Search

Aims: To evaluate the accuracy and feasibility of artificial intelligence (AI) in left ventricular global longitudinal strain (GLS) analysis as compared to conventional (Manual) and semi-automated (SemiAuto) method in echocardiography (Echo)., Methods and Results: GLS validation was performed on 550 standard Echo exams by expert cardiologists. The performance of a beginner cardiologist without experience of GLS analysis was assessed on a subset of 90 exams. The AI employs fully automated view selection, classification, endocardial border tracing, and calculation of GLS from an entire Echo exam, while SemiAuto requires manual chamber view selection, and Manual involves full user input. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC) for all three methods. Agreement of measures included Pearson's correlation (R) and Bland-Altman analysis [median bias; limits of agreement (LOA)]. With an 89% feasibility the AI showed good agreement with Manual (R = 0.92, bias = 0.7% and LOA: -3.5 to 4.8%) and with SemiAuto (r = 0.90, bias = 0.10% and LOA: -4.5 to 4%). ICCs for GLS were 1.0 for AI, 0.93 for SemiAuto, and 0.80 for Manual. After the 55th analysis, the beginner showed stable time performance with Manual (171 s), contrasting with the consistent performance of SemiAuto (85-69 s) from the beginning. The highest agreement between beginner and expert readers was achieved with AI (R = 1.00), followed by SemiAuto (R = 0.85) and Manual (R = 0.74)., Conclusion: Automated GLS analysis enhances efficiency and accuracy in cardiac diagnostics, particularly for novice users. Integration of automated solutions into routine clinical practice could yield more standardized results., Competing Interests: Conflict of interest: A.L. reports honoraria or consultancy fees from Edwards Lifesciences, Boston Scientific, Abiomed, Novartis, Pfizer, Astra Zeneca, Boehringer, Abbott, MSD, Corvia, Daiichi and Meril; received grant support form Edwards Lifesciences and Novartis, and owns stock options from Transverse Medical, Picardia and Filterlex. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Left ventricular ejection fraction (LVEF) measurement in echocardiography (Echo) using the recommended modified biplane Simpson (MBS) method is operator-dependent and exhibits variability. We aimed to assess the accuracy of a novel fully automated (Auto) artificial intelligence (AI) in view selection and biplane LVEF calculation compared to MBS-Echo, with cardiac magnetic resonance imaging (CMR) as reference., Methods: Each of the 301 consecutive patients underwent CMR and Echo on the same day. LVEF was measured independently by Auto-Echo, MBS-Echo and CMR. Interobserver (n = 40) and test-retest (n = 14) analysis followed., Results: A total of 229 patients (76%) underwent complete analysis. Auto-Echo and MBS-Echo showed high correlations with CMR (R = 0.89 and 0.89) and with each other (R = 0.93). Auto underestimated LVEF (bias: 2.2%; limits of agreement [LOA]: -13.5 to 17.9%), while MBS overestimated it (bias: -2.2%; LOA: 18.6 to 14.1%). Despite comparable areas under the curves of Auto- and MBS-Echo (0.93 and 0.92), 46% (n = 70) of MBS-Echo misclassified LVEF by ≥5% units in patients with a reduced CMR-LVEF <51%. Although LVEF bias variability across different LV function ranges was significant (p < 0.001), Auto-Echo was closer to CMR for patients with reduced LVEF, wall motion abnormalities, and poor image quality than MBS-Echo. The interobserver correlation coefficient of Auto-Echo was excellent compared to MBS-Echo (1.00 vs. <0.91) for different readers. True test-retest variability was higher for MBS-Echo than for Auto-Echo (7.9% vs. 2.5%)., Conclusion: The tested AI has the potential to improve the clinical utility of Echo by reducing user-related variability, providing more accurate and reliable results than MBS., Competing Interests: Declaration of Competing Interest The authors report none., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test-retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, p < 0.001 for all) with a mean bias of +4.5% EF, -12 mL and -11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement ( n = 40, test-retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.

We report the case of a man with a diagnosis of invasive gastric adenocarcinoma associated with scattered osteoblastic metastases. This 65-year-old patient was admitted with nausea, vomiting and abdominal pain, in addition to loss of weight (13 Kg in 3 months). Upper digestive endoscopy revealed extensive infiltrating changes in the body of the stomach, and histopathologic data confirmed the diagnosis of a Borrmann IV gastric adenocarcinoma. Complementary exams were indicative of a gastric cancer with bone metastases, characterized by disseminated osteoblastic images of the scintigraphy of the skeleton. Palliative chemotherapy was the treatment of first choice. High serum levels of CA 19-9, CEA and alkaline phosphatase can constitute useful indicative tools of this challenging condition. Images from a scintigraphy study of the skeleton can characterize the type and extension of bone metastases. The aim of this case study is to report a case of a very uncommon condition, considering that disseminated osteoblastic metastases rarely develop in this kind of tumor.