AI-Enhanced Diagnostic Imaging (AIEDI)

AI-Enhanced Diagnostic Imaging uses artificial intelligence and deep learning algorithms to help radiologists interpret medical images such as X-rays, MRI scans, and CT scans. This type of diagnostic imaging can reduce screening errors, improve clinical decision making, and accelerate patient care by automating the detection and quantification of abnormalities.

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"Diagnostic performance of deep learning–based reconstruction algorithm in 3D MR neurography"

This research was conducted by Falko Ensle, Malwina Kaniewska, Anja Tiessen, Maelene Lohezic, Jonas M. Getzmann, and Roman Guggenