Background:Long-term aortic dissection monitoring requires consistent, landmark-based measurements over time. Purpose:To evaluate the performance of deep reinforcement learning (DRL) agents for the detection of anatomic landmarks in patients with Stanford Type B aortic dissection (TBAD). Materials and Methods:This is an international retrospective study of 396 CT angiography scans of patients with TBAD from 9 participating sites (mean age 57.6 years ± 13.7/[SD]; 236 male, 160 female). Aortic landmarks, including the aortic annulus and 8 aortic branch vessels, were manually labeled. Additionally, interobserver variability data were collected between 2 observers for 30 scans. DRL agents were trained independently for each landmark with the manual labels serving as the reference standard. Unique landmark locations were obtained from (1) single agents' predictions and (2) clusters of landmark predictions using the DBSCAN clustering algorithm. The performance was analyzed based on distance metrics (mean, median, quantiles) and failure rates, defined as a distance error of more than 10 mm. Interobserver variability data were analyzed with a pairwise Wilcoxon test. Results:On the internal test set, DRL single agents predicted landmark locations with median errors of 2.7 (95% CI, 2.2-3.3) mm and 4.8% failure rate. Cluster-based predictions resulted in a median error of 2.5 (95% CI, 2.4-2.7) mm and 4.0% failure rate. Pooled over all landmarks, cluster-based predictions outperformed single-agent predictions (P < 1e-5). In the external test set, cluster-based DRL models demonstrated significantly lower localization errors and fewer failures compared to single-agent DRL models (P < .01), and were either not significantly different (single agents) from or significantly better (cluster-based, P < .05) than human interobserver variability. The median processing time for a single agent's prediction was 1.0 second (IQR, 0.7-1.4 seconds). Conclusion:Single-agent and cluster-based DRL predict aortic landmarks in patients with TBAD with high accuracy and precision, comparable to the variability between human observers.

BACKGROUND: Risk stratification is highly desirable in patients with uncomplicated Stanford type B aortic dissection but inadequately supported by evidence. We sought to validate externally a published prediction model for late adverse events (LAEs), consisting of 1 clinical (connective tissue disease) and 4 imaging variables: maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and number of identifiable intercostal arteries. METHODS: We assembled a retrospective multicenter cohort (ROADMAP [Registry of Aortic Diseases to Model Adverse Events and Progression]) of 401 patients with uncomplicated Stanford type B aortic dissection presenting to 1 of 8 aortic centers between 2001 and 2013, followed until 2020. LAEs were defined as fatal or nonfatal aortic rupture, new refractory hypertension or pain, organ or limb ischemia, aortic aneurysm formation (≥6 cm), or rapid growth (≥1 cm per year). We applied the original model parameters to the validation cohort and examined the effect on risk categorization using LAE end points. RESULTS: One hundred and seventy-six patients (44%) with incomplete imaging or clinical data were excluded. Of 225 patients in the final cohort, 90 (40%) developed LAEs, predominantly driven by aneurysm formation. Baseline maximum aortic diameter was significantly larger in patients with (42.6 [95% CI, 39.1–45.8] mm) compared with patients without LAEs (39.9 [95% CI, 36.3–44.2] mm; P =0.001). A multivariable Cox regression model indicated that only maximum diameter was associated with LAEs (hazard ratio, 1.07 [95% CI, 1.03–1.11] per mm; P <0.001), while the other parameters were not ( P >0.05). Applying the original prediction model to the validation cohort resulted in a poor 5-year sensitivity (38%) and specificity (69%). CONCLUSIONS: A clinical and imaging-based prediction model performed poorly in the ROADMAP cohort. Maximum aortic diameter remains the strongest predictor of LAEs in uncomplicated Stanford type B aortic dissection.

Deep learning-based denoising of dose-modulated cardiac CT an-giographic examinations using a three-dimensional approach resulted in excellent image quality compared with conventional methods, and left ventricular segmentation on the denoised images were strongly correlated with expert manual segmentations

Rationale and objectives: Post-TAVR persistent pulmonary hypertension (PH) is a better predictor of poor outcome than pre-TAVR PH. In this longitudinal study we sought to evaluate whether pulmonary artery (distensibility (D-PA) measured on preprocedural ECG-gated CTA is associated with persistent-PH and 2-year mortality after TAVR.Materials and methods: Three hundred and thirty-six patients undergoing TAVR between July 2012 and March 2016 were retrospectively included and followed for all-cause mortality until November 2017. All patients underwent retrospectively ECG-gated CTA prior to TAVR. Main pulmonary artery (MPA) area was measured in systole and in diastole. D-PA was calculated as: [(area-MPA(max)-area-MPA(min))/area-MPA(max)]%. ROC analysis was performed to assess the AUC for persistent-PH. Youden Index was used to determine the optimal threshold of D-PA for persistent-PH. Two groups were compared based on a D-PA threshold of 8% (specificity of 70% for persistent-PH). Kaplan-Meier, Cox proportional-hazard, and logistic regression analyses were performed. The primary clinical endpoint was defined as persistent-PH post-TAVR. The secondary endpoint was defined as all-cause mortality 2 years after TAVR.Results: Median follow-up time was 413 (interquartiles 339-757) days. A total of 183 (54%) had persistent-PH and 68 (20%) patients died within 2-years after TAVR. Patients with D-PA<8% had significantly more persistent-PH (67% vs 47%, p<0.001) and 2-year deaths (28% vs 15%, p=0.006), compared to patients with D-PA>8%. Adjusted multivariable regression analyses showed that D-PA<8% was independently associated with persistent-PH (OR 2.10 [95%-CI 1.3-4.5], p=0.007) and 2-year mortality (HR 2.91 [95%-CI 1.5-5.8], p=0.002). Kaplan-Meier analysis showed that 2-year mortality of patients with D-PA<8% was significantly higher compared to patients with D-PA >= 8% (mortality 28% vs 15%; log-rank p=0.003).Conclusion: D-PA on preprocedural CTA is independently associated with persistent-PH and two-year mortality in patients who undergo TAVR.

Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and quantification. In this retrospective study, four readers labelled four-view mammograms as BAC positive (BAC+) or BAC negative (BAC−) at image level. Starting from a pretrained VGG16 model, we trained a convolutional neural network to discriminate BAC+ and BAC− mammograms. Accuracy, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) were used to assess the diagnostic performance. Predictions of calcified areas were generated using the generalized gradient-weighted class activation mapping (Grad-CAM++) method, and their correlation with manual measurement of BAC length in a subset of cases was assessed using Spearman ρ. A total 1493 women (198 BAC+) with a median age of 59 years (interquartile range 52–68) were included and partitioned in a training set of 410 cases (1640 views, 398 BAC+), validation set of 222 cases (888 views, 89 BAC+), and test set of 229 cases (916 views, 94 BAC+). The accuracy, F1 score, and AUC-ROC were 0.94, 0.86, and 0.98 in the training set; 0.96, 0.74, and 0.96 in the validation set; and 0.97, 0.80, and 0.95 in the test set, respectively. In 112 analyzed views, the Grad-CAM++ predictions displayed a strong correlation with BAC measured length (ρ = 0.88, p < 0.001). Our model showed promising performances in BAC detection and in quantification of BAC burden, showing a strong correlation with manual measurements. Integrating our model to clinical practice could improve BAC reporting without increasing clinical workload, facilitating large-scale studies on the impact of BAC as a biomarker of cardiovascular risk, raising awareness on women’s cardiovascular health, and leveraging mammographic screening. • We implemented a deep convolutional neural network (CNN) for BAC detection and quantification. • Our CNN had an area under the receiving operator curve of 0.95 for BAC detection in the test set composed of 916 views, 94 of which were BAC+ . • Furthermore, our CNN showed a strong correlation with manual BAC measurements (ρ = 0.88) in a set of 112 views.

Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches — INS, MCDS, MCDbS — based on convolutional neural networks and uncertainty quantification methods to predict the orientation (ϕ,θ) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time.

Purpose To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD). Materials and Methods The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique. Results The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling. Conclusion This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy.Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue.

