patterns in ex vivo Mouse Gastrointestinal Preparations

Different regions of the gastrointestinal tract have specific functions and thus distinct motility patterns. Motility is primarily regulated by the enteric nervous system (ENS), an intrinsic network of neurons located within the gut wall. Under physiological conditions, the ENS is influenced by the central nervous system (CNS). However, by using ex vivo organ bath experiments, ENS regulation of gut motility can also be studied in the absence of CNS influences. The current technique enables the characterisation of small intestinal, caecal, and colonic motility patterns using an ex vivo organ bath and video imaging protocol. This approach is combined with the novel edge detection script GutMap, available in MATLAB, that functions across Windows and Mac platforms. Dissected intestinal segments are cannulated in an organ bath containing physiological saline with a camera mounted overhead. Video recordings of gut contractions are then converted to spatiotemporal heatmaps and analysed using the GutMap software interface. Using data analysed from the heatmaps, parameters of contractile patterns (including contraction propagation frequency and velocity as well as gut diameter) at baseline and in the presence of drugs/treatments/genetic mutations can be compared. Here, we studied motility patterns of female mice at baseline and in the presence of a nitric oxide synthase inhibitor (Nω-Nitro-L-arginine; NOLA) (nitric oxide being the main inhibitory neurotransmitter of gut motility) to showcase the application of GutMap. This technique is suitable for application to a broad range of animal models of clinical disorders to understand underlying biological pathways contributing to gastrointestinal dysfunction. Key features • Enhanced video imaging analysis of gut contractility in rodents using a novel software interface. • New edge detection algorithm to accurately contour curvatures of the gastrointestinal tract. • Allows for output of high-resolution spatiotemporal heatmaps across Windows and Mac platforms. • Edge detection and analysis method makes motility measurements accessible in different gut regions including the caecum and stomach.