Similar Precision Research Articles

An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor

The segmentation of pancreas and pancreatic tumor remain a persistent challenge for radiologists. Consequently, it is essential to develop automated segmentation methods to address this task. U-Net based models are most often used among various deep learning-based techniques in tumor segmentation. This paper introduces an innovative hybrid two-stage U-Net model for segmenting both the pancreas and pancreatic tumors. The optimization technique, used in this approach, involves a combination of meta-heuristic optimization algorithms namely, Grey Wolf Border Collie Optimization (GWBCO) technique, combining the Grey Wolf Optimization algorithm and the Border Collie Optimization algorithm. Our approach is evaluated using key parameters, such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), sensitivity, specificity and precision to assess its effectiveness and achieves a DSC of 93.33 % for pancreas segmentation. Additionally, the model also achieves high DSC of 91.46 % for pancreatic tumor segmentation. This method helps in improving the diagnostic accuracy and assists medical professionals to provide treatment at an early stage with precise intervention. The method offers•Two-stage U-Net model addresses both pancreas and tumor segmentation.•Combination of two metaheuristic optimization algorithms, Grey Wolf and Border Collie for enhanced performance.•High dice similarity coefficient for pancreas and tumor segmentation.

Comparison of ultra-wide band microwave system and ultrasound in live cattle to predict beef carcase subcutaneous fatness

Ultrasound and ultrawide band microwave system (MiS) were directly compared in their ability to scan live cattle to predict carcase traits. Commercial beef cattle (n = 315) were scanned on farm 0–14 days prior to slaughter. Traits measured were subcutaneous fatness at the P8 site (over the gluteus muscle on the rump, at the intersection of a line through the pin bone parallel to the chine and perpendicular through the 3rd sacral crest) and subcutaneous fatness at the rib fat site (between 12th & 13th rib, ¾ of the length ventrally over the longissimus muscle). The precision of prediction of carcase traits was slightly better using MiS. MiS prediction of P8 fat depth had an average RMSEP of 2.48 mm and R2 of 0.65. The MiS could predict carcase rib fat with an average RMSEP of 2.28 mm and R2 of 0.56. The accuracy of prediction was very similar between the two technologies. When predicting P8, the average bias was smallest using MiS at 0.157 mm, but the average slope was smallest using ultrasound at 0.03 mm. When predicting rib fat, MiS had the smallest average bias at 0.204 mm, and smallest average slope deviation at 0.06 mm. The MiS predicted P8 and rib fat carcase traits with similar precision and accuracy as ultrasound.

Fast vs slow rubidium-82 infusion profiles and test-retest precision of myocardial perfusion using contemporary 3D cardiac analog PET-CT imaging.

BackgroundOn legacy 2D PET systems utilizing a 50 mL/min Rb-82 profile, test-retest precision of quantitative perfusion is ∼10%. It is unclear whether Rb-82 infusion rate significantly impacts quantitative perfusion and/or image quality on modern analog 3D PET-CT systems. We aimed to determine whether the Rb-82 infusion profile significantly impacts test-retest precision of quantitative perfusion, perfusion metrics, and/or image quality on a modern analog 3D PET-CT scanner. MethodsNinety-eight volunteers from 3 distinct groups: healthy volunteers (Normals), patients with risk factors and/or coronary disease (Clinicals) and patients with prior transmural myocardial infarctions (Infarcts), underwent cardiac stress testing on an analog 3D PET-CT. Participants received 3 consecutive resting scans and 2 consecutive stress scans, minutes apart, with two randomly assigned Rb-82 infusion profiles: 50 mL/min (fast [F]) and 20 mL/min (slow [S]). Perfusion metrics (resting (rMBF) and stress myocardial blood flow (sMBF)) were calculated using HeartSee software. Coefficients of variance (COV), repeatability (RC), MBF and image quality metrics were compared. ResultsrMBF correlated well between F and S profiles, with intraclass correlation coefficients (ICC) ranging 0.91-0.93. sMBF was highly correlated between F and S profiles (ICC=0.97). Fast and slow profiles were associated with similar same-day test-retest precision (COV 11.5% vs. 11.3% (p=0.77); RC 21.5% vs. 22.6%, for F-F vs S-S). There were no clinically significant differences in MBF values between F and S profiles. Image quality metrics were similar between the 2 profiles. ConclusionsThere are no clinically significant differences in precision, perfusion metrics or image quality between Rb-82 fast and slow infusions using a contemporary analog 3D PET-CT.

