The influence of artificial intelligence assistance on the diagnostic performance of CCTA for coronary stenosis for radiologists with different levels of experience.

BACKGROUND: The interpretation of coronary computed tomography angiography (CCTA) stenosis may be difficult among radiologists of different experience levels. Artificial intelligence (AI) may improve the diagnostic performance. PURPOSE: To investigate whether the diagnostic performance and time efficiency of radiologists with different levels of experience in interpreting CCTA images could be improved by using CCTA with AI assistance (CCTA-AI). MATERIAL AND METHODS: This analysis included 200 patients with complete CCTA and invasive coronary angiography (ICA) data, using ICA results as the reference. Eighteen radiologists were divided into three levels based on experience (Levels I, II, and III), and the three levels were divided into groups without (Groups 1, 2, and 3) and with (Groups 4, 5, and 6) AI assistance, totaling six groups (to avoid reader recall bias). The average sensitivity, specificity, NPV, PPV, and AUC were reported for the six groups and CCTA-AI at the patient, vessel, and segment levels. The interpretation time in the groups with and without CCTA-AI was recorded. RESULTS: Compared to the corresponding group without CCTA-AI, the Level I group with CCTA-AI had improved sensitivity (75.0% vs. 83.0% on patient-based; P = 0.003). At Level III, the specificity was better with CCTA-AI. The median interpretation times for the groups with and without CCTA-AI were 413 and 615 s, respectively (P < 0.001). CONCLUSION: CCTA-AI could assist with and improve the diagnostic performance of radiologists with different experience levels, with Level I radiologists exhibiting improved sensitivity and Level III radiologists exhibiting improved specificity. The use of CCTA-AI could shorten the training time for radiologists.

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience.

BACKGROUND: To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists. METHODS: We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1-Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6. RESULTS: The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2-+ 16.6% and + 5.9-+ 16.1%, respectively) and NPVs (range + 3.7-+ 13.4% and + 2.7-+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61). CONCLUSIONS: Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA. Trial registration Clinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis.

Diagnostic Performance of Angiography-Derived Quantitative Flow Ratio in Complex Coronary Lesions.

BACKGROUND: Quantitative flow ratio derived from computed tomography angiography (CT-QFR) and invasive coronary angiography (Murray law-based quantitative flow ratio [µQFR]) are novel approaches enabling rapid computation of fractional flow reserve without the use of pressure guidewires and vasodilators. However, the feasibility and diagnostic performance of both CT-QFR and µQFR in evaluating complex coronary lesions remain unclear. METHODS: Between September 2014 and September 2021, 240 patients with 30% to 90% coronary diameter stenosis who underwent both coronary computed tomography angiography and invasive coronary angiography with fractional flow reserve within 60 days were retrospectively enrolled. The diagnostic performance of CT-QFR and µQFR in detecting functional ischemia among all lesions, especially complex coronary lesions, was analyzed using fractional flow reserve as the reference standard. RESULTS: CT-QFR and µQFR analyses were performed on 309 and 289 vessels, respectively. The diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for CT-QFR in all lesions at the per-vessel level were 91% (with a 95% CI of 84%-96%), 92% (95% CI, 88%-95%), 83% (95% CI, 75%-90%), 96% (95% CI, 93%-98%), and 92% (95% CI, 88%-95%), with values for µQFR of 90% (95% CI, 81%-95%), 97% (95% CI, 93%-99%), 92% (95% CI, 84%-97%), 96% (95% CI, 92%-98%), and 94% (95% CI, 91%-97%), respectively. Among bifurcation, tandem, and moderate-to-severe calcified lesions, the diagnostic values of CT-QFR and µQFR showed great correlation and agreement with those of invasive fractional flow reserve, achieving an area under the receiver operating characteristic curve exceeding 0.9 for each complex lesion at the vessel level. Furthermore, the accuracies of CT-QFR and µQFR in the gray zone were 85% and 84%, respectively. CONCLUSIONS: Angiography-derived quantitative flow ratio (CT-QFR and µQFR) demonstrated remarkable diagnostic performance in complex coronary lesions, indicating its pivotal role in the management of patients with coronary artery disease.

Contrastive Learning for Prediction of Alzheimer's Disease Using Brain 18F-FDG PET.

Brain 18F-FDG PET images are commonly-known materials for effectively predicting Alzheimer's disease (AD). How-ever, the data volume of PET is usually insufficient, which is unfavorable to train an accurate AD prediction networks. Fur-thermore, the PET image is noisy with low signal-to-noise ratio, and simultaneously the feature (metabolic abnormality) used for predicting AD in PET image is not always obvious. Such charac-teristics of 18F-FDG PET images hinder the existing deep learning networks to learn the feature of lesion (i.e., glucose metabolism abnormality) effectively, which leads to unsatisfactory classifica-tion performance and poor robustness. In this paper, a contrastive-based learning method is proposed to address the challenges of PET image inherently possessed. Firstly, the slices of 3D PET image are amplified by cropping the image of anchors (i.e., an augmented version of the same image) to generate extended train-ing data. Meanwhile, contrastive loss is adopted to enlarge inter-class feature distances and reduce intra-class feature differences using subject fuzzy labels as supervised information. Secondly, we construct a double convolutional hybrid attention module to enhance the network to learn different perceptual domains where two convolutional layers with different convolutional kernels (7 × 7 and 5 × 5) are constructed. Moreover, we recommend a diagnosis mechanism by analyzing the consistency of predicted result for PET slices alone with clinical neuropsychological assessment to achieve a better AD diagnosis. The experimental results show that the proposed method outperforms the state-of-the-arts for brain 18F-FDG PET images while remaining satisfactory computational performance, and hence demonstrate the advantage of the method in effectively predicting AD.