Melazzini L, Savoldi F, Chessa M, Vitali P, Zanardo M, Bertoldo EG, Fiolo V, Griffanti L, Carminati M, Frigiola A, Giamberti A, Secchi F, Callus E, Codari M, Sardanelli F. Adults with tetralogy of Fallot show specific features of cerebral small vessel disease: the BACH San Donato study. Brain Imaging Behav. 2022 Aug;16(4):1721-1731. doi: 10.1007/s11682-022-00629-6. Epub 2022 Mar 9. PMID: 35266099; PMCID: PMC8906830. Abstract Life expectancy in adults with congenital heart disease (ACHD) has increased. As these patients grow older, they experience aging-related diseases more than their healthy peers. To better characterize this field, we launched the multi-disciplinary BACH (Brain Aging in Congenital Heart disease) San Donato study, that aimed at investigating signs of brain injury in ACHD. Twenty-three adults with repaired tetralogy of Fallot and 23 age- and sex-matched healthy controls were prospectively recruited and underwent brain magnetic resonance imaging. White matter hyperintensities (WMHs) were segmented using a machine-learning approach and automatically split into periventricular and deep. Cerebral microbleeds were manually counted. A subset of 14 patients were also assessed with an extensive neuropsychological battery. Age was 41.78 ± 10.33 years (mean ± standard deviation) for patients and 41.48 ± 10.28 years for controls (p = 0.921). Albeit not significantly, total brain (p = 0.282) and brain tissue volumes (p = 0.539 for cerebrospinal fluid, p = 0.661 for grey matter, p = 0.793 for white matter) were lower in ACHD, while total volume (p = 0.283) and sub-classes of WMHs (p = 0.386 for periventricular WMHs and p = 0.138 for deep WMHs) were higher in ACHD than in controls. Deep WMHs were associated with poorer performance at the frontal assessment battery (r = -0.650, p = 0.012). Also, patients had a much larger number of microbleeds than controls (median and interquartile range 5 [3-11] and 0 [0-0] respectively; p < 0.001). In this study, adults with tetralogy of Fallot showed specific signs of brain injury, with some clinical implications. Eventually, accurate characterization of brain health using neuroimaging and neuropsychological data would aid in the identification of ACHD patients at risk of cognitive deterioration.

Machine learning (ML) and deep learning (DL) algorithms have recently gained traction in the field of aortic imaging for their potential to provide novel insights and to standardize the extraction of imaging features while delivering accurate and reproducible results. With DL algorithms, it is now possible to model complex relationships within vast imaging and clinical datasets. In this chapter, we highlighted the most recent ML and DL applications in image segmentation, image classification, and outcome prediction of patients with aortic dissection and abdominal aortic aneurysm.

Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2–4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. Pre-training ICCs of the four morphologic features ranged from 0.04 (−0.05 to 0.13) to 0.68 (0.49–0.81) between observer 1 and observers 2–4 and from 0.50 (0.32–0.69) to 0.89 (0.78–0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52–0.87) to 0.97 (0.94–0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.

Epicardial adipose tissue (EAT) is emerging as a novel source of biomarkers for cardiovascular and metabolic risk. In particular, certain features of EAT such as thickness, volume, and radiodensity have been associated with the presence of cardiovascular diseases and the occurrence of cardiovascular adverse events. Noninvasive imaging modalities such as echocardiography, computed tomography (CT), and magnetic resonance imaging can be used to assess and measure EAT volume and features, each technique having its individual advantages and limitations. Recently, artificial intelligence methods have been applied to EAT imaging, CT in particular, to alleviate potentially cumbersome tasks related to its assessment by human operators and to investigate the prognostic power of EAT-derived features.

Heparan sulfate (HS) binds with several signaling molecules and regulates ligand–receptor interactions, playing an essential role in embryonic development. Here we showed that HS was intensively expressed in pancreatic islet β-cells after 1week of age in mice. The enzymatic removal of HS in isolated islets resulted in attenuated glucose-induced insulin secretion with a concomitant reduction in gene expression of several key components in the insulin secretion machinery. We further depleted islet HS by inactivating the exostosin tumor-like 3 gene specifically in β-cells. These mice exhibited abnormal islet morphology with reduced β-cell proliferation after 1week of age and glucose intolerance due to defective insulin secretion. These results demonstrate that islet HS is involved in the regulation of postnatal islet maturation and required to ensure normal insulin secretion.

White matter hyperintensities (WMHs) on T2-weighted images are radiological signs of cerebral small vessel disease. As their total volume is variably associated with cognition, a new approach that integrates multiple radiological criteria is warranted. Location may matter, as periventricular WMHs have been shown to be associated with cognitive impairments. WMHs that appear as hypointense in T1-weighted images (T1w) may also indicate the most severe component of WMHs. We developed an automatic method that sub-classifies WMHs into four categories (periventricular/deep and T1w-hypointense/nonT1w-hypointense) using MRI data from 684 community-dwelling older adults from the Whitehall II study. To test if location and intensity information can impact cognition, we derived two general linear models using either overall or subdivided volumes. Results showed that periventricular T1w-hypointense WMHs were significantly associated with poorer performance in the trail making A (p = 0.011), digit symbol (p = 0.028) and digit coding (p = 0.009) tests. We found no association between total WMH volume and cognition. These findings suggest that sub-classifying WMHs according to both location and intensity in T1w reveals specific associations with cognitive performance.

