Objective The main objective of the present study was to compare the use of four-dimensional (4D) flow MRI with the habitual sequence (two-dimensional phase-contrast (2DPC) MRI) for the assessment of aortic regurgitation (AR) in the clinical routine. Methods This was a retrospective, observational cohort study of patients with varying grades of AR. For the purposes of the present study, we selected all the cases with a regurgitant fraction (RF)>5% as determined by 2DPC MRI (n=34). In all cases, both sequences (2DPC and 4D flow MRI) were acquired in a single session to ensure comparability. We compared the results of the two techniques by evaluating forward flow, regurgitant flow and regurgitation fraction. Then, the patients were divided into subgroups to determine if these factors had any influence on the measurements: aortic diameter (≤ vs >38 mm), valve anatomy (tricuspid vs bicuspid/quadricuspid), stenosis (gradient ≥15 vs <15) and region of interest location (aortic valve vs sinotubular junction). Results No statistically significant differences were observed between the two techniques with Pearson’s correlation coefficients (r) of forward flow (r=0.826/p value<0001), regurgitant flow (r=0.866/p value<0001) and RF (r=0.761/p value<0001). Conclusions The findings of this study confirm the value of 4D flow MRI for grading AR in clinical practice with an excellent correlation with the standard technique (2DPC MRI).

Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist’s assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label (“positive”, “doubtful”, or “negative”). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI’s sensitivity was low (33%). AI assigned a “doubtful” label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice.

Background/Objectives: The growing use of artificial intelligence (AI) in musculoskeletal radiographs presents significant potential to improve diagnostic accuracy and optimize clinical workflow. However, assessing its performance in clinical environments is essential for successful implementation. We hypothesized that our AI applied to urgent bone X-rays could detect fractures, joint dislocations, and effusion with high sensitivity (Sens) and specificity (Spec). The specific objectives of our study were as follows: 1. To determine the Sens and Spec rates of AI in detecting bone fractures, dislocations, and elbow joint effusion compared to the gold standard (GS). 2. To evaluate the concordance rate between AI and radiology residents (RR). 3. To compare the proportion of doubtful results identified by AI and the RR, and the rates confirmed by GS. Methods: We conducted an observational, double-blind, retrospective study on adult bone X-rays (BXRs) referred from the emergency department at our center between October and November 2022, with a final sample of 792 BXRs, categorized into three groups: large joints, small joints, and long-flat bones. Our AI system detects fractures, dislocations, and elbow effusions, providing results as positive, negative, or doubtful. We compared the diagnostic performance of AI and the RR against a senior radiologist (GS). Results: The study population’s median age was 48 years; 48.6% were male. Statistical analysis showed Sens = 90.6% and Spec = 98% for fracture detection by the RR, and 95.8% and 97.6% by AI. The RR achieved higher Sens (77.8%) and Spec (100%) for dislocation detection compared to AI. The Kappa coefficient between RR and AI was 0.797 for fractures in large joints, and concordance was considered acceptable for all other variables. We also analyzed doubtful cases and their confirmation by GS. Additionally, we analyzed findings not detected by AI, such as chronic fractures, arthropathy, focal lesions, and anatomical variants. Conclusions: This study assessed the impact of AI in a real-world clinical setting, comparing its performance with that of radiologists (both in training and senior). AI achieved high Sens, Spec, and AUC in bone fracture detection and showed strong concordance with the RR. In conclusion, AI has the potential to be a valuable screening tool, helping reduce missed diagnoses in clinical practice.

This study sought to examine the correlation between left ventricular (LV) myocardial feature tracking (FT) and deep learning-based strain (DLS) analysis in the diagnostic (CMRd) and follow-up (CMRf) cardiac magnetic resonance imaging of patients with acute myocarditis. The retrospective study included 17 patients with acute myocarditis and 20 healthy controls. The CMRd took place within 14 days of symptom onset, while the CMRf took place at least 2 months after the event. The global-circumferential FT (FTc) and global-circumferential DLS (DLSc) were analyzed. The continuous variables were compared using paired t-tests or the Wilcoxon test, whereas Pearson’s test or Spearman’s test was used to evaluate the correlation between the continuous variables. The time between the CMRd and CMRf was 5 months [3–11]. The LV ejection fraction (LVEF) was 55 ± 6 and 59 ± 4%, p = 0.008, respectively, and 94.1% of the patients showed late gadolinium enhancement (LGE) and myocardial edema on the CMRd. Significantly lower FTc (−16.1 ± 2.2% vs. −18.9 ± 1.9%, p = 0.001) and DLSc (−38.1 ± 5.2% vs. −41.3 ± 4.5%, p = 0.015) were observed with respect to the controls. Significant increases in the FTc (−16.1 ± 2.2 vs. −17.5 ± 1.9%, p = 0.016) and DLSc (−38.1 ± 5.2 vs. −39.8 ± 3.9%, p = 0.049) were found between the CMRd and CMRf, which were unrelated to the LGE. The LVEF correlated well with the FTc (r = 0.840) and DLSc (r = 0.760). Both techniques had excellent reproducibility, with high intra- (FTc = 0.980, DLSc = 1.000) and inter-observer (FTc = 0.970, DLSc = 0.980) correlation. There was correlation between the LV DLSc/FTc and LVEF in the patients with acute myocarditis according to the CMRd and CMRf.

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