Estimation of malignancy of pulmonary nodules at CT scans: Effect of computer-aided diagnosis on diagnostic performance of radiologists.

OBJECTIVES: To develop a computer-aided diagnosis (CAD) system for distinguishing malignant from benign pulmonary nodules on computed tomography (CT) scans, and to assess whether the diagnostic performance of radiologists with different experiences can be improved with the assistant of CAD. MATERIALS AND METHODS: A total of 857 malignant nodules from 601 patients and 426 benign nodules from 278 patients were retrospectively collected from four hospitals. In this study, we exploited convolutional neural network in the framework of deep learning to classify whether a nodule was benign or malignant. A total of 745 malignant nodules and 370 benign nodules were used as the training data of our CAD system. The remaining 112 malignant nodules and 56 benign nodules were used as the test data. The participants were two senior chest radiologists, two secondary chest radiologists, and two junior radiology residents. The readers estimated the likelihood of malignancy of pulmonary nodules first without and then with CAD output. Receiver-operating characteristic (ROC) curve was used to evaluate readers' diagnostic performance. RESULTS: When a threshold level of 58% was used to estimate the likelihood of malignancy, the sensitivity, specificity, and diagnostic accuracy values of our CAD scheme alone were 93.8%, 83.9%, and 90.5%, respectively. For all six readers, the mean area under the ROC curve (Az ) values without and with CAD system were 0.913 and 0.938, respectively. For each reader, there is a large difference in Az values that assessed without and with CAD system. With CAD output, the readers made correct changes an average of 15.7 times and incorrect changes an average of 2 times. CONCLUSION: Our CAD system significantly improved the diagnostic performance of readers regardless of their experience levels for assessment of the likelihood of malignancy of pulmonary nodules.

Prognostic Significance of Serum Magnesium in Acute Intracerebral Hemorrhage Patients.

BACKGROUND: Experimental animal model studies have shown neuroprotective properties of magnesium. We assessed the relationship between admission magnesium and admission stroke severity and 3-month clinical outcomes in patients with acute intracerebral hemorrhage (ICH). METHODS: The present study included 323 patients with acute ICH who were prospectively identified. Demographic characteristics, lifestyle risk factors, National Institute of Health Stroke Scale (NIHSS) score, hematoma volumes, and other clinical features were recorded at baseline for all participants. Patients were divided into three groups based on the admission magnesium levels (T1: <0.84; T2: 0.84-0.91; T3: ≥0.91 mmol/L). Clinical outcomes were death, poor functional outcome (defined by modified rankin ccale [mRS] scores 3-6) at 3 months. RESULTS: After 3-month follow-up, 40 (12.4%) all-cause mortality and 132 (40.9%) poor functional outcome were documented. Median NIHSS scores for each tertile (T1 to T3) were 8.0, 5.5, and 6.0, and median hematoma volumes were 10.0, 8.05, and 12.4 ml, respectively. There was no significant association between baseline NIHSS scores (P=0.176) and hematoma volumes (P=0.442) in T3 and T1 in multivariable linear regression models. Compared with the patients in T1, those in T3 were associated with less frequency of all-cause mortality [adjusted odds ratio (OR), 0.10; 95% confidence interval (CI), 0.02-0.54; P-trend=0.010] but not poor functional outcome (adjusted OR, 1.80; 95%CI, 0.71-4.56; P-trend=0.227) after adjustment for potential confounders. CONCLUSION: Elevated admission serum magnesium level is associated with lower odds of mortality but not poor functional outcome at 3 months in patients with acute ICH.

Relationship between deep medullary veins in susceptibility-weighted imaging and ipsilateral cerebrovascular reactivity of middle cerebral artery in patients with ischemic stroke.

Deep cerebral veins have been recently associated with the severity of hemodynamic impairment in moyamoya disease. The aim of the current study was to determine the correlation of deep medullary veins (DMVs) in susceptibility-weighted imaging (SWI) with ipsilateral cerebrovascular reactivity (CVR) of and anterior cecebrocervical artery stenosis in patients with ischemic stroke. Patients with unilateral TIA or infarction who underwent 3.0 T magnetic resonance imaging SWI, digital subtraction angiography and transcranial Doppler with CO2 stimulation within the first 7 days of hospitalization were retrospectively selected. CVR and stenosis of anterior cerebrocervical arteries were compared between different DMVs stages in symptomatic hemispheres (SHs) and asymptomatic hemispheres (AHs). A total of 61 patients were subsequently included in the present study. A univariate analysis was conducted and results for age (PAHs=0.004, PSHs=0.006), hypertension (PAHs=0.008, PSHs=0.020), current smoking (PAHs=0.006, PSHs=0.021), CVR (PAHs=0.000, PSHs=0.000), and artery stenosis (PAHs=0.000, PSHs=0.000) were obtained. The results suggested statistically significant differences between DMVs grades in SHs and AHs. A subsequent multivariate analysis revealed that CVR (ORAHs=0.925, 95% CIAHs: 0.873-0.981; ORSHs=0.945, 95% CISHs: 0.896-0.996), and artery stenosis (ORAH=3.147, 95% CIAH: 1.010-9.806; ORSHs=2.882, 95% CISHs: 1.017-8.166) were independent risk factors of DMVs. In conclusion, 3.0 T SWI was useful in detecting the DMVs around the lateral ventricle in patients with atherosclerotic ischemic stroke. CVR and stenosis of anterior cerebrocervical arteries were independent risk factors for ipsilateral DMVs in SHs and AHs.