(1) Background: the study of dynamic contrast enhancement (DCE) has a limited role in the detection of prostate cancer (PCa), and there is a growing interest in performing unenhanced biparametric prostate-MRI (bpMRI) instead of the conventional multiparametric-MRI (mpMRI). In this study, we aimed to retrospectively compare the performance of the mpMRI, which includes DCE study, and the unenhanced bpMRI, composed of only T2-weighted imaging and diffusion-weighted imaging (DWI), in PCa detection in men with elevated prostate-specific-antigen (PSA) levels. (2) Methods: a 1.5 T MRI, with an endorectal-coil, was performed on 431 men (aged 61.5 ± 8.3 years) with a PSA ≥4.0 ng/mL. The bpMRI and mpMRI tests were independently assessed in separate sessions by two readers with 5 (R1) and 3 (R2) years of experience. The histopathology or ≥2 years follow-up served as a reference standard. The sensitivity and specificity were calculated with their 95% CI, and McNemar’s and Cohen’s κ statistics were used. (3) Results: in 195/431 (45%) of histopathologically proven PCa cases, 62/195 (32%) were high-grade PCa (GS ≥ 7b) and 133/195 (68%) were low-grade PCa (GS ≤ 7a). The PCa could be excluded by histopathology in 58/431 (14%) and by follow-up in 178/431 (41%) of patients. For bpMRI, the sensitivity was 164/195 (84%, 95% CI: 79–89%) for R1 and 156/195 (80%, 95% CI: 74–86%) for R2; while specificity was 182/236 (77%, 95% CI: 72–82%) for R1 and 175/236 (74%, 95% CI: 68–80%) for R2. For mpMRI, sensitivity was 168/195 (86%, 95% CI: 81–91%) for R1 and 160/195 (82%, 95% CI: 77–87%) for R2; while specificity was 184/236 (78%, 95% CI: 73–83%) for R1 and 177/236 (75%, 95% CI: 69–81%) for R2. Interobserver agreement was substantial for both bpMRI (κ = 0.802) and mpMRI (κ = 0.787). (4) Conclusions: the diagnostic performance of bpMRI and mpMRI were similar, and no high-grade PCa was missed with bpMRI.

Background Breast arterial calcifications (BAC), representing Mönckeberg's sclerosis of the tunica media of breast arteries, are an imaging biomarker for cardiovascular risk stratification in the female population. Our aim was to estimate the intra- and inter-reader reproducibility of a semiquantitative score for BAC assessment (BAC-SS). Methods Consecutive women who underwent screening mammography at our center from January 1st to January 31st, 2018 were retrieved and included according to BAC presence. Two readers (R1 and R2) independently applied the BAC-SS to medio-lateral oblique views, obtaining a BAC score by summing: (I) number of calcified vessels (from 0 to n); (II) vessel opacification, i.e., the degree of artery coverage by calcium bright pixels (0 or 1); and (III) length class of calcified vessels (from 0 to 4). R1 repeated the assessment 2 weeks later. Scoring time was recorded. Cohen's κ statistics and Bland-Altman analysis were used. Results Among 408 women, 57 (14%) had BAC; 114 medio-lateral oblique views were assessed. Median BAC score was 4 [interquartile range (IQR): 3-6] for R1 and 4 (IQR: 2-6) for R2 (P=0.417) while median scoring time was 156 s (IQR: 99-314 s) for R1 and 191 s (IQR: 137-292 s) for R2 (P=0.743). Bland-Altman analysis showed a 77% intra-reader reproducibility [bias: 0.193, coefficient of repeatability (CoR): 0.955] and a 64% inter-reader reproducibility (bias: 0.211, CoR: 1.516). Cohen's κ for BAC presence was 0.968 for intra-reader agreement and 0.937 for inter-reader agreement. Conclusions Our BAC-SS has a good intra- and inter-reader reproducibility, within acceptable scoring times. A large-scale study is warranted to test its ability to stratify cardiovascular risk in women.

BackgroundAlthough white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker.PurposeTo determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined.Study TypeSystematic review and meta‐analysis.PopulationIn all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE.Field Strength/Sequence1.0T, 1.5T, or 3.0T/fluid‐attenuated inversion recovery (FLAIR) and/or proton density/T2‐weighted fast spin echo sequences or gradient echo T1‐weighted sequences.AssessmentAfter a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted.Statistical TestsThe pooled WMH volume with 95% confidence interval (CI) was calculated using the random‐effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta‐regression analysis of WMH volume on age was performed.ResultsOf the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta‐analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2 = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88–5.53 cm3). At meta‐regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm3 per year, P < 0.05, R2 = 0.27).Data ConclusionThe lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined.Level of Evidence1Technical Efficacy Stage1

Data set from the article Trimboli RM, Capra D, Codari M, Cozzi A, Di Leo G, Sardanelli F. Breast arterial calcifications as a biomarker of cardiovascular risk: radiologists' awareness, reporting, and action. A survey among the EUSOBI members. Eur Radiol. 2021 Feb;31(2):958-966. doi: 10.1007/s00330-020-07136-6. Epub 2020 Aug 27. PMID: 32851451; PMCID: PMC7813731. Abstract Objectives: To investigate the knowledge of radiologists on breast arterial calcifications (BAC) and attitude about BAC reporting, communication to women, and subsequent action. Methods: An online survey was offered to EUSOBI members, with 17 questions focused on demographics, level of experience, clinical setting, awareness of BAC association with cardiovascular risk, mammographic reporting, modality of BAC assessment, and action habits. Descriptive statistics were used. Results: Among 1084 EUSOBI members, 378 (34.9%) responded to the survey, 361/378 (95.5%) radiologists, 263 females (69.6%), 112 males (29.6%), and 3 (0.8%) who did not specify their gender. Of 378 respondents, 305 (80.7%) declared to be aware of BAC meaning in terms of cardiovascular risk and 234 (61.9%) to routinely include BAC in mammogram reports, when detected. Excluding one inconsistent answer, simple annotation of BAC presence was declared by 151/233 (64.8%), distinction between low versus extensive BAC burden by 59/233 (25.3%), and usage of an ordinal scale by 22/233 (9.5%) and of a cardinal scale by 1/233 (0.4%). Among these 233 radiologists reporting BAC, 106 (45.5%) declared to orally inform the woman and, in case of severe BAC burden, 103 (44.2%) to investigate cardiovascular history, and 92 (39.5%) to refer the woman to a cardiologist. Conclusion: Among EUSOBI respondents, over 80% declared to be aware of BAC cardiovascular meaning and over 60% to include BAC in the report. Qualitative BAC assessment predominates. About 40% of respondents who report on BAC, in the case of severe BAC burden, investigate cardiovascular history and/or refer the woman to a cardiologist.

PurposeArtificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context.MethodsA narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections.ResultsWe first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way.ConclusionsBiomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.

This paper summarizes the 2020 Diversity in Radiology and Molecular Imaging: What We Need to Know Conference, a three-day virtual conference held September 9–11, 2020. The World Molecular Imaging Society (WMIS) and Stanford University jointly organized this event to provide a forum for WMIS members and affiliates worldwide to openly discuss issues pertaining to diversity in science, technology, engineering, and mathematics (STEM). The participants discussed three main conference themes, "racial diversity in STEM," "women in STEM," and "global health," which were discussed through seven plenary lectures, twelve scientific presentations, and nine roundtable discussions, respectively. Breakout sessions were designed to flip the classroom and seek input from attendees on important topics such as increasing the representation of underrepresented minority (URM) members and women in STEM, generating pipeline programs in the fields of molecular imaging, supporting existing URM and women members in their career pursuits, developing mechanisms to effectively address microaggressions, providing leadership opportunities for URM and women STEM members, improving global health research, and developing strategies to advance culturally competent healthcare.

Current blood vessel rendering usually depicts solely the surface of vascular structures and does not visualize any interior structures. While this approach is suitable for most applications, certain cardiovascular diseases, such as aortic dissection would benefit from a more comprehensive visualization. In this work, we investigate different shading styles for the visualization of the aortic inner and outer wall, including the dissection flap. Finding suitable shading algorithms, techniques, and appropriate parameters is time-consuming when practitioners fine-tune them manually. Therefore, we build a shading pipeline using well-known shading algorithms such as Blinn-Phong, Oren-Nayar, Cook-Torrance, Toon, and extended Lit-Sphere shading with techniques such as the Fresnel effect and screen space ambient occlusion. We interviewed six experts from various domains to find the best combination of shadings for preset combinations that maximize user experience and the applicability in clinical settings.