Deep learning

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Supervised learning approach

A new deep learning-based algorithm to predict relapse-free survival in papillary thyroid carcinoma

The tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC). Sebastian Stenman, researcher from the Institute for Molecular Medicine, and the Department of Pathology at the University of Helsinki, Finland, is developing and training a deep learning algorithm using supervised learning to detect and quantify the proportion of tall cells in PTC.

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Article • AI provides prognostic information

Next-generation deep learning models predict cancer survival

Deaths from cancer are currently estimated at 10 million each year worldwide. Conventional cancer staging systems aim to categorize patients into different groups with distinct outcomes. ‘However, even within a specific stage, there is often substantial variation in patient outcomes,’ Markus Plass, academic researcher from the Medical University of Graz, Austria, explained to Healthcare in…

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News • AI-enhanced imaging

Detecting Diabetes with Whole-Body MRI

Type 2 diabetes can be diagnosed with a whole-body magnetic resonance imaging (MRI) scan. This is shown by a current study by researchers from the German Center for Diabetes Research, the Institute of Diabetes Research and Metabolic Diseases of Helmholtz Zentrum München at the University of Tübingen, the Max Planck Institute for Intelligent Systems and Tübingen University Hospital. They used…

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News • Histological grading assistance

AI improves precision in breast cancer diagnosis

Researchers at Karolinska Institutet have developed an AI-based tool that improves the diagnosis of breast cancer tumours and the ability to predict the risk of recurrence. The greater diagnostic precision can lead to more personalised treatment for the large group of breast cancer patients with intermediate risk tumours. The results are published in the scientific journal Annals of Oncology.

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News • Imaging assistance

Deep learning method boosts MRI results without new data

When patients undergo an MRI, they are told to lie still because even the slightest movement compromises the quality of the images and can create blurred spots and speckles known as artifacts. Moreover, a long acquisition time is usually required to provide high-quality MRI images. A team of researchers from Washington University in St. Louis has found a new deep learning method that can minimize…

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Article • Diagnosis, prognosis, prediction

AI offers advances in cardiovascular imaging

Artificial Intelligence (AI) is providing numerous opportunities across clinical care in the field of cardiovascular imaging. While challenges remain, AI is being applied in terms of diagnosis and prognosis, defining cardiovascular imaging pathways, and image acquisition and analysis. It can also help cardiologists predict which patients may do well, or which treatments are best applied to those…

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