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OBJECTIVES: To evaluate the effectiveness of a novel artificial intelligence (AI) algorithm for fully automated measurement of left atrial (LA) volumes and function using cardiac CT in patients with atrial fibrillation. METHODS: We included 79 patients (mean age 63 ± 12 years; 35 with atrial fibrillation (AF) and 44 controls) between 2017 and 2020 in this retrospective study. Images were analyzed by a trained AI algorithm and an expert radiologist. Left atrial volumes were obtained at cardiac end-systole, end-diastole, and pre-atrial contraction, which were then used to obtain LA function indices. Intraclass correlation coefficient (ICC) analysis of the LA volumes and function parameters was performed and receiver operating characteristic (ROC) curve analysis was used to compare the ability to detect AF patients. RESULTS: The AI was significantly faster than manual measurement of LA volumes (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs ≥ 0.877; p < 0.001) than controls (all ICCs ≥ 0.799; p < 0.001). The AI comparably estimated LA volumes in AF patients (all within 1.3 mL of the manual measurement), but overestimated volumes by clinically negligible amounts in controls (all by ≤ 4.2 mL). The AI's ability to distinguish AF patients from controls using the LA volume index was similar to the expert's (AUC 0.81 vs 0.82, respectively; p = 0.62). CONCLUSION: The novel AI algorithm efficiently performed fully automated multiphasic CT-based quantification of left atrial volume and function with similar accuracy as compared to manual quantification. Novel CT-based AI algorithm efficiently quantifies left atrial volumes and function with similar accuracy as manual quantification in controls and atrial fibrillation patients. KEY POINTS: ⢠There was good-to-excellent agreement between manual and automated methods for left atrial volume quantification. ⢠The AI comparably estimated LA volumes in AF patients, but overestimated volumes by clinically negligible amounts in controls. ⢠The AI's ability to distinguish AF patients from controls was similar to the manual methods.
Assuntos
Fibrilação Atrial , Idoso , Inteligência Artificial , Fibrilação Atrial/diagnóstico por imagem , Átrios do Coração/diagnóstico por imagem , Humanos , Pessoa de Meia-Idade , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodosRESUMO
OBJECTIVE: This study aimed to validate 4-dimensional phase contrast (4D PC) cine magnetic resonance imaging (MRI) as a means of evaluating left ventricular outflow tract (LVOT) obstruction in patients with hypertrophic cardiomyopathy (HCM). METHODS: In this institutional review board-approved prospective study, 23 patients with suspected HCM from October 2012 to September 2013 underwent 4D PC MRI. Postprocessed 4D PC pathline cine data were reviewed by 2 blinded reviewers to determine presence or absence of LVOT obstruction. Sensitivity, specificity, and accuracy in 4D PC qualitative and quantitative assessment of LVOT obstruction were calculated using echo as reference standard. RESULTS: Consensus interpretation of 4D PC showed 100.0% (7/7) sensitivity, 75.0% specificity (12/16), and 82.6% (19/23) accuracy in assessment of LVOT obstruction. The 4D PC quantitative estimates of LVOT gradient have 71.4% (5/7) sensitivity, 93.8% (15/16) specificity, and 87.0% (20/23) accuracy in evaluation of LVOT obstruction compared with echo. CONCLUSIONS: The 4D PC MRI can assess for LVOT obstruction in HCM patients.
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Técnicas de Imagem de Sincronização Cardíaca/métodos , Cardiomiopatia Hipertrófica/diagnóstico por imagem , Ventrículos do Coração/diagnóstico por imagem , Imageamento Tridimensional/métodos , Imagem Cinética por Ressonância Magnética/métodos , Obstrução do Fluxo Ventricular Externo/diagnóstico por imagem , Cardiomiopatia Hipertrófica/complicações , Meios de Contraste , Feminino , Humanos , Aumento da Imagem/métodos , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Obstrução do Fluxo Ventricular Externo/etiologiaRESUMO
Four-dimensional flow is a magnetic resonance technology that has undergone significant technical improvements in recent years. With increasingly rapid acquisition times and new postprocessing tools, it can provide a tool for demonstrating and visualizing cardiovascular flow phenomena, which may offer new insights into disease. We present an interesting clinical case in which 4-dimensional flow demonstrates potential etiologies for 2 interesting phenomena in the same patient: (1) development of an unusual aneurysm and (2) cryptogenic stroke.
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Aneurisma da Aorta Torácica/diagnóstico , Aneurisma da Aorta Torácica/fisiopatologia , Hemodinâmica/fisiologia , Angiografia por Ressonância Magnética/métodos , Acidente Vascular Cerebral/diagnóstico , Acidente Vascular Cerebral/fisiopatologia , Aneurisma da Aorta Torácica/complicações , Velocidade do Fluxo Sanguíneo , Humanos , Interpretação de Imagem Assistida por Computador , Imageamento Tridimensional , Masculino , Pessoa de Meia-Idade , Acidente Vascular Cerebral/etiologia , Tomografia Computadorizada por Raios XRESUMO
PURPOSE: We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. MATERIALS AND METHODS: In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). RESULTS: Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 (P < 0.001). CONCLUSIONS: The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.
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As the number of coronary computed tomography angiography (CTA) examinations is expected to increase, technologies to optimize the imaging workflow are of great interest. The aim of this study was to investigate the potential of artificial intelligence (AI) to improve clinical workflow and diagnostic accuracy in high-volume cardiac imaging centers. A total of 120 patients (79 men; 62.4 (55.0-72.7) years; 26.7 (24.9-30.3) kg/m2) undergoing coronary CTA were randomly assigned to a standard or an AI-based (human AI) coronary analysis group. Severity of coronary artery disease was graded according to CAD-RADS. Initial reports were reviewed and changes were classified. Both groups were similar with regard to age, sex, body mass index, heart rate, Agatston score, and CAD-RADS. The time for coronary CTA assessment (142.5 (106.5-215.0) s vs. 195.0 (146.0-265.5) s; p < 0.002) and the total reporting time (274.0 (208.0-377.0) s vs. 350 (264.0-445.5) s; p < 0.02) were lower in the human AI than in the standard group. The number of cases with no, minor, or CAD-RADS relevant changes did not differ significantly between groups (52, 7, 1 vs. 50, 8, 2; p = 0.80). AI-based analysis significantly improves clinical workflow, even in a specialized high-volume setting, by reducing CTA analysis and overall reporting time without compromising diagnostic accuracy.
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BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes. METHODS: We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. RESULTS: There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 âmL/m2. AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p â< â0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p â< â0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p â= â0.01). CONCLUSION: This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.
Assuntos
Fibrilação Atrial , Aprendizado Profundo , Neoplasias Pulmonares , Idoso , Fibrilação Atrial/diagnóstico por imagem , Detecção Precoce de Câncer , Feminino , Átrios do Coração/diagnóstico por imagem , Humanos , Neoplasias Pulmonares/diagnóstico por imagem , Masculino , Valor Preditivo dos Testes , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodosRESUMO
PURPOSE: To determine the association of early changes in posttreatment apparent diffusion coefficient (ADC) and venous enhancement (VE) with tumor size change after transarterial chemoembolization (TACE) by using an investigational semiautomated software. MATERIALS AND METHODS: This retrospective HIPAA-compliant study was approved by the institutional review board, with waiver of informed consent. Patients underwent magnetic resonance (MR) imaging at 1.5 T before TACE, as well as 1 and 6 months after TACE. Volumetric analysis of change in ADC and VE 1 month after TACE compared with pretreatment values was performed in 48 patients with 71 hepatocellular carcinoma (HCC) lesions. Diagnostic accuracy was evaluated with receiver operating characteristic (ROC) analysis, using tumor response at 6 months according to Response Evaluation Criteria in Solid Tumors (RECIST) and modified RECIST as end points. RESULTS: According to RECIST criteria, 6 months after TACE, 30 HCC lesions showed partial response (PR), 35 showed stable disease (SD), and six showed progressive disease (PD). Increase in ADC and decrease in VE 1 month after TACE were significantly different between PR, SD, and PD. At area under the ROC curve (AUC) analysis of the ADC increase, there was an AUC of 0.78 for distinguishing PR from SD and PD and an AUC of 0.89 for distinguishing PR and SD from PD. The AUC for decrease in VE was 0.73 for discrimination of PR from SD and PD and 0.90 for discrimination of PR and SD from PD. CONCLUSION: Volumetric analysis of increase in ADC and decrease in VE 1 month after TACE can provide an early assessment of response to treatment. Volumetric analysis of multiparametric MR imaging data may have potential as a prognostic biomarker for patients undergoing local-regional treatment of liver cancer.