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1.
Ann Cardiol Angeiol (Paris) ; 72(5): 101641, 2023 Nov.
Article in French | MEDLINE | ID: mdl-37703710

ABSTRACT

Chest pain is one of the major causes for admission in the Emergency Room in most countries and one of the principal reasons for urgent consultation with a cardiologist or a general practitioner. After clinical examination and initial biological measurements, substantial patients require further explorations. CT scan allows the search for pulmonary embolism in the early stage of pulmonary arteries iodine contrast exploration. During the same exam at the systemic arterial phase, the search for aortic dissection or coronary artery disease is possible while exploring the later contrast in the aortic artery. This triple rule-out exam allows correct diagnosis in case of acute chest pain with suspected pulmonary embolism, aortic dissection and other acute aortic syndromes or acute coronary syndrome. But X-rays are substantially increased as well as iodine contrast agent quantity while exam quality is globally decreased. Artificial intelligence may play an important role in the development of this protocol.

2.
Diagn Interv Imaging ; 103(6): 316-323, 2022 Jun.
Article in English | MEDLINE | ID: mdl-35090845

ABSTRACT

PURPOSE: The purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS). MATERIALS AND METHODS: The DLM was trained with 10,800 curved multiplanar reformatted (cMPR) CCTA images classified by an expert radiologist using the CAD-RADS. For each of the three main coronary arteries, nine cMPR images 40° apart acquired around each arterial circumference were then classified by the DLM using the highest probability. For the validation set composed of 159 arteries from 53 consecutive patients, the images were read by two senior and two junior readers; consensus of the two seniors was the reference standard. With the DLM, the majority vote for the nine images was used to classify each artery. Three groups (CAD-RADS 0, 1-2, or 3-4-5) and 2 groups CAD-RADS 0-1-2 or 3-4-5 (<50% vs. ≥50% stenosis) were used for comparisons with readers and consensus. Performance of the model and readers was compared to the consensus reading using the intraclass coefficient (ICC) and Cohen's kappa coefficient at the artery and patient levels. RESULTS: With the three groups at the artery level, the ICC of the DLM was 0.82 (95% CI: 0.75-0.88) and not significantly different from those of 3/4 readers; accuracy was 81%. With the binary classification, Cohen kappa coefficient of the DLM was 0.85 (95% CI: 0.69-0.94) and not significantly different from that of 3/4 readers; accuracy was 96%. At the patient level, sensitivity and specificity were 93% and 97% respectively, and the negative predictive value was 97%. CONCLUSION: The DLM detected ≥50% stenoses with performances similar to those achieved by senior radiologists.


Subject(s)
Coronary Artery Disease , Coronary Stenosis , Deep Learning , Computed Tomography Angiography/methods , Constriction, Pathologic , Coronary Angiography/methods , Coronary Stenosis/diagnostic imaging , Humans , Predictive Value of Tests
3.
Ann Cardiol Angeiol (Paris) ; 71(5): 325-330, 2022 Nov.
Article in French | MEDLINE | ID: mdl-35940969

ABSTRACT

The etiology of cardiac masses is often oncological or thrombotic, rarely inflammatory. Among heart tumors, the vast majority are metastatic. We describe the most frequent benign primary cardiac tumors and the most frequent malignant primary cardiac tumors and give information about the advantages of using a multi-modality approach for the accurate diagnosis of a cardiac mass using Computed Tomography Scanner and Magnetic Resonance Investigation.


Subject(s)
Heart Neoplasms , Humans , Heart Neoplasms/diagnosis , Tomography, X-Ray Computed , Magnetic Resonance Imaging , Heart
4.
Article in English | MEDLINE | ID: mdl-34971420

ABSTRACT

To investigate the feasibility and accuracy of cardiac multidetector computed tomography (MDCT) prosthesis sizing prior to ostium secundum atrial septal defect (ASD) percutaneous closure. Seventy consecutive patients were included in this retrospective bicentric study between May 2012 and June 2018. All underwent cardiac MDCT (primarily performed to rule out abnormal venous pulmonary return and coronary anomaly) and transesophageal echocardiography (TEE) before transcatheter closure: dimensions of the defect and peripheral rims were measured. Measurements of the defect obtained at TEE and MDCT were compared to prosthesis size. Our primary objective was the comparison of ASD maximal diameter obtained at MDCT (CT-Dmax) to prosthesis size. Intraclass correlation coefficient (ICC), Bland Altman plots and linear regression were calculated. Intra- and inter-observer agreements were calculated for MDCT defect measurements. Forty-three patients were finally included for defect measurements: 17 patients did not undergo transcatheter closure, and 10 had incomplete data. For CT-Dmax, ICC was 0.88 (CI 95% = [0.78-0.93]; p = 0.06); mean difference was - 0.8 ± 5.7 mm; regression linear equation was 0.9 × + 3.2 (p < 0.001). For maximal diameter at TEE versus prosthesis size, ICC was 0.46 (CI 95% = [0.21-0.61]; p = 0.003); mean difference was-6.0 ± 8.2 mm; regression linear equation was 0.91 × + 7.6 (p < 0.001). Intra- and inter-observer agreement for CT-Dmax were 0.97 (CI 95% = [0.95-0.98]) and 0.86 (CI 95% = [0.73-0.93]) respectively. MDCT is a reliable tool for sizing the defect of ostium secundum ASD, making it a complement or even an alternative to pre-procedural TEE.

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