The relationship between toxic air pollution, health expenditure, and economic growth in the European Union: fresh evidence from the PMG-ARDL model.

Air pollution is a danger to economies throughout the European Union. Industry, population expansion, a building boom owing to housing and infrastructure development, increasing vehicle traffic, crowded streets, a lack of availability of clean fuel, and ineffective control programs are the primary causes. Toxic air is a double-edged sword for a country's health since it affects just a tiny fraction of Europe's population. The financial burden and healthcare expenses for people rise when health expenditures rise. The present research looks at how dangerous air levels, healthcare costs, and the expansion of the European Union's economy are all connected. The findings are based on data collected over 29 years and account for the abovementioned variables. The results of the unit root test have the significant probability values of all variables: health expenditures (HE), gross domestic product (GDP), nitrous oxides (NOX), and carbon dioxides (CO2) emissions at both level and first difference. We used the Johansen, Kao, and Pedroni cointegration tests to test the null hypothesis of no cointegration to see that sample variables had a long-term association. The PMG-ARDL test was used to get these findings. The results confirmed the significant probability values of dependent variables in long- and short-run results that GDP has a positive and significant effect on health expenditure, while NOX and CO2 emissions have a negative and significant impact on (HE), in the European Union. To verify the results, we applied the robustness test, fully modified OLS (FMOLD), and dynamic OLS (DOLS); the robustness test results validated the PMG-ARDL test results. Environmental pollution (CO2, NOX) has a significant and negative impact on healthcare expenditures and a significant effect on GDP (HE) in the EU region. The findings of this research have implications for a wide range of parties, including those who would examine the link between factors in a study meant to improve air quality, distribute health resources, or develop strategies for economic development.

Construction and validation of a chromatin regulator-related gene signature for prognostic and therapeutic significance of clear cell renal cell carcinoma.

Background: Epigenetic alterations driven by chromatin regulators (CRs) are well-recognized cancer hallmarks. Growing evidence suggests that the imbalance of CRs may lead to the occurrence of various diseases including tumors. However, the role and prognostic value of CRs in clear cell renal cell carcinoma (ccRCC) remain undefined. Methods: Consensus clustering analysis was used to identify different subtypes. Univariate and multivariate Cox regression analysis were performed to identify prognosis-related CRs and constructed a risk model. Transcriptome sequencing was used to verify gene expression levels. Kaplan-Meier survival analysis was used to compare overall survival (OS) between high- and low-risk groups. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was used to evaluate the performance of the model. The ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA) were executed to evaluate the immune characteristics of samples. Correlation analysis was used to assess the relationship between risk score and immune checkpoint genes, the relationship between expression levels of CRs and immune cell infiltration and drug therapeutic response. Finally, we also compared differences in drug sensitivity between low- and high-risk groups. Results: We identified three CRs-related subtypes with different characteristics. A prognostic model was built with four CRs and can precisely predict the OS of patients in different risk groups. The model has good stability and applicability and was further verified in the internal and external dataset. The transcriptomic levels of the four CRs were also validated, and the risk score was an independent prognostic factor for ccRCC. Obvious differences in the immune microenvironment and the expression levels of immune checkpoints were observed in low- and high-risk group. Higher immune activity and immune cell infiltration were found in the high-risk group. Besides, the expression levels of CRs were associated with drug therapeutic response. Patients with high-risk score may be more sensitive to gemcitabine, vinblastine, paclitaxel, axitinib, sunitinib, and temsirolimus. Conclusions: CRs were strongly associated with the occurrence and development of ccRCC. Targeting CRs may become a new therapeutic strategy for ccRCC. Besides, CRs-related gene signature can predict the prognosis and therapeutic significance of ccRCC, which provides an important reference for clinical decision-making.

Identification of pyroptosis-related subtypes and comprehensive analysis of characteristics of the tumor microenvironment infiltration in clear cell renal cell carcinoma.