Development and evaluation of machine-learning methods in whole-body magnetic resonance imaging with diffusion weighted imaging for staging of patients with cancer: the MALIBO diagnostic test accuracy study

Background Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants Retrospective analysis of (1) prospective single-centre study in healthy volunteers > 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.

Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification.

White matter hyperintensities (WMH) observed in T2 fluid-attenuated inversion recovery (FLAIR) images have emerged as potential markers of neurodegenerative diseases like Multiple Sclerosis (MS). Lacking comprehensive automated WMH classification systems in current research, there is a need to develop accurate detection and classification methods for WMH that will benefit the diagnosis and monitoring of brain diseases. Juxtacortical WMH (JCWMH) is a less explored subtype of WMH, primarily due to the hard definition of the cortex in FLAIR images, which is escalated by the presence of lesions to obtain appropriate gray matter (GM) masks. In this study, we present a method to perform a specialized GM segmentation developed for the classification of WMH, especially JCWMH. Using T1 and FLAIR images, we propose a pipeline to integrate masks of white matter, cerebrospinal fluid, ventricles, and WMH to create a unique mask to refine the primary GM map. Subsequently, we utilize this pipeline to generate paired data for training a conditional generative adversarial network (cGAN) to substitute the pipeline and reduce the inputs to only FLAIR images. The classification of WMH is then based on the distances between WMH and ventricular and GM masks. Due to the lack of multi-class labeled WMH datasets and the need for extensive data for training deep learning models, we attempted to collect a large local dataset and manually segment and label some data for WMH and ventricles. In JCWMH classification, the proposed method exhibited a Dice similarity coefficient, precision, and sensitivity of 0.76, 0.69, and 0.84, respectively. With values of 0.66, 0.55, and 0.81, the proposed method clearly outperformed the approach commonly used in the literature, which uses extracted GM masks from registered T1 images on FLAIR. After training, the method proves its efficiency by providing results in less than one second. In contrast, the usual approach would require at least two minutes for registration and segmentation alone. The proposed method is automated and fast and requires no initialization as it works exclusively with FLAIR images. Such innovative methods will undoubtedly facilitate accurate and meaningful analysis of WMH in clinical practice by reducing complexity and increasing efficiency.

An Automated pre-Dilution Setup for Von Willebrand Factor Activity Assays.

Accurate quantification of von Willebrand factor ristocetin cofactor activity (VWF:RCo) is critical for the diagnosis and classification of von Willebrand disease, the most common hereditary and acquired bleeding disorder in humans. Moreover, it is important to accurately assess the function of von Willebrand factor (VWF) concentrates within the pharmaceutical industry to provide consistent and high-quality biopharmaceuticals. Although the performance of VWF:RCo assay has been improved by using coagulation analyzers, which are specialized devices for blood and blood plasma samples, scientists still report a high degree of intra- and inter-assay variation in clinical laboratories. Moreover, high, manual sample dilutions are required for VWF:RCo determination of VWF concentrates within the pharmaceutical industry, which are a major source for assay imprecision. For the first time, we present a precise and accurate method to determine VWF:RCo, where all critical pipetting and mixing steps are automated. A pre-dilution setup was established on CyBio FeliX (Analytik-Jena) liquid handling system, and an adapted VWF:RCo method on BCS-XP analyzer (Siemens) is used. The automated pre-dilution method was executed on three different, most frequently used coagulation analyzers and compared to manual pre-dilutions performed by an experienced operator. Comparative sample testing revealed a similar assay precision (coefficient of variation = 5.9% automated, 3.1% manual pre-dilution) and no significant differences between the automated approach and manual dilutions of an expert in this method. While no outliers were generated with the automated procedure, the manual pre-dilution resulted in an error rate of 8.3%. Overall, this operator-independent protocol enables standardization and offers an efficient way of fully automating VWF activity assays, while maintaining the precision and accuracy of an expert analyst. Key features • Automated pre-dilution setup for von Willebrand factor concentrates of various natures. • Combination of a liquid handling system (CyBio FeliX) with a coagulation analyzer (BCS-XP). • Simplifies method transfer to other laboratories. • Basic training for CyBio FeliX and BCS-XP is required. Graphical overview VWF:RCo assay principle and measurement setup. Platelets (yellow ellipsoids) with negative surface charge (- - -) are treated with formaldehyde, which partly denatures the cell surface and thus stabilizes platelets for use as assay reagents. Stabilized platelets (dark-yellow-framed yellow ellipsoids) are then brought in contact with ristocetin A (chemical structure shown; black dots), which binds to the platelet surface and facilitates binding of VWF (green circles). The graphs show an example of quantitative determination of platelet agglutination by measurement of light transmission, where increasing amounts of VWF increase light transmission over time. The photo in the left-bottom corner shows the CyBio FeliX setup for VWF sample dilution and the photo in the right-bottom corner displays the BCS-XP system, which is used for VWF:RCo measurements.

Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study

Point-of-care (POC) high-sensitivity cardiac troponin (hs-cTn) assays have been shown to provide similar analytical precision despite substantially shorter turnaround times compared with laboratory-based hs-cTn assays. We applied the previously developed machine learning based personalised Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) algorithm, which can predict the individual probability of myocardial infarction, with a single POC hs-cTn measurement, and compared its diagnostic performance with standard-of-care pathways for rapid rule-out of myocardial infarction. We retrospectively analysed pooled data from consecutive patients of two prospective observational cohorts in geographically distinct regions (the Safe Emergency Department Discharge Rate cohort from the USA and the Suspected Acute Myocardial Infarction in Emergency cohort from Australia) who presented to the emergency department with suspected myocardial infarction. Patients with ST-segment elevation myocardial infarction were excluded. Safety and efficacy of direct rule-out of myocardial infarction by the ARTEMIS algorithm (at a pre-specified probability threshold of <0·5%) were compared with the European Society of Cardiology (ESC)-recommended and the American College of Cardiology (ACC)-recommended 0 h pathways using a single POC high-sensitivity cardiac troponin I (hs-cTnI) measurement (Siemens Atellica VTLi as investigational assay). The primary diagnostic outcome was an adjudicated index diagnosis of type 1 or type 2 myocardial infarction according to the Fourth Universal Definition of Myocardial Infarction. The safety outcome was a composite of incident myocardial infarction and cardiovascular death (follow-up events) at 30 days. Additional analyses were performed for type I myocardial infarction only (secondary diagnostic outcome), and for each cohort separately. Subgroup analyses were performed for age (<65 years vs ≥65 years), sex, symptom onset (≤3 h vs >3 h), estimated glomerular filtration rate (<60 mL/min per 1·73 m2vs ≥60 mL/min per 1·73 m2), and absence or presence of arterial hypertension, diabetes, a history of coronary artery disease, myocardial infarction, or heart failure, smoking, and ischaemic electrocardiogram signs. Among 2560 patients (1075 [42%] women, median age 58 years [IQR 48·0-69·0]), prevalence of myocardial infarction was 6·5% (166/2560). The ARTEMIS-POC algorithm classified 899 patients (35·1%) as suitable for rapid rule-out with a negative predictive value of 99·96% (95% CI 99·64-99·96) and a sensitivity of 99·68% (97·21-99·70). For type I myocardial infarction only, negative predictive value and sensitivity were both 100%. Proportions of missed index myocardial infarction (0·05% [0·04-0·42]) and follow-up events at 30 days (0·07% [95% CI 0·06-0·59]) were low. While maintaining high safety, the ARTEMIS-POC algorithm identified more than twice as many patients as eligible for direct rule-out compared with guideline-recommended ESC 0 h (15·2%) and ACC 0 h (13·8%) pathways. Superior efficacy persisted across all clinically relevant subgroups. The patient-tailored, medical decision support ARTEMIS-POC algorithm applied with a single POC hs-cTnI measurement allows for very rapid, safe, and more efficient direct rule-out of myocardial infarction than guideline-recommended pathways. It has the potential to expedite the safe discharge of low-risk patients from the emergency department including early presenters with symptom onset less than 3 h at the time of admission and might open new opportunities for the triage of patients with suspected myocardial infarction even in ambulatory, preclinical, or geographically isolated care settings. The German Center for Cardiovascular Research (DZHK).

The Completeness of Accreting Neutron Star Binary Candidates from the Chinese Space Station Telescope

Abstract A neutron star (NS) has many extreme physical conditions, and one may obtain some important information about an NS via accreting neutron star binary (ANSB) systems. The upcoming Chinese Space Station Telescope (CSST) provides an opportunity to search for a large sample of ANSB candidates. Our goal is to check the completeness of the potential ANSB samples from CSST data. In this paper, we generate some ANSBs and normal binaries under the CSST photometric system by binary evolution and binary population synthesis method and use a machine learning method to train a classification model. Although the Precision (94.56%) of our machine learning model is as high as before study, the Recall is only about 63.29%. The Precision/Recall is mainly determined by the mass transfer rate between the NSs and their companions. In addition, we also find that the completeness of ANSB samples from CSST photometric data by the machine learning method also depends on the companion mass and the age of the system. ANSB candidates with a low initial mass companion star (0.1 M ⊙ to 1 M ⊙) have a relatively high Precision (94.94%) and high Recall (86.32%), whereas ANSB candidates with a higher initial mass companion star (1.1 M ⊙ to 3 M ⊙) have similar Precision (93.88%) and quite low Recall (42.67%). Our results indicate that although the machine learning method may obtain a relatively pure sample of ANSBs, a completeness correction is necessary for one to obtain a complete sample.

Cost-effective fully 3D-printed on-drop electrochemical sensor based on carbon black/polylactic acid: a comparative study with screen-printed sensors in food analysis.

3D-printing technology allows scientist to fabricate easily electrochemical sensors. Until now, these sensors were designed employing a large amount of material, which increases the cost and decreases manufacturing throughput. In this work, a low-cost 3D-printed on-drop electrochemical sensor (3D-PES) was fully manufactured by fused filament fabrication, minimizing the number of printing layers. Carbon black/polylactic acid filament was employed, and the design and several printing parameters were optimized to yield the maximum electroanalytical performance using the minimal amount ofmaterial. Print speed and extrusion width showed a critical influence on the electroanalytical performance of 3D-PES. Under optimized conditions, the fabrication procedure offered excellent reproducibility (RSD 1.3% in working electrode diameter), speed (< 3min/unit), and costs (< 0.01 $ in materialcost). The 3D-PES was successfully applied to the determination of phloridzin in apple juice. The analytical performance of 3D-PES was compared with an equivalent commercial on-drop screen-printed electrode, yielding similar precision and accuracy but lower sensitivity. However, 3D-PES provides interesting features such as recyclability, biodegradability, low-cost, and the possibility of being manufactured near the point of need, some of which meets several demands of Green Chemistry. This cost-effective printing approach is a green and promising alternative for manufacturing disposable and portable electroanalytical devices, opening new possibilities not only in on-site food analysis but also in point-of-care testing.

Fourier-transform near-infrared spectroscopy first application to age determination in European fish species: the case of the Atlantic horse mackerel from the central Mediterranean Sea

Fourier-transform near-infrared spectroscopy (FT-NIRS) has recently been used to reduce the processing time for estimates of annual age in several fish species. The present study represents the first application of this technique to marine organisms from the European waters. Atlantic horse mackerel (Trachurus trachurus) from the central Mediterranean Sea was selected for its ecological role, its socioeconomic value, and because its age is regularly estimated by otolith reading under a stereomicroscope for stock assessment purposes. Absorption spectra of the whole otoliths were acquired by FT-NIRS across a multiyear dataset, obtained during acoustic surveys carried out in different regions of the central Mediterranean Sea. The acquired spectra were processed to optimize calibration models to predict age. The best linear models obtained by the optimizing procedure predicted age successfully with a coefficient of determination of 0.95–0.96, mean squared error of 0.5 years, and bias &amp;lt;0.03 years. Although regional calibration models were also obtained separately, the models with all regions combined appeared much more robust and encompassing the whole age range, suggesting its usefulness for the entire spatial distribution range considered in the preset study. The use of FT-NIRS allowed to successfully predict age for horse mackerel between the ages 1 and 14, with similar or better precision (and bias) of the traditional methods, also increasing the standardization by repeatability of age assignments. The preliminary results obtained by this study encourage further effort to fine-tune the calibration model for new species, in order to apply a precise, rapid, cost-effective method for stock assessment purposes of Mediterranean fish resources.

Physicochemical Methods for the Structuring and Assembly of MOF Crystals.

ConspectusMetal-organic frameworks (MOFs) are promising for various applications through the creation of innovative materials and assemblies. This potential stems from their modular nature, as diverse metal ions and organic linkers can be combined to produce MOFs with unique chemical properties and lattice structures. Following extensive research on the design and postsynthetic chemical modification of MOF lattices at the molecular level, increasing attention is now focused on the next hierarchical level: controlling the morphology of MOF crystals and their subsequent assembly and positioning to create functional composites.Beyond well-established methods to regulate crystal size and shape through nucleation and coordination modulation, physicochemical techniques leveraging wetting effects, interparticle interactions, and magnetic or electric fields offer attractive avenues for the hierarchical structuring and assembly of MOFs. These techniques facilitate crystal alignment and yield unique superstructures. While our research group primarily focuses on directing MOF crystal orientation and positioning using external stimuli such as magnetic and electric fields, we also explore hierarchical MOF synthesis and structuring using liquid interfaces and depletion force-assisted packing.This account highlights our journey and progress in developing methods to regulate the morphology, assembly, orientation, and positioning of MOF crystals, placed in the context of work by other groups. First, we examine commonly utilized structuring methods for MOF crystals that employ liquid-liquid and air-liquid interfaces to spatially confine reactions, allowing us to access unique morphologies such as mushroom-like crystals and Janus particles. We also discuss strategies for concentrating and packing MOF crystals into superstructures, utilizing fluid interfaces for spatial confinement of crystals, depletion forces, entropic effects, and crystal sedimentation.A particularly compelling challenge in expanding the applicability of MOF materials is how to manipulate free-standing MOF crystals. This issue is especially important because MOFs are typically produced as loose powders, and industrial material processing is generally more efficient when the material is fluidized. While extensive research has been conducted regarding MOF growth on substrates with both positional and orientational control, there is a clear need for similar precision with free-standing MOFs dispersed in a fluid matrix. Our group has thus focused on the relatively new, yet powerful approach of using electric and magnetic fields to manipulate MOF crystals, which offers unprecedented control over the orientation and positioning of dispersed MOF crystals, complementing the more well-established methods of MOF growth on substrates. In this Account, we provide foundational background and discussions on the interactions between these external fields and MOF crystals, including critical considerations for effective MOF manipulation using such techniques. We also discuss their unique advantages and applications, and briefly examine potential application areas, such as photonics, smart materials like soft robotics and absorbents, and sensing. This Account highlights the promising potential of well-organized and aligned MOF crystals over randomly oriented ones in various applications, owing to enhanced selectivity and performance. It underscores the importance of specialized assembly methods to advance materials science and engineering, encouraging the reader to explore such approaches.

Artificial intelligence-based evaluation of carotid artery compressibility via point-of-care ultrasound in determining the return of spontaneous circulation during cardiopulmonary resuscitation

AimThis study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation. MethodsA prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases. RealCAC-Net processes images in three steps: TransUNet-based segmentation, the carotid artery compressibility measurement algorithm for improved segmentation and CAC calculation, and CAC-based classification from 0 (indicating a circular shape) to 1 (indicating high compression). The accuracy of the ROSC classification model was tested using metrics such as the dice similarity coefficient, intersection-over-union, precision, recall, and F1 score. ResultsRealCAC-Net, which applied the carotid artery compressibility measurement algorithm, performed better than the baseline model in cross-validation, with an average dice similarity coefficient of 0.90, an intersection-over-union of 0.84, and a classification accuracy of 0.96. The test set achieved a classification accuracy of 0.96 and an F1 score of 0.97, demonstrating its efficacy in accurately identifying ROSC in cardiac arrest situations. ConclusionsRealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.

The use of venous blood gas in assessing arterial acid−base and oxygenation status – an analysis of aggregated data from multiple studies evaluating the venous to arterial conversion (v-TAC) method

ABSTRACT Background Several methods exist to reduce the number of arterial blood gases (ABGs). One method, Roche v-TAC, has been evaluated in different patient groups. This paper aggregates data from these studies, in different patient categories using common analysis criteria. Research design and methods We included studies evaluating v-TAC based on paired arterial and peripheral venous blood samples. Bland-Altman analysis compared measured and calculated arterial values of pH, PCO2, and PO2. Subgroup analyses were performed for normal, chronic hypercapnia and chronic base excess, acute hyper- and hypocapnia, and acute and chronic base deficits. Results 811 samples from 12 studies were included. Bias and limits of agreement for measured and calculated values: pH 0.001 (−0.029 to 0.031), PCO2 −0.08 (−0.65 to 0.49) kPa, and PO2 0.04 (−1.71 to 1.78) kPa, with similar values for all sub-group analyses. Conclusion These data suggest that v-TAC analysis may have a role in replacing ABGs, avoiding arterial puncture. Substantial data exist in patients with chronic hypercapnia and chronic base excess, acute hyper- and hypocapnia, and in patients with relatively normal acid−base status, with similar bias and precision across groups and across study data. Limited data exist for patients with acute and chronic base deficits.

Robotic-assisted total hip arthroplasty utilizing a fluoroscopy-guided system produced similar cup accuracy and precision relative to a computerized tomography-based robotic platform

Robotic-assisted total hip arthroplasty utilizing a fluoroscopy-guided system produced similar cup accuracy and precision relative to a computerized tomography-based robotic platform

Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging

Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.

Despite the improved clinical sensitivity of the Roche benzodiazepines II assay it cannot replace mass spectrometry in all patient populations

IntroductionBenzodiazepines are frequently prescribed and misused therefore urine drug screening (UDS) is performed in many patient populations. Most current benzodiazepine immunoassays have poor sensitivity, particularly for detecting the metabolites of newer benzodiazepines such as lorazepam in urine. ObjectivesWe aimed to verify the clinical performance of the new qualitative Roche Benzodiazepines II (BNZ2) immunoassay, as well as compare its performance to the Roche Benzodiazepines Plus (BENZ) assay in two patient populations: UDS in the emergency department (ED) and compliance monitoring. MethodsAn initial verification study was performed, selecting for samples containing clonazepam and lorazepam metabolites. Performance of the BNZ2 and BENZ assays was compared to liquid chromatography-tandem mass spectrometry (LC-MS/MS) as the reference method. Sensitivity, specificity, false positive rate (FPR) and false negative rate (FNR) were determined. ResultsWe verified the performance claims in the initial verification and demonstrated similar precision, with coefficient of variations (CVs) of 12.8% and 7.7% for negative and positive controls, respectively. Furthermore, we observed higher clinical sensitivity and lower FNR with the BNZ2 assay in both the ED and compliance monitoring populations due to improved cross-reactivity for lorazepam and clonazepam metabolites. Despite these improvements, the BNZ2 assay was unable to detect 27% of specimens positive by LC-MS/MS, including specimens from patients using benzodiazepines without prescription. DiscussionDue to its improved performance and rapid turnaround time, the BNZ2 assay should be implemented for UDS in the ED. However, the assay should not replace LC-MS/MS testing for compliance monitoring, as unsuspected benzodiazepine use may go undetected.

MultiTumor Analyzer (MTA-20–55): A network for efficient classification of detected brain tumors from MRI images

Brain cancer, one of the leading causes of mortality worldwide, is caused by brain tumors. Early diagnosis of tumors and predicting their progression can help doctors to save lives. In this article, we have designed an automated approach for locating and classifying tumors from MRI images. The novelties of the research work include the following two stages: Developing an encoder-decoder type 20-Layered deep neural network (DNN) named MultiTumor Analyzer (MTA-20) with 15 down-sampling layers and 4 up-sampling layers, the segmentation is performed in the initial stage. Here, we have adhered a Leaky ReLU activation function instead of ReLU which learn a parameter with negative values that may have valuable information which is essential specifically for image segmentation. Further, a 55-layered DNN using multistage feature fusion is developed in the second stage of the work for the classification of localized tumors. The classification is performed using developed MultiTumor Analyzer (MTA-55) DNN with Softmax classifier. The efficacy of the designed network is validated using highly cited quantitative measures such as accuracy, sensitivity, specificity, dice similarity coefficient (DSC), precision, and F1-measure. It is observed that the proposed MTA-20 DNN attains the average accuracy, sensitivity, specificity, DSC, and precision of 99.2 %, 94.6 %, 99.3 %, 88 %, and 82.5 % respectively against seven state-of-the-art techniques. Also, it is found that, the proposed MTA-55 DNN provides the overall accuracy, recall, specificity, F1-measure, precision, and DSC of 99.8 %, 99.633 %, 99.844 %, 99.659 %, 99.689 %, and 99.656 % respectively as compared to thirteen state-of-the-art techniques. These results corroborate the superiority of the proposed technique.

Comparing Actual and Rounded Serum Creatinine Concentration for Assessing the Accuracy of Vancomycin Dosing in Elderly Patients: A Single-Center Retrospective Study.

Prescribers often face the challenge of predicting creatinine clearance (CrCl) in elderly patients who are 65 years or older and have serum creatinine (SCr) concentrations below 1 mg/dL. Studies have shown that utilizing rounded SCr would underestimate CrCl in this population, which could lead to the under-dosing of some medications like vancomycin. The current study aimed to compare the accuracy of vancomycin dosing using actual SCr versus rounded SCr to 1 mg/dL in elderly patients. A total of 245 patients were included. The therapeutic trough level (10-20 mg/L) was achieved in 138 (56.3%) patients using actual SCr. Sub-therapeutic (<10 mg/L) and supra-therapeutic (>20 mg/L) trough levels were observed in 32 (13.1%) and 75 (30.6%) patients, respectively. The predictive performance of different vancomycin doses based on actual SCr and rounded SCr compared to the targeted maintenance dose (TMD) showed a stronger correlation of dosing based on actual SCr with TMD (r = 0.55 vs. 0.31) compared to rounded SCr dosing; both doses showed similar precision, with ranges of ±552 mg/day for the dosing based on actual SCr and ±691 mg/day for the dosing based on rounded SCr. Furthermore, the dosing based on actual SCr showed a lower error percentage (69%) and a higher accuracy rate (57.6%) within ±10% of the TMD compared to the dosing based on rounded SCr, which had an error percentage of (92.3%) and an accuracy rate of (40%). The prevalence of vancomycin-associated nephrotoxicity (VAN) was seen in 44 (18%) patients. Patients between 75 and 84 years of age, those who were bedridden, and those with vancomycin trough concentrations greater than 20 mg/L had a higher risk of developing VAN. In conclusion, in elderly patients, estimating vancomycin dosing based on actual SCr was more accurate compared to rounded SCr to 1 mg/dL. The efficacy of vancomycin could be negatively affected by rounding up SCr, which could underestimate CrCl and result in the under-dosing of vancomycin.

Forecasting the BAO measurements of the CSST galaxy and AGN spectroscopic surveys

ABSTRACT The spectroscopic survey of the China’s Space Survey Telescope (CSST) is expected to obtain a huge number of slitless spectra, including more than one hundred million galaxy spectra and millions of active galactic nuclei (AGNs) spectra. By making use of these spectra, we can measure the Baryon Acoustic Oscillation (BAO) signals over large redshift ranges with excellent precisions. In this work, we predict the CSST measurements of the post-reconstruction galaxy power spectra at $0\lt z\lt 1.2$ and pre-reconstruction AGN power spectra at $0\lt z\lt 4$, and derive the BAO signals at different redshift bins by constraining the BAO scaling parameters using the Markov Chain Monte Carlo method. Our result shows that the CSST spectroscopic survey can provide accurate BAO measurements with precisions higher than 1 and 3 per cent for the galaxy and AGN surveys, respectively. By comparing with current measurements in the same range at low redshifts, this can improve the precisions by a factor of $2\sim 3$, and similar precisions can be obtained in the pessimistic case. We also investigate the constraints on the cosmological parameters using the measured BAO data by the CSST, and obtain stringent constraint results for the energy density of dark matter, Hubble constant, and equation of state of dark energy.

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning-based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). Among the compared models, SegNeXt-ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. This two-stage SegNeXt-ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance.