Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.

PURPOSE: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose 18F-FDG PET data over the entire dose reduction spectrum. METHODS: In this multicenter study, five AI models were investigated for restoring low-count whole-body PET/MRI, covering convolutional benchmarks - U-Net, enhanced deep super-resolution network (EDSR), generative adversarial network (GAN) - and the most cutting-edge image reconstruction transformer models in computer vision to date - Swin transformer image restoration network (SwinIR) and EDSR-ViT (vision transformer). The models were evaluated against six groups of count levels representing the simulated 75%, 50%, 25%, 12.5%, 6.25%, and 1% (extremely ultra-low-count) of the clinical standard 3 MBq/kg 18F-FDG dose. The comparisons were performed upon two independent cohorts - (1) a primary cohort from Stanford University and (2) a cross-continental external validation cohort from Tübingen University - in order to ensure the findings are generalizable. A total of 476 original count and simulated low-count whole-body PET/MRI scans were incorporated into this analysis. RESULTS: For low-count PET restoration on the primary cohort, the mean structural similarity index (SSIM) scores for dose 6.25% were 0.898 (95% CI, 0.887-0.910) for EDSR, 0.893 (0.881-0.905) for EDSR-ViT, 0.873 (0.859-0.887) for GAN, 0.885 (0.873-0.898) for U-Net, and 0.910 (0.900-0.920) for SwinIR. In continuation, SwinIR and U-Net's performances were also discreetly evaluated at each simulated radiotracer dose levels. Using the primary Stanford cohort, the mean diagnostic image quality (DIQ; 5-point Likert scale) scores of SwinIR restoration were 5 (SD, 0) for dose 75%, 4.50 (0.535) for dose 50%, 3.75 (0.463) for dose 25%, 3.25 (0.463) for dose 12.5%, 4 (0.926) for dose 6.25%, and 2.5 (0.534) for dose 1%. CONCLUSION: Compared to low-count PET images, with near-to or nondiagnostic images at higher dose reduction levels (up to 6.25%), both SwinIR and U-Net significantly improve the diagnostic quality of PET images. A radiotracer dose reduction to 1% of the current clinical standard radiotracer dose is out of scope for current AI techniques.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing.

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother's genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients' aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies.

Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.

PURPOSE: To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. METHODS: We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3-30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. RESULTS: The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). CONCLUSIONS: Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.

Dynamics of Detection of Anti-SARS-CoV-2 IgG/IgM and SARS-CoV-2 RNA in Respiratory Tract Specimens in 48 COVID-19 Patients.

BACKGROUND: The rapid spread of pneumonia caused by SARS-CoV-2 has seriously threatened people. In this study, we detected the expression of anti-SARS-CoV-2 IgG/IgM and respiratory tract SARS-CoV-2 RNA in patients with COVID-19 and explored the correlation and clinical significance between SARS-CoV-2 antibody and respiratory SARS-CoV-2 RNA. METHODS: From March 5, 2020 to April 28, 2020, 48 cases with COVID-19 diagnosed in Beijing Xiaotangshan Hospital were enrolled. SARS-CoV-2 RNAs were detected by real-time fluorescence RT-PCR method. Serum SARS-CoV-2 IgG/IgM antibodies were determined by colloidal gold immunochromatography. The statistical analysis was performed using chi-squared test. RESULTS: In all the patients, SARS-CoV-2 RNA among 270 upper respiratory tract (nasal or throat swabs) samples, 71 lower respiratory tract (sputum) samples, and anti-SARS-CoV-2 IgM/IgG antibodies in 123 serum samples were detected during the hospitalization period. The positive rate of anti-SARS-CoV-2 IgG was significantly higher than that of anti-SARS-CoV-2 IgM within the first week after symptom onset (p < 0.05). The positive rate of anti-SARS-CoV-2 IgG was also significantly higher than that of anti-SARS-CoV-2 IgM during day 8 - 30 after symptom onset (p < 0.01). The positive rate of SARS-CoV-2 RNA in the lower respiratory tract specimens (64.8%, 46/71) was significantly higher than that in the upper respiratory tract (46.7%, 126/270) (p < 0.05). The positive rate (100%, 4/4) of SARS-CoV-2 RNA detection in the lower respiratory tract specimens before IgG seroconversion was significantly higher than that of the positive rate (59.3%, 32/54) after IgG seroconversion (p < 0.01). The positive rate (72.2%, 57/79) of SARS-CoV-2 RNA detection in the upper respiratory tract specimens before IgG seroconversion was significantly higher than that of the positive rate (30.7%, 39/127) after IgG seroconversion (p < 0.01). CONCLUSIONS: Anti-SARS-CoV-2 IgG might be detected within the first week after symptom onset. The application of SARS-CoV-2 antibody (IgG/IgM) detection is important for the suspected cases of SARS-CoV-2 infection with negative SARS-CoV-2 RNA results. The positive rate of SARS-CoV-2 RNA detection in the lower respiratory tract specimens was significantly higher than that in the upper respiratory tract. Sputum detection is recommended for the detection of SARS-CoV-2 RNA. Using lower respiratory tract specimens may reduce the false negative PCR tests. The detection of SARS-CoV-2 RNA can be improved by investigating follow-up specimens over time.

Two-dimensional swarm formation in time-invariant external potential: Modeling, analysis, and control.

Cluster formation has been observed in many organisms in nature. It has the desirable properties for designing energy efficient protocols for Wireless Sensor Networks (WSNs). In this paper, we present a new approach for energy efficient WSN protocols that investigates how the cluster formation of sensors responds to the external time-invariant energy potential. In this approach, the necessity for data transmission to the Base Station is eliminated, thereby conserving energy for WSNs. We define swarm formation topology and estimate the curvature of an external potential manifold by analyzing the change of the swarm formation in time. We also introduce a dynamic formation control algorithm for maintaining defined swarm formation topology in the external potential.

Identifying mutation-driven changes in gene functionality that lead to venous thromboembolism.

Venous thromboembolism (VTE) is a common hematological disorder. VTE affects millions of people around the world each year and can be fatal. Earlier studies have revealed the possible VTE genetic risk factors in Europeans. The 2018 Critical Assessment of Genome Interpretation (CAGI) challenge had asked participants to distinguish between 66 VTE and 37 non-VTE African American (AA) individuals based on their exome sequencing data. We used variants from AA VTE association studies and VTE genes from DisGeNET database to evaluate VTE risk via four different approaches; two of these methods were most successful at the task. Our best performing method represented each exome as a vector of predicted functional effect scores of variants within the known genes. These exome vectors were then clustered with k-means. This approach achieved 70.8% precision and 69.7% recall in identifying VTE patients. Our second-best ranked method had collapsed the variant effect scores into gene-level function changes, using the same vector clustering approach for patient/control identification. These results show predictability of VTE risk in AA population and highlight the importance of variant-driven gene functional changes in judging disease status. Of course, more in-depth understanding of AA VTE pathogenicity is still needed for more precise predictions.

What went wrong with variant effect predictor performance for the PCM1 challenge.

The recent years have seen a drastic increase in the amount of available genomic sequences. Alongside this explosion, hundreds of computational tools were developed to assess the impact of observed genetic variation. Critical Assessment of Genome Interpretation (CAGI) provides a platform to evaluate the performance of these tools in experimentally relevant contexts. In the CAGI-5 challenge assessing the 38 missense variants affecting the human Pericentriolar material 1 protein (PCM1), our SNAP-based submission was the top performer, although it did worse than expected from other evaluations. Here, we compare the CAGI-5 submissions, and 24 additional commonly used variant effect predictors, to analyze the reasons for this observation. We identified per residue conservation, structural, and functional PCM1 characteristics, which may be responsible. As expected, predictors had a hard time distinguishing effect variants in nonconserved positions. They were also better able to call effect variants in a structurally rich region than in a less-structured one; in the latter, they more often correctly identified benign than effect variants. Curiously, most of the protein was predicted to be functionally robust to mutation-a feature that likely makes it a harder problem for generalized variant effect predictors.

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

Physicochemical Aspects and Sensory Profiles as Various Potential Factors for Comprehensive Quality Assessment of Nü-Er-Cha Produced from Rhamnus heterophylla Oliv.

Nü-Er-Cha, produced from the leaves of Rhamnus heterophylla Oliv., is known as an herbal tea and used in the treatment of bleeding, irregular menstruation and dysentery. A method was developed for the quality assessment of herbal tea, Nü-Er-Cha, adopting physical parameters, chemical constituents and sensory profiles as various potential factors. Their inner relationship was mined by multivariate statistical analysis tools, and the three factors were integrated by a technique for order preference by a similarity to ideal solution (TOPSIS) approach to comprehensively analyze the characters of Nü-Er-Cha. Viscosity was also introduced to the physical parameter determination besides conductivity, pH and color. Seven common peaks of eight batches of Nü-Er-Cha were marked by a high performance liquid chromatography (HPLC) fingerprint. They were further identified by HPLC mass spectrometry/mass spectrometry (HPLC-MS/MS) as hydroxybenzoic acids and flavanol glycosides. Fifty trained members participated in the sensory evaluation. Significant correlations between total sensory scores and conductivity, viscosity as well as pH were observed, a relatively innovative result for the quality assessment of herbal teas. The common peaks, belonging to hydroxybenzoic acids and flavanol glycosides, were mainly related to the color of infusions and leaves. The result of the TOPSIS analysis showed that S3 and S4 ranked as the top two in the comprehensive quality assessment. This may be related to rhamnetin triglycoside with a galactose/glucose and two rhamnoses, which had a higher peak response in S3 and S4 than that in the other samples. The present study may contribute to a better understanding of the relationship regarding physical properties, chemical composition and sensory profiles, and it may supply ideas for the comprehensive quality assessment of the herbal tea Nü-Er-Cha.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

Screening active components of modified Xiaoyao powder as NRF2 agonists.

Nuclear factor (erythroid-derived 2)-like 2 (NRF2) regulates antioxidant enzymes and phase II detoxifying enzymes, such as NAD(P)H: quinone oxidoreductase 1 (NQO1). Modified Xiaoyao powder (MXP) is most frequently used in the prevention and treatment of breast cancer in China. This study aimed to screen active components of MXP for antioxidant stress and chemoprevention, which depend on NRF2-NQO1 signalling pathway. A total of 25 monomeric compounds contained in MXP were screened using an antioxidant response element-luciferase reporter. The most potent antioxidant response element-luciferase inducers were chosen to further examine their effects on NRF2 and NQO1 in MCF-7 cells. These results were then confirmed by determining the oxidative stress levels and chemopreventive effect on inhibiting carcinogenesis transformation in NRF2 knockdown (NRF2KD ) and NRF2 wild-type MCF-10A cells. We found that quercetin, kaempferol, and atractylenolide II in MXP were potent NRF2 inducers, which could up-regulate the expression of NRF2 and its downstream enzymes NQO1. In addition, these components could decrease reduced oxidative stress and inhibit carcinogenesis transformation, which depended on NRF2-NQO1 pathway. In conclusion, NRF2-NQO1 pathway plays an essential role in mediating the activity of MXP and its active components, at least in part; some beneficial effects of MXP may be applicable to breast cancer chemoprevention. Our study firstly found MXP active components including quercetin, kaempferol, and atractylenolide II. Our results firstly demonstrate that NRF2-NQO1 pathway plays an essential role in mediating the activity of MXP and its active components in breast cancer chemoprevention. Our study firstly found that atractylenolide II is a novel NRF2 inducer.

Integrated Display and Simulation for Automatic Dependent Surveillance-Broadcast and Traffic Collision Avoidance System Data Fusion.

Automatic Dependent Surveillance-Broadcast (ADS-B) is the direction of airspace surveillance development. Research analyzing the benefits of Traffic Collision Avoidance System (TCAS) and ADS-B data fusion is almost absent. The paper proposes an ADS-B minimum system from ADS-B In and ADS-B Out. In ADS-B In, a fusion model with a variable sampling Variational Bayesian-Interacting Multiple Model (VSVB-IMM) algorithm is proposed for integrated display and an airspace traffic situation display is developed by using ADS-B information. ADS-B Out includes ADS-B Out transmission based on a simulator platform and an Unmanned Aerial Vehicle (UAV) platform. This paper describes the overall implementation of ADS-B minimum system, including theoretical model design, experimental simulation verification, engineering implementation, results analysis, etc. Simulation and implementation results show that the fused system has better performance than each independent subsystem and it can work well in engineering applications.

Porphyrin-based honeycomb films and their antibacterial activity.

Micrometer-sized porous honeycomb-patterned thin films based on hybrid complexes formed via electrostatic interaction between Mn(III) meso-tetra(4-sulfonatophenyl) porphine chloride (an acid form, {MnTPPS}) and dimethyldioctadecylammonium bromide (DODMABr). The morphology of the microporous thin films can be well regulated by controlling the concentration of MnTPPS-DODMA complexes, DODMABr, and polystyrene (PS), respectively. The formation of the microporous thin films was largely influenced by different solvents. The well-ordered microporous films of MnTPPS-DODMA complexes exhibit a more efficient antibacterial activity under visible light than those of hybrid complexes of nanoparticles modified with DODMABr, implying that well-ordered microporous films containing porphyrin composition can improve photochemical activity and more dominance in applications in biological medicine fields.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

The role of magnesium in cardiac arrest.

Cardiac arrest is a leading cause of death globally. Only 25.8% of in-hospital and 33.5% of out-of-hospital individuals who achieve spontaneous circulation following cardiac arrest survive to leave the hospital. Respiratory failure and acute coronary syndrome are the two most common etiologies of cardiac arrest. Effort has been made to improve the outcomes of individuals resuscitated from cardiac arrest. Magnesium is an ion that is critical to the function of all cells and organs. It is often overlooked in everyday clinical practice. At present, there have only been a small number of reviews discussing the role of magnesium in cardiac arrest. In this review, for the first time, we provide a comprehensive overview of magnesium research in cardiac arrest focusing on the effects of magnesium on the occurrence and prognosis of cardiac arrest, as well as in the two main diseases causing cardiac arrest, respiratory failure and acute coronary syndrome. The current findings support the view that magnesium disorder is associated with increased risk of cardiac arrest as well as respiratory failure and acute coronary syndrome.

Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures.

Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies-including cross-validation, external validation, and consecutive validation-demonstrate the model's efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.