Pyroptosis is a kind of programmed cell death triggered by the inflammasome. Growing evidence has revealed the crucial utility of pyroptosis in tumors. However, the potential mechanism of pyroptosis in clear cell renal cell carcinoma (ccRCC) is still unclear. In this research, we systematically analyze the genetic and transcriptional alterations of pyroptosis-related genes (PRGs) in ccRCC, identify pyroptosis-related subtypes, analyze the clinical and microenvironmental differences among different subtypes, develop a corresponding prognostic model to predict the prognosis of patients, and interpret the effect of pyroptosis on ccRCC microenvironment. This study provides a new perspective for a comprehensive understanding of the role of pyroptosis in ccRCC and its impact on the immune microenvironment, and a reliable scoring system was established to predict patients' prognosis.

Construction and validation of programmed cell death-based molecular clusters for prognostic and therapeutic significance of clear cell renal cell carcinoma.

As the dominant histological subtype of kidney cancer, clear cell renal cell carcinoma (ccRCC) poorly responds to conventional chemotherapy and radiotherapy. Although novel immunotherapies such as immune checkpoint inhibitors could have a durable effect in treating ccRCC patients, the limited availability of dependable biomarkers has restricted their application in clinic. In the study of carcinogenesis and cancer therapies, there has been a recent emphasis on researching programmed cell death (PCD). In the current study, we discovered the enriched and prognostic PCD in ccRCC utilizing gene set enrichment analysis (GSEA) and investigate the functional status of ccRCC patients with different PCD risks. Then, genes related to PCD that had prognostic value in ccRCC were identified for the conduction of non-negative matrix factorization to cluster ccRCC patients. Next, the tumor microenvironment, immunogenicity, and therapeutic response in different molecular clusters were analyzed. Among PCD, apoptosis and pyroptosis were enriched in ccRCC and correlated with prognosis. Patients with high PCD levels were related to poor prognosis and a rich but suppressive immune microenvironment. PCD-based molecular clusters were identified to differentiate the clinical status and prognosis of ccRCC. Moreover, the molecular cluster with high PCD levels may correlate with high immunogenicity and a favorable therapeutic response to ccRCC. Furthermore, a simplified PCD-based gene classifier was established to facilitate clinical application and used transcriptome sequencing data from clinical ccRCC samples to validate the applicability of the gene classifier. We thoroughly extended the understanding of PCD in ccRCC and constructed a PCD-based gene classifier for differentiation of the prognosis and therapeutic efficacy in ccRCC.

Genetic dissection of QTLs for oil content in four maize DH populations.

Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored. TOC exhibited continuously and approximately normal distribution in the four populations. The moderate to high broad-sense heritability (67.00-86.60%) indicated that the majority of TOC variations are controlled by genetic factors. A total of 16 QTLs were identified across all chromosomes in a range of 3.49-30.84% in term of phenotypic variation explained. Among them, six QTLs were identified as the major QTLs that explained phenotypic variation larger than 10%. Especially, qOC-1-3 and qOC-2-3 on chromosome 9 were recognized as the largest effect QTLs with 30.84% and 21.74% of phenotypic variance, respectively. Seventeen well-known genes involved in fatty acid metabolic pathway located within QTL intervals. These QTLs will enhance our understanding of the genetic basis of TOC in maize and offer prospective routes to clone candidate genes regulating TOC for breeding program to cultivate maize varieties with the better grain quality.

Unsupervised Cross-Modality Adaptation via Dual Structural-Oriented Guidance for 3D Medical Image Segmentation.

Deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation; however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised domain adaptation (UDA) is a promising solution to tackle this problem. In this work, we present a novel UDA method, named dual adaptation-guiding network (DAG-Net), which incorporates two highly effective and complementary structural-oriented guidance in training to collaboratively adapt a segmentation model from a labelled source domain to an unlabeled target domain. Specifically, our DAG-Net consists of two core modules: 1) Fourier-based contrastive style augmentation (FCSA) which implicitly guides the segmentation network to focus on learning modality-insensitive and structural-relevant features, and 2) residual space alignment (RSA) which provides explicit guidance to enhance the geometric continuity of the prediction in the target modality based on a 3D prior of inter-slice correlation. We have extensively evaluated our method with cardiac substructure and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our DAG-Net greatly outperforms the state-of-the-art UDA approaches for 3D medical image segmentation on unlabeled target images.

Activation of ventral tegmental area dopaminergic neurons ameliorates anxiety-like behaviors in single prolonged stress-induced PTSD model rats.

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric condition that arises after extremely traumatic events, with clinically significant and lasting impacts on both physical and psychological health. The present study examined the role of ventral tegmental area (VTA) dopaminergic signaling in anxiety-like behaviors and the underlying mechanisms in PTSD model rats. Chemogenetic technology was employed to specifically activate VTA dopamine (DA) neurons in rats subjected to single prolonged stress (SPS), and open field and elevated plus maze tests were applied to evaluate the anxiety-like manifestations. Subsequently, in vivo extracellular electrophysiological analyses were used to examine alterations in the firing characteristics of VTA DA neurons. Chemogenetic activation enhanced the firing and burst rates of VTA DA neurons in SPS-induced PTSD model rats and concomitantly mitigated the anxiety-like behavioral phenotypes. Collectively, these findings reveal a direct association between PTSD-relevant anxiety behaviors and VTA dopaminergic activity, and further suggest that interventions designed to enhance VTA dopaminergic activity may be a potential strategy for PTSD treatment.

Ventral tegmental area dopaminergic action in music therapy for post-traumatic stress disorder: A literature review.

Post-traumatic stress disorder (PTSD) is a debilitating sequela of extraordinary traumatic sufferings that threaten personal health and dramatically attenuate the patient's quality of life. Accumulating lines of evidence suggest that functional disorders in the ventral tegmental area (VTA) dopaminergic system contribute substantially to PTSD symptomatology. Notably, music therapy has been shown to greatly ameliorate PTSD symptoms. In this literature review, we focused on whether music improved PTSD symptoms, based on VTA dopaminergic action, including the effects of music on dopamine (DA)-related gene expression, the promotion of DA release and metabolism, and the activation of VTA functional activities. In addition, the strengths and limitations of the studies concerning the results of music therapy on PTSD are discussed. Collectively, music therapy is an effective approach for PTSD intervention, in which the VTA dopaminergic system may hold an important position.

Genetic dissection of QTLs for starch content in four maize DH populations.

Starch is the principal carbohydrate source in maize kernels. Understanding the genetic basis of starch content (SC) benefits greatly in improving maize yield and optimizing end-use quality. Here, four double haploid (DH) populations were generated and were used to identify quantitative trait loci (QTLs) associated with SC. The phenotype of SC exhibited continuous and approximate normal distribution in each population. A total of 13 QTLs for SC in maize kernels was detected in a range of 3.65-16.18% of phenotypic variation explained (PVE). Among those, only some partly overlapped with QTLs previously known to be related to SC. Meanwhile, 12 genes involved in starch synthesis and metabolism located within QTLs were identified in this study. These QTLs will lay the foundation to explore candidate genes regulating SC in maize kernel and facilitate the application of molecular marker-assisted selection for a breeding program to cultivate maize varieties with a deal of grain quality.

Arecoline Induces an Excitatory Response in Ventral Tegmental Area Dopaminergic Neurons in Anesthetized Rats.

Arecoline is the principle psychoactive alkaloid in areca nuts. Areca nuts are chewable seeds of Areca catechu L., which are epidemic plants that grow in tropical and subtropical countries and cause dependency after long-term use. However, the mechanisms underlying such dependency remain largely unclear, and therefore, no effective interventions for its cessation have been developed. The present study aimed to examine the effects of arecoline on neurons of the ventral tegmental area (VTA). After rats were anesthetized and craniotomized, electrophysiological electrodes were lowered into the VTA to obtain extracellular recordings. The mean firing rate of dopaminergic and GABAergic neurons were then calculated and analyzed before and after arecoline treatment. The burst characteristics of the dopaminergic neurons were also analyzed. The results showed that arecoline evoked a significant enhancement of the firing rate of dopaminergic neurons, but not GABAergic neurons. Moreover, arecoline evoked remarkable burst firings in the dopaminergic neurons, including an increase in the burst rate, elongation in the burst duration, and an enhancement in the number of spikes per burst. Collectively, the findings revealed that arecoline significantly excited VTA dopaminergic neurons, which may be a mechanism underlying areca nut dependency and a potential target for areca nut cessation therapy.

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

BACKGROUND: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians. METHODS: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flow-volume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data. RESULTS: Of 45,831 patients' spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5-52.5%), with poor agreements (κ = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001). CONCLUSIONS: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians' correct diagnosis of upper airway obstruction using spirometry.

Deep learning for spirometry quality assurance with spirometric indices and curves.

BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance. METHODS: Spirometry PDF files retrieved from one hospital between October 2017 and October 2020 were labeled according to ATS/ERS 2019 criteria and divided into training and internal test sets. Additional files from three hospitals were used for external testing. A deep learning-based model was constructed and assessed to determine acceptability, usability, and quality rating for FEV1 and FVC. System warning messages and patient instructions were also generated for general practitioners (GPs). RESULTS: A total of 16,502 files were labeled. Of these, 4592 curves were assigned to the internal test set, the remaining constituted the training set. In the internal test set, the model generated 95.1%, 92.4%, and 94.3% accuracy for FEV1 acceptability, usability, and rating. The accuracy for FVC acceptability, usability, and rating were 93.6%, 94.3%, and 92.2%. With the assistance of the model, the performance of GPs in terms of monthly percentages of good quality (A, B, or C grades) tests for FEV1 and FVC was higher by ~ 21% and ~ 36%, respectively. CONCLUSION: The proposed model assisted GPs in spirometry quality assurance, resulting in enhancing the performance of GPs in quality control of spirometry.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Clinical analysis of the "small plateau" sign on the flow-volume curve followed by deep learning automated recognition.

BACKGROUND: Small plateau (SP) on the flow-volume curve was found in parts of patients with suspected asthma or upper airway abnormalities, but it lacks clear scientific proof. Therefore, we aimed to characterize its clinical features. METHODS: We involved patients by reviewing the bronchoprovocation test (BPT) and bronchodilator test (BDT) completed between October 2017 and October 2020 to assess the characteristics of the sign. Patients who underwent laryngoscopy were assigned to perform spirometry to analyze the relationship of the sign and upper airway abnormalities. SP-Network was developed to recognition of the sign using flow-volume curves. RESULTS: Of 13,661 BPTs and 8,168 BDTs completed, we labeled 2,123 (15.5%) and 219 (2.7%) patients with the sign, respectively. Among them, there were 1,782 (83.9%) with the negative-BPT and 194 (88.6%) with the negative-BDT. Patients with SP sign had higher median FVC and FEV1% predicted (both P < .0001). Of 48 patients (16 with and 32 without the sign) who performed laryngoscopy and spirometry, the rate of laryngoscopy-diagnosis upper airway abnormalities in patients with the sign (63%) was higher than those without the sign (31%) (P = 0.038). SP-Network achieved an accuracy of 95.2% in the task of automatic recognition of the sign. CONCLUSIONS: SP sign is featured on the flow-volume curve and recognized by the SP-Network model. Patients with the sign are less likely to have airway hyperresponsiveness, automatic visualizing of this sign is helpful for primary care centers where BPT cannot available.

Design, synthesis and anticancer evaluation of new 4-anilinoquinoline-3-carbonitrile derivatives as dual EGFR/HER2 inhibitors and apoptosis inducers.

Dual targeting of EGFR/HER2 receptor is an attractive strategy for cancer therapy. Four series of 4-anilinoquinoline-3-carbonitrile derivatives were designed and prepared by introducing various functional groups, including a polar hydrophilic group (carboxylic acid), a heterocyclic substituent possessing polarity to some extent, and an unpolar hydrophobic phenyl portion, at the C-6 position of the quinoline skeleton. All of the prepared derivatives were screened for their inhibitory activities against EGFR /HER2 receptors and their antiproliferative activities against the SK-BR-3 and A431 cell lines. Compounds 6a, 6 g and 6d exhibited significant activities against the target cell lines. In particular, the antiproliferative activity of 6d (IC50 = 1.930 µM) against SK-BR-3 was over 2-fold higher than that of neratinib (IC50 = 3.966 µM), and comparable to that of Lapatinib (IC50 = 2.737 µM). On the other hand, 6d (IC50 = 1.893 µM) was more active than the reference drug Neratinib (IC50 = 2.151 µM), and showed comparable potency to Lapatinib (IC50 = 1.285 µM) against A431. Cell cycle analysis and apoptosis assays indicated that 6d arrests the cell cycle in the S phase, and it is a potent apoptotic inducer. Moreover, molecular docking exhibited the binding modes of compound 6d in EGFR and HER2 binding sites, respectively. Compound 6d can be considered as a candidate for further investigation as a more potent anticancer agent.

The diagnostic value of a non-contrast computed tomography scan-based radiomics model for acute aortic dissection.

ABSTRACT: To investigate the diagnostic value of a computed tomography (CT) scan-based radiomics model for acute aortic dissection.For the dissection group, we retrospectively selected 50 patients clinically diagnosed with acute aortic dissection between October 2018 and November 2019, for whom non-contrast CT and CT angiography images were available. Fifty individuals with available non-contrast CT and CT angiography images for other causes were selected for inclusion in the non-dissection group. Based on the aortic dissection locations on the CT angiography images, we marked the corresponding regions-of-interest on the non-contrast CT images of both groups. We collected 1203 characteristic parameters from these regions by extracting radiomics features. Subsequently, we used a random number table to include 70 individuals in the training group and 30 in the validation group. Finally, we used the Lasso regression for dimension reduction and predictive model construction. The diagnostic performance of the model was evaluated by a receiver operating characteristic (ROC) curve.Fourteen characteristic parameters with non-zero coefficients were selected after dimension reduction. The accuracy, sensitivity, specificity, and area under the ROC curve of the prediction model for the training group were 94.3% (66/70), 91.2% (31/34), 97.2% (35/36), and 0.988 (95% confidence interval [CI]: 0.970-0.998), respectively. The respective values for the validation group were 90.0% (27/30), 94.1% (16/17), 84.6% (11/13), and 0.952 (95% CI: 0.883-0.986).Our non-contrast CT scan-based radiomics model accurately facilitated acute aortic dissection diagnosis.

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages CT scans and the clinical data of outpatients and achieves an AUC of 0.920 in the single-center study. We also propose a domain adaptation approach to improve the generalization of our model and achieve an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein.

Ventral Tegmental Area Dysfunction and Disruption of Dopaminergic Homeostasis: Implications for Post-traumatic Stress Disorder.

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric condition characterized by intrusive recollections of the traumatic event, avoidance behaviors, hyper-arousal to event-related cues, cognitive disruption, and mood dysregulation. Accumulating preclinical and clinical evidence implicates dysfunction of the ventral tegmental area (VTA) dopaminergic system in PTSD pathogenesis. This article reviews recent advances in our knowledge of the relationship between dopaminergic dyshomeostasis and PTSD, including the contributions of specific dopaminergic gene variants to disease susceptibility, alterations in VTA dopamine neuron activity, dysregulation of dopaminergic transmission, and potential pharmacological and psychological interventions for PTSD targeting the dopaminergic system. An in-depth understanding of PTSD etiology is crucial for the development of innovative risk assessment, diagnostic, and treatment strategies following traumatic events.

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images. Materials and Methods Head CT angiography images were retrospectively retrieved from two hospital databases acquired across four different scanners between January 2015 and June 2019. The data were divided into training and validation sets; 400 additional independent CT angiograms acquired between July and December 2019 were used for external validation. A deep learning-based algorithm was constructed and assessed. Both internal and external validation were performed. Jackknife alternative free-response receiver operating characteristic analysis was performed. Results A total of 1068 patients (mean age, 57 years ± 11 [standard deviation]; 660 women) were evaluated for a total of 1068 CT angiograms encompassing 1337 cerebral aneurysms. Of these, 534 CT angiograms (688 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5% (633 of 649; 95% CI: 96.0, 98.6). Moreover, eight new aneurysms that had been overlooked in the initial reports were detected (1.2%, eight of 649). With the aid of the algorithm, the overall performance of radiologists in terms of area under the weighted alternative free-response receiver operating characteristic curve was higher by 0.01 (95% CI: 0.00, 0.03). Conclusion The proposed deep learning algorithm assisted radiologists in detecting cerebral aneurysms on CT angiography images, resulting in a higher detection rate. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kallmes and Erickson in this issue.