Publications by authors named "Malte E K Jensen"
Article Synopsis
- - Over 85 million CT scans are done annually in the US, with a significant portion focused on the abdomen, highlighting a need for efficient interpretation methods due to a shortage of radiologists.
- - To address this, researchers introduced Merlin, a 3D Vision Language Model (VLM) that uses both electronic health records and radiology reports for training without the need for manual annotations, utilizing a vast clinical dataset of millions of images and codes.
- - Merlin was evaluated on various tasks, including chronic disease prediction and report generation, showing better performance than current methods, demonstrating its potential to support radiologists in their work.
Article Synopsis
- Researchers developed and validated an open-source AI algorithm to detect different contrast phases in abdominal CT scans, using data from 739 exams across 200 patients.
- The algorithm achieved high accuracy rates of 92.3% for internal testing and 90.1% for external validation, indicating strong performance in identifying non-contrast, arterial, venous, and delayed phases.
- The study confirms the algorithm's effectiveness and potential for clinical applications, enhancing how medical professionals interpret CT scans for improved patient outcomes.
Purpose: The aim of this study was to assess and compare the arterial uptake of the inflammatory macrophage targeting PET tracer [Cu]Cu-DOTATATE in patients with no or known cardiovascular disease (CVD) to investigate potential differences in uptake.
Methods: Seventy-nine patients who had undergone [Cu]Cu-DOTATATE PET/CT imaging for neuroendocrine neoplasm disease were retrospectively allocated to three groups: controls with no known CVD risk factors (n = 22), patients with CVD risk factors (n = 24), or patients with known ischemic CVD (n = 33). Both maximum, mean of max and most-diseased segment (mds) standardized uptake value (SUV) and target-to-background ratio (TBR) uptake metrics were measured and reported for the carotid arteries and the aorta.
Semi-Supervised Analysis of the Electrocardiogram Using Deep Generative Models.
Annu Int Conf IEEE Eng Med Biol Soc
November 2021
Deep learning has gained increased impact on medical classification problems in recent years, with models being trained to high performance. However neural networks require large amounts of labeled data, which on medical data can be expensive and cumbersome to obtain. We propose a semi-supervised setup using an unsupervised variational autoencoder combined with a supervised classifier to distinguish between atrial fibrillation and non-atrial fibrillation using ECG records from the MIT-BIH Atrial Fibrillation Database.
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