Deep learning

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News • "Candycrunch"

AI and mass spectrometry to find cancer clues at lightning speed

Researchers have developed an AI model that increases the potential for detecting cancer through sugar analyses. The AI model is faster and better at finding abnormalities than current methods.

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News • Three-dimensional tissue processing

Pathology performs leap into 3D with AI

Adding a new dimension to pathology: Researchers explore new, deep learning models that can use 3D pathology datasets to make clinical outcome predictions for curated prostate cancer specimens.

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News • Atrial fibrillation early warning

Deep learning predicts heart arrhrythmia 30 minutes in advance

Researchers have developed a deep-learning model that predicts the transition from a normal cardiac rhythm to atrial fibrillation 30 minutes before onset, with an accuracy of around 80%.

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News • Osteosarcoma model

Bone cancer prognosis enhanced via deep learning

Osteosarcoma is the most prevalent malignant bone tumor. Now, researchers have developed a machine-learning model to predict the density of viable tumor cells after surgery and chemotherapy treatment.

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News • Point-of-care testing

New AI tool detects Covid-19 in lung ultrasound images

Using ultrasound imaging to detect Covid-19 infections, a new automated detection tool could help doctors in the emergency room diagnose patients quickly and accurately.

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News • Voxel-wise classification

Deep learning tool uses MRI to enhance brain tumor diagnosis

A novel AI-based, non-invasive diagnostic tool enables accurate brain tumor diagnosis, outperforming current classification methods. The tool leverages MRI information to aid clinical decision making.

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Article • Need for diversity in training datasets

Artificial intelligence in healthcare: not always fair

Machine learning and AI are playing an increasingly important role in medicine and healthcare, and not just since ChatGPT. This is especially true in data-intensive specialties such as radiology, pathology or intensive care. The quality of diagnostics and decision-making via AI, however, does not only depend on a sophisticated algorithm but – crucially – on the quality of the training data.

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