Artificial intelligence-assisted interpretation of systolic function by echocardiogram

Objective: Precise and reliable echocardiographic assessment of left ventricular ejection fraction (LVEF) is needed for clinical decision-making. Recently, artificial intelligence (AI) models have been developed to estimate LVEF accurately. The aim of this study was to evaluate whether an AI model could estimate an expert read of LVEF and reduce the interinstitutional variability of level 1 readers with the AI-LVEF displayed on the echocardiographic screen. Methods: This prospective, multicentre echocardiographic study was conducted by five cardiologists of level 1 echocardiographic skill (minimum level of competency to interpret images) from different hospitals. Protocol 1: Visual LVEFs for the 48 cases were measured without input from the AI-LVEF. Protocol 2: the 48 cases were again shown to all readers with inclusion of AI-LVEF data. To assess the concordance and accuracy with or without AI-LVEF, each visual LVEF measurement was compared with an average of the estimates by five expert readers as a reference. Results: A good correlation was found between AI-LVEF and reference LVEF (r=0.90, p<0.001) from the expert readers. For the classification LVEF, the area under the curve was 0.95 on heart failure with preserved EF and 0.96 on heart failure reduced EF. For the precision, the SD was reduced from 6.1±2.3 to 2.5±0.9 (p<0.001) with AI-LVEF. For the accuracy, the root-mean squared error was improved from 7.5±3.1 to 5.6±3.2 (p=0.004) with AI-LVEF. Conclusions: AI can assist with the interpretation of systolic function on an echocardiogram for level 1 readers from different institutions

Artificial intelligence-assisted interpretation of systolic function by echocardiogram

Objective Precise and reliable echocardiographic assessment of left ventricular ejection fraction (LVEF) is needed for clinical decision-making. Recently, artificial intelligence (AI) models have been developed to estimate LVEF accurately. The aim of this study was to evaluate whether an AI model could estimate an expert read of LVEF and reduce the interinstitutional variability of level 1 readers with the AI-LVEF displayed on the echocardiographic screen.Methods This prospective, multicentre echocardiographic study was conducted by five cardiologists of level 1 echocardiographic skill (minimum level of competency to interpret images) from different hospitals. Protocol 1: Visual LVEFs for the 48 cases were measured without input from the AI-LVEF. Protocol 2: the 48 cases were again shown to all readers with inclusion of AI-LVEF data. To assess the concordance and accuracy with or without AI-LVEF, each visual LVEF measurement was compared with an average of the estimates by five expert readers as a reference.Results A good correlation was found between AI-LVEF and reference LVEF (r=0.90, p&lt;0.001) from the expert readers. For the classification LVEF, the area under the curve was 0.95 on heart failure with preserved EF and 0.96 on heart failure reduced EF. For the precision, the SD was reduced from 6.1±2.3 to 2.5±0.9 (p&lt;0.001) with AI-LVEF. For the accuracy, the root-mean squared error was improved from 7.5±3.1 to 5.6±3.2 (p=0.004) with AI-LVEF.Conclusions AI can assist with the interpretation of systolic function on an echocardiogram for level 1 readers from different institutions

p62/SQSTM1 Plays a Protective Role in Oxidative Injury of Steatotic Liver in a Mouse Hepatectomy Model

Aims: Liver injury and regeneration involve complicated processes and are affected by various physio-pathological factors. We investigated the mechanisms of steatosis-associated liver injury and delayed regeneration in a mouse model of partial hepatectomy. Results: Initial regeneration of the steatotic liver was significantly delayed after hepatectomy. Although hepatocyte proliferation was not significantly suppressed, severe liver injury with oxidative stress (OS) occurred immediately after hepatectomy in the steatotic liver. Fas-ligand (FasL)/Fas expression was upregulated in the steatotic liver, whereas the expression of antioxidant and anti-apoptotic molecules (catalase/MnSOD/Ref-1 and Bcl-2/Bcl-xL/FLIP, respectively) and p62/SQSTM1, a steatosis-associated protein, was downregulated. Interestingly, pro-survival Akt was not activated in response to hepatectomy, although it was sufficiently expressed even before hepatectomy. Suppression of p62/SQSTM1 increased FasL/Fas expression and reduced nuclear factor erythroid 2-related factor-2 (Nrf-2)-dependent antioxidant response elements activity and antioxidant responses in steatotic and nonsteatotic hepatocytes. Exogenously added FasL induced severe cellular OS and necrosis/apoptosis in steatotic hepatocytes, with only the necrosis being inhibited by pretreatment with antioxidants, suggesting that FasL/Fas-induced OS mainly leads to necrosis. Furthermore, p62/SQSTM1 re-expression in the steatotic liver markedly reduced liver injury and improved liver regeneration. Innovation: This study is the first which demonstrates that reduced expression of p62/SQSTM1 plays a crucial role in posthepatectomy acute injury and delayed regeneration of steatotic liver, mainly via redox-dependent mechanisms. Conclusion: In the steatotic liver, reduced expression of p62/SQSTM1 induced FasL/Fas overexpression and suppressed antioxidant genes, mainly through Nrf-2 inactivation, which, along with the hypo-responsiveness of Akt, caused posthepatectomy necrotic/apoptotic liver injury and delayed regeneration, both mainly via a redox-dependent mechanism. Antioxid. Redox Signal. 21, 2515-2530

Abstracts of The Second Eurasian RISK-2020 Conference and Symposium

This abstract book contains abstracts of the various research ideas presented at The Second Eurasian RISK-2020 Conference and Symposium.The RISK-2020 Conference and Symposium served as a perfect venue for practitioners, engineers, researchers, scientists, managers and decision-makers from all over the world to exchange ideas and technology about the latest innovation developments dealing with risk minimization