Publications by authors named "M Buchwald"
Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites.
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- Researchers developed a new AI method to analyze routine CTAC scans from cardiac imaging to create volumetric measurements of various tissues, including fat and muscle, in the chest area.
- The study examined data from nearly 10,000 patients, finding that higher volumes of certain types of body fat (VAT, EAT, IMAT) were linked to an increased risk of all-cause mortality, whereas higher bone and skeletal muscle volumes were associated with lower mortality risk.
- This suggests that CTAC scans hold significant potential for identifying body composition markers that may help predict patient mortality risk beyond their current use.
Systematic hand-held echocardiography in patients hospitalized with acute coronary syndrome.
Eur Heart J Cardiovasc Imaging
September 2024
Aims: Transthoracic echocardiography is recommended in all patients with acute coronary syndrome but is time-consuming and lacks an evidence base. We aimed to assess the feasibility, diagnostic accuracy, and time efficiency of hand-held echocardiography in patients with acute coronary syndrome and describe the impact of echocardiography on clinical management in this setting.
Methods And Results: Patients with acute coronary syndrome underwent both hand-held and transthoracic echocardiographies with agreement between key imaging parameters assessed using kappa statistics.
Article Synopsis
- Low-dose computed tomography (CT) scans, used in hybrid myocardial perfusion imaging, provide valuable anatomical and pathological insights beyond just attenuation correction, which may be enhanced through AI-driven frameworks.
- This study analyzed data from over 10,000 patients, segmenting various structures and utilizing deep learning to assess coronary artery health, leading to improved all-cause mortality predictions.
- The comprehensive model integrating data from CT attenuation correction, myocardial perfusion imaging, and clinical factors outperformed other AI models in predicting mortality risk, particularly among patients with normal perfusion.
Preschool mental disorders are often associated with significant interpersonal problems, related to impaired affect recognition, theory of mind (ToM), and empathy. To date, these skills have not been studied together in preschoolers with externalizing behavior problems (EBPs). The aim of the present study was to investigate whether and to what extent preschool children with EBPs show impairments in affect recognition, ToM, and empathy.
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