High-accuracy protein model quality assessment using attention graph neural networks.

Great improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing quality assessment (QA) approaches lag behind the development of protein structure prediction methods, where almost all existing QA models degrade in accuracy when the target is a decoy of high quality. How to give an accurate assessment to high-accuracy decoys is particularly useful with the available of accurate structure prediction methods. Here we propose a fast and effective single-model QA method, QATEN, which can evaluate decoys only by their topological characteristics and atomic types. Our model uses graph neural networks and attention mechanisms to evaluate global and amino acid level scores, and uses specific loss functions to constrain the network to focus more on high-precision decoys and protein domains. On the CASP14 evaluation decoys, QATEN performs better than other QA models under all correlation coefficients when targeting average LDDT. QATEN shows promising performance when considering only high-accuracy decoys. Compared to the embedded evaluation modules of predicted ${C}_{\alpha^{-}} RMSD$ (pRMSD) in RosettaFold and predicted LDDT (pLDDT) in AlphaFold2, QATEN is complementary and capable of achieving better evaluation on some decoy structures generated by AlphaFold2 and RosettaFold. These results suggest that the new QATEN approach can be used as a reliable independent assessment algorithm for high-accuracy protein structure decoys.

Improved model quality assessment using sequence and structural information by enhanced deep neural networks.

Protein model quality assessment plays an important role in protein structure prediction, protein design and drug discovery. In this work, DeepUMQA2, a substantially improved version of DeepUMQA for protein model quality assessment, is proposed. First, sequence features containing protein co-evolution information and structural features reflecting family information are extracted to complement model-dependent features. Second, a novel backbone network based on triangular multiplication update and axial attention mechanism is designed to enhance information exchange between inter-residue pairs. On CASP13 and CASP14 datasets, the performance of DeepUMQA2 increases by 20.5 and 20.4% compared with DeepUMQA, respectively (measured by top 1 loss). Moreover, on the three-month CAMEO dataset (11 March to 04 June 2022), DeepUMQA2 outperforms DeepUMQA by 15.5% (measured by local AUC0,0.2) and ranks first among all competing server methods in CAMEO blind test. Experimental results show that DeepUMQA2 outperforms state-of-the-art model quality assessment methods, such as ProQ3D-LDDT, ModFOLD8, and DeepAccNet and DeepUMQA2 can select more suitable best models than state-of-the-art protein structure methods, such as AlphaFold2, RoseTTAFold and I-TASSER, provided themselves.

A joint brain extraction and image quality assessment framework for fetal brain MRI slices.

Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.

The European Organisation of External Quality Assurance Providers in Laboratory Medicine (EQALM) Statement: guidelines for publishing about interlaboratory comparison studies (PubILC).

Data and results from interlaboratory comparison (ILC) studies, external quality assessment (EQA) and proficiency testing (PT) activities are important and valuable contributions both to the further development of all disciplines of medical laboratory diagnostics, and to the evaluation and comparison of in vitro diagnostic assays. So far, however, there are no recommendations as to which essential items should be addressed in publications on interlaboratory comparisons. The European Organization of External Quality Assurance Providers in Laboratory Medicine (EQALM) recognized the need for such recommendations, and these were developed by a group of experts. The result of this endeavor is the EQALM Statement on items recommended to be addressed in publications on interlaboratory comparison activities (PubILC), in conjunction with a user-friendly checklist. Once adopted by authors and journals, the EQALM Statement will ensure essential information and/or study-related facts are included within publications on EQA/PT activities.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Methods for improving turnaround time of comprehensive biomarker testing in non-small cell lung cancer.

Comprehensive biomarker testing for patients with non-small cell lung cancer is critical for selecting appropriate targeted therapy or immunotherapy. Ensuring timely ordering, processing, and reporting is key to optimizing patient outcomes. However, various factors can prevent or delay patients from being offered the option of treatment selection based on comprehensive biomarker testing. These factors include problems with access to testing, tissue adequacy, turnaround time, and health insurance coverage and billing practices. Turnaround time depends on several logistical and tissue handling factors, which involve institutional policies, processes, resources, testing methodology, and testing algorithms that vary across different practices. In this article, the authors identify key factors that prolong biomarker testing turnaround time, propose strategies to reduce it, and present a process map to aid physicians and key organizational stakeholders in improving testing efficiency.

Quality assessment of enzymatic methyl-seq library constructed using crude cell lysate.

Enzymatic methyl-seq (EM-seq), an enzyme-based method, identifies genome-wide DNA methylation, which enables us to obtain reliable methylome data from purified genomic DNA by avoiding bisulfite-induced DNA damage. However, the loss of DNA during purification hinders the methylome analysis of limited samples. The crude DNA extraction method is the quickest and minimal sample loss approach for obtaining useable DNA without requiring additional dissolution and purification. However, it remains unclear whether crude DNA can be used directly for EM-seq library construction. In this study, we aimed to assess the quality of EM-seq libraries prepared directly using crude DNA. The crude DNA-derived libraries provided appropriate fragment sizes and concentrations for sequencing similar to those of the purified DNA-derived libraries. However, the sequencing results of crude samples exhibited lower reference sequence mapping efficiencies than those of the purified samples. Additionally, the lower-input crude DNA-derived sample exhibited a marginally lower cytosine-to-thymine conversion efficiency and hypermethylated pattern around gene regulatory elements than the higher-input crude DNA- or purified DNA-derived samples. In contrast, the methylation profiles of the crude and purified samples exhibited a significant correlation. Our findings indicate that crude DNA can be used as a raw material for EM-seq library construction.

The "hidden noise" problem in MR image reconstruction.

PURPOSE: The performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared-error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, the reference data will contain noise and is not a true gold standard. In this work, we demonstrate that the "hidden noise" present in reference data can substantially confound standard approaches for ranking different image reconstruction results. METHODS: Using both experimental and simulated k-space data and several different image reconstruction techniques, we examined whether there was correlation between performance metrics obtained with typical noisy reference data versus those obtained with higher-quality reference data. RESULTS: For conventional performance metrics, the reconstructions that matched best with the higher-quality reference data were substantially different from the reconstructions that matched best with typical noisy reference data. This leads to suboptimal reconstruction results if the performance with respect to noisy reference data is used to select which reconstruction methods/parameters to employ. These issues were reduced when employing alternative error metrics that better account for noise. CONCLUSION: Reference data containing hidden noise can substantially mislead the ranking of image reconstruction methods when using conventional error metrics, but this issue can be mitigated with alternative error metrics.

Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation.

PURPOSE: To develop an inline automatic quality control to achieve consistent diagnostic image quality with subject-specific scan time, and to demonstrate this method for 2D phase-contrast flow MRI to reach a predetermined SNR. METHODS: We designed a closed-loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free-breathing 2D pseudo-golden-angle spiral phase-contrast sequence was modified to listen for image-quality messages from the reconstructions. Ten healthy volunteers and 1 patient were imaged at 0.55 T. Target SNR was selected based on retrospective analysis of cardiac output error, and performance of the automatic SNR-driven "stop" was assessed inline. RESULTS: SNR calculation and automated segmentation was feasible within 20 s with inline deployment. The SNR-driven acquisition time was 2 min 39 s ± 67 s (aorta) and 3 min ± 80 s (main pulmonary artery) with a min/max acquisition time of 1 min 43 s/4 min 52 s (aorta) and 1 min 43 s/5 min 50 s (main pulmonary artery) across 6 healthy volunteers, while ensuring a diagnostic measurement with relative absolute error in quantitative flow measurement lower than 2.1% (aorta) and 6.3% (main pulmonary artery). CONCLUSION: The inline quality control enables subject-specific optimized scan times while ensuring consistent diagnostic image quality. The distribution of automated stopping times across the population revealed the value of a subject-specific scan time.

Automated detection of motion artifacts in brain MR images using deep learning.

Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning (DL) model to detect rigid motion in T1-weighted brain images. We leveraged a 2D convolutional neural network (CNN) trained on motion-synthesized data for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed 93% agreement with the labeling of a radiologist a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is aimed at inline automatic detection of motion artifacts, accelerating part of the time-consuming quality assessment (QA) process and augmenting expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.

Evaluation of quality and quantity of randomized controlled trials in hepatobiliary surgery: A scoping/mapping review.

AIM: To evaluate the quantity and quality of randomized controlled trials (RCTs) in hepatobiliary surgery and for identifying gaps in current evidences. METHODS: A systematic search was conducted in MEDLINE (via PubMed), Web of Science, and Cochrane Controlled Register of Trials (CENTRAL) for RCTs of hepatobiliary surgery published from inception until the end of 2023. The quality of each study was assessed using the Cochrane risk-of-bias (RoB) tool. The associations between risk of bias and the region and publication date were also assessed. Evidence mapping was performed to identify research gaps in the field. RESULTS: The study included 1187 records. The number and proportion of published randomized controlled trials (RCTs) in hepatobiliary surgery increased over time, from 13 RCTs (.0005% of publications) in 1970-1979 to 201 RCTs (.003% of publications) in 2020-2023. There was a significant increase in the number of studies with a low risk of bias in RoB domains (p < .01). The proportion of RCTs with low risk of bias improved significantly after the introduction of CONSORT guidelines (p < .001). The evidence mapping revealed a significant research focus on major and minor hepatectomy and cholecystectomy. However, gaps were identified in liver cyst surgery and hepatobiliary vascular surgery. Additionally, there are gaps in the field of perioperative management and nutrition intervention. CONCLUSION: The quantity and quality of RCTs in hepatobiliary surgery have increased over time, but there is still room for improvement. We have identified gaps in current research that can be addressed in future studies.

Assessing Quality in Interventional Practice: Identifying Meaningful and Actionable Metrics.

Interventional cardiologists should insist on quality assessment techniques that indisputably reflect the merit of care delivered. Only measurable outcomes and metrics that are modifiable should be identified and collected. An evaluation process should be adopted that genuinely appraises clinical practice, incorporating appropriate benchmarks for comparison.

Emerging trends in sperm selection: enhancing success rates in assisted reproduction.

This comprehensive review explores the evolving landscape of sperm selection techniques within the realm of Assisted Reproductive Technology (ART). Our analysis delves into a range of methods from traditional approaches like density gradient centrifugation to advanced techniques such as Magnetic-Activated Cell Sorting (MACS) and Intracytoplasmic Morphologically Selected Sperm Injection (IMSI). We critically assess the efficacy of these methods in terms of sperm motility, morphology, DNA integrity, and other functional attributes, providing a detailed comparison of their clinical outcomes. We highlight the transition from conventional sperm selection methods, which primarily focus on physical characteristics, to more sophisticated techniques that offer a comprehensive evaluation of sperm molecular properties. This shift not only promises enhanced prediction of fertilization success but also has significant implications for improving embryo quality and increasing the chances of live birth. By synthesizing various studies and research papers, we present an in-depth analysis of the predictability of different sperm selection procedures in ART. The review also discusses the clinical applicability of these methods, emphasizing their potential in shaping the future of assisted reproduction. Our findings suggest that the integration of advanced sperm selection strategies in ART could lead to more cost-effective treatments with reduced duration and higher success rates. This review aims to provide clinicians and researchers in reproductive medicine with comprehensive insights into the current state and future prospects of sperm selection technologies in ART.

Analyses of quality of life in cancer drug trials - a review of measurements and analytical choices in post-reimbursement studies.

OBJECTIVES: For drugs reimbursed with limited evidence of patient benefits, confirmatory evidence of overall survival (OS) and quality of life (QoL) benefits is important. For QoL data to serve as valuable input to patients and decision-makers, it must be measured and analyzed using appropriate methods. We aimed to assess the measurement and analyses of post-reimbursement QoL data for cancer drugs introduced in Swedish healthcare with limited evidence at the time of reimbursement. METHODS: We reviewed any published post-reimbursement trial data on QoL for cancer drugs reimbursed in Sweden between 2010 and 2020 with limited evidence of improvement in QoL and OS benefits at the time of reimbursement. We extracted information on the instruments used, frequency of measurement, extent of missing data, statistical approaches, and the use of pre-registration and study protocols. RESULTS: Out of 22 drugs satisfying our inclusion criteria, we identified published QoL data for 12 drugs in 22 studies covering multiple cancer types. The most frequently used QoL instruments were EORTC QLQ-C30 and EQ-5D-3/5L. We identified three areas needing improvement in QoL measurement and analysis: (i) motivation for the frequency of measurements, (ii) handling of the substantial missing data problem, and (iii) inclusion and adherence to QoL analyses in clinical trial pre-registration and study protocols. CONCLUSIONS: Our review shows that the measurements and analysis of QoL data in our sample of cancer trials covering drugs initially reimbursed without any confirmed QoL or OS evidence have significant room for improvement. The increasing use of QoL assessments must be accompanied by a stricter adherence to best-practice guidelines to provide valuable input to patients and decision-makers.

Motion Robust MR Fingerprinting Scan to Image Neonates With Prenatal Opioid Exposure.

PURPOSE: To explore whether MR fingerprinting (MRF) scans provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure (POE). STUDY TYPE: Prospective. POPULATION: 13 infants with POE (3 male; 12 newborns (age 7-65 days) and 1 infant aged 9-months). FIELD STRENGTH/SEQUENCE: 3T, 3D T1-weighted MPRAGE, 3D T2-weighted TSE and MRF sequences. ASSESSMENT: The image quality of MRF and MRI was assessed in a fully crossed, multiple-reader, multiple-case study. Sixteen image quality features in three types-image artifacts, structure and myelination visualization-were ranked by four neuroradiologists (8, 7, 5, and 8 years of experience respectively), using a 3-point scale. MRF T1 and T2 values in 8 white matter brain regions were compared between babies younger than 1 month and babies between 1 and 2 months. STATISTICAL TESTS: Generalized estimating equations model to test the significance of differences of regional T1 and T2 values of babies under 1 month and those older. MRI and MRF image quality was assessed using Gwet's second order auto-correlation coefficient (AC2) with confidence levels. The Cochran-Mantel-Haenszel test was used to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features. A P value <0.05 was considered statistically significant. RESULTS: The MRF of two infants were excluded in T1 and T2 value analysis due to severe motion artifact but were included in the image quality assessment. In infants under 1 month of age (N = 6), the T1 and T2 values were significantly higher compared to those between 1 and 2 months of age (N = 4). MRF images showed significantly higher image quality ratings in all three feature types compared to MRI images. CONCLUSIONS: MR Fingerprinting scans have potential to be a motion-robust and efficient method for nonsedated infants. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety.

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

Quality assessment of common anti-malarial medicines marketed in Gambella, National Regional State, South Western-Ethiopia.

BACKGROUND: Over the past years, there has been a growing concern that a considerable amount of anti-malarial supply in the underdeveloped world particularly in the private sector, is of poor quality. The World Health Organization (WHO) has received about 1500 reports that mentions instances of substandard and falsified products since 2013. The majority of the reports concerned antibiotics and anti-malarials. The majority of reports (42%) originate from the WHO African region. OBJECTIVE: This study intends to assess the quality of the most widely used anti-malarial medications [artemether-lumefantrine tablets, chloroquine phosphate tablets, primaquine phosphate tablets, artesunate, and artemether injections] in Gambella, South-West, Ethiopia. METHODS: A total of 52 samples were collected on June 2022 from Gambella National Regional State, Ethiopia. Half of the districts (six) located in the four zones of the region were chosen using simple random sampling technique. All drug retail outlets available in the selected districts (locally known as woredas) were included. The samples were subjected to visual inspection with a tool adopted from the joint WHO/FIP/ USP checklist. The pharmacopeial tests for identification, uniformity of dosage forms, assay, thickness, diameter, hardness, friability, disintegration test, dissolution, and sterility tests were carried out according to the USP 44-NF 39 and International Pharmacopoeia 11th edition, 2022 monographs. RESULTS AND DISCUSSION: Only 25% of the samples were registered on the Ethiopian Food and Drug Authority (EFDA's) electronic regulatory/ registration system (ERIS). Besides, 88.8% of artemether injection products were presented in clear glass ampoules. This might expose the products to photochemical degradation that leads to in loss of anti-plasmodial activity. In addition, 50% of the artemether products assessed were not bioequivalent with the comparator product in the in vitro dissolution comparison tests. Overall, the study findings reveal a high prevalence (58.3%) of substandard anti-malarial drugs in the region. The stated percent of the samples had failed in one or more of the quality test parameters assessed in this study. CONCLUSION: The study findings reveal a high prevalence (58.3%) of substandard anti-malarial drugs in the region. Only a quarter were registered and 38% of the unregistered products failed the quality tests. Hence, the national, regional medicine regulatory bodies and other stake holders should perform the required roles to circumvent presence of Substandard and Falsified (SF) anti-malarial drugs in the study sites.

Stroke Rehabilitation Clinical Practice Guidelines in Low- and Middle-Income Countries: A Systematic Review of Quality and Unique Features.

INTRODUCTION: Efforts toward reducing stroke burden have been an immense challenge. One important reasons could be the scope and quality of clinical practice guidelines (CPGs) developed for stroke rehabilitation in low- and middle-income countries (LMICs), restricting its translation to clinical practice. This systematic review aimed to assess the availability, scope and quality of CPGs for stroke rehabilitation in LMICs. METHODS: Following PRISMA guidelines, CPGs for stroke rehabilitation in LMICs were searched across four major electronic databases (Medline, Embase, CINAHL, and PEDro). Additional studies were identified from grey literature and a hand search of key bibliographies and search engines. The availability and content of the CPGs were narratively summarized and quality of de novo CPGs was analyzed using "Appraisal of Guidelines REsearch and Evaluation" (AGREE) tools: version II & Recommendations Excellence (REX) version. Features of contextualizations/adaptations of non-de novo CPGs were narratively summarized. RESULTS: Twelve CPGs from 10 countries were included. CPGs from Pakistan, Sri Lanka, India, and China were developed de novo. CPGs from Kenya, Philippines, South Africa, Cameroon, Mongolia, and Ukraine were contextualized/adapted based on existing guidelines from high-income countries. Most contextualized CPGs had limited stakeholder involvement, local health systems/patient pathway analyses. All ten countries included recommendations for physiotherapy, seven for communication, swallowing, and five for occupational therapy services poststroke. Quality assessment using AGREE-REX and AGREE-II for de novo guidelines was poor, especially scoring low in development and applicability. CONCLUSION: Contextualized CPGs for stroke rehabilitation in LMICs were scarcely available and not meeting required quality. There is a need for development of context-specific, culturally relevant CPGs for stroke rehabilitation in LMICs to improve implementation/translation into clinical practice.

Diagnostic performance of mpox virus (MPXV) real-time PCR assays: multicenter assessment and extended sensitivity analysis.

PURPOSE: To comprehensively investigate the diagnostic performance of routinely used assays in MPXV testing, the National Center of Clinical Laboratories in China conducted a nationwide external quality assessment (EQA) scheme and an evaluated nine assays used by ≥ 5 laboratories in the EQA. METHODS: MPXV virus-like particles with 2700, 900 and 300 copies/mL were distributed to 195 EQA laboratories. For extended analysis, triple-diluted samples from 9000 to 4.12 copies/mL were repeated 20 times using the assays employed by ≥ 5 laboratories. The diagnostic performance was assessed by analyzing EQA data and calculating the limits of detection (LODs). RESULTS: The performance was competent in 87.69% (171/195) of the participants and 87.94% (175/199) of the datasets. The positive percentage agreements (PPAs) were greater than 99% for samples at 2700 and 900 copies/mL, and 95.60% (761/796) for samples at 300 copies/mL. The calculated LODs for the two clades ranged from 228.44 to 924.31 copies/mL and were greater than the LODs specified by the respective kits. EasyDiagnosis had the lowest calculated LODs and showed superior performance in EQA, whereas BioGerm and Sansure, with higher calculated LODs, did not perform well in EQA. CONCLUSION: This study provides valuable information from the EQA data and evaluation of the diagnostic performance of MPXV detection assays. It also provided insights into reagent optimization and enabled prompt public health interventions for the outbreak.

Automating quality control in cardiac magnetic resonance: Artificial intelligence for discriminative assessment of planning and motion artifacts and real-time reacquisition guidance.

BACKGROUND: Accurate measurements from cardiovascular magnetic resonance (CMR) images require precise positioning of scan planes and elimination of motion artifacts from arrhythmia or breathing. Unidentified or incorrectly managed artifacts degrade image quality, invalidate clinical measurements, and decrease diagnostic confidence. Currently, radiographers must manually inspect each acquired image to confirm diagnostic quality and decide whether reacquisition or a change in sequences is warranted. We aimed to develop artificial intelligence (AI) to provide continuous quality scores across different quality domains, and from these, determine whether cines are clinically adequate, require replanning, or warrant a change in protocol. METHODS: A three-dimensional convolutional neural network was trained to predict cine quality graded on a continuous scale by a level 3 CMR expert, focusing separately on planning and motion artifacts. It incorporated four distinct output heads for the assessment of image quality in terms of (a, b, c) 2-, 3- and 4-chamber misplanning, and (d) long- and short-axis arrhythmia/breathing artifact. Backpropagation was selectively performed across these heads based on the labels present for each cine. Each image in the testing set was reported by four level 3 CMR experts, providing a consensus on clinical adequacy. The AI's assessment of image quality and ability to identify images requiring replanning or sequence changes were evaluated with Spearman's rho and the area under receiver operating characteristic curve (AUROC), respectively. RESULTS: A total of 1940 cines across 1387 studies were included. On the test set of 383 cines, AI-judged image quality correlated strongly with expert judgment, with Spearman's rho of 0.84, 0.84, 0.81, and 0.81 for 2-, 3- and 4-chamber planning quality and the extent of arrhythmia or breathing artifacts, respectively. The AI also showed high efficacy in flagging clinically inadequate cines (AUROC 0.88, 0.93, and 0.93 for identifying misplanning of 2-, 3- and 4-chamber cines, and 0.90 for identifying movement artifacts). CONCLUSION: AI can assess distinct domains of CMR cine quality and provide continuous quality scores that correlate closely with a consensus of experts. These ratings could be used to identify cases where reacquisition is warranted and guide corrective actions to optimize image quality, including replanning, prospective gating, or real-time imaging.

The development and implementation of a proficiency testing program for SARS-CoV-2 using dried tube specimens in resource-limited countries.

INTRODUCTION: When COVID-19 hit the world in 2019, an enhanced focus on diagnostic testing for SARS-CoV-2 was essential for a successful pandemic response. Testing laboratories stretched their capabilities for the new coronavirus by adopting different test methods. The necessity of having external quality assurance (EQA) mechanisms was even more critical due to this rapid expansion. However, there was a lack of experience in providing the necessary SARS-CoV-2 EQA materials, especially in locations with constrained resources. OBJECTIVE: We aimed to create a PT (Proficiency testing) programme based on the Dried Tube Specimens (DTS) method that would be a practical option for molecular based SARS-CoV-2 EQA in Low- and Middle-Income Countries. METHODS: Based on previous ISO/IEC 17043:2010 accreditation experiences and with assistance from the US Centers for Disease Control and Prevention, The Supranational Reference Laboratory of Uganda (adapted the DTS sample preparation method and completed a pilot EQA program between 2020 and 2021. Stability and panel validation testing was conducted on the designed materials before shipping to pilot participants in six African countries. Participants received a panel containing five SARS-CoV-2 DTS samples, transported at ambient conditions. Results submitted by participants were compared to validation results. Participants were graded as satisfactory (≥ 80%) or unsatisfactory (< 80%) and performance reports disseminated. RESULTS: Our SARS-CoV-2 stability experiments showed that SARS-CoV-2 RNA was stable (-15 to -25 °C, 4 to 8 °C, (18 to 28 °C) room temperature and 35 to 38 °C) as well as DTS panels (4 to 8 °C, 18 to 28 °C, 35 to 38 °C and 45 °C) for a period of 4 weeks. The SARS-CoV-2 DTS panels were successfully piloted in 35 test sites from Zambia, Malawi, Mozambique, Nigeria, and Seychelles. The pilot results of the participants showed good accuracy, with an average of 86% (30/35) concordance with the original SARS CoV-2 expectations. CONCLUSION: The SARS-CoV-2 DTS PT panel is reliable, stable at ambient temperature, simple to prepare and requires minimal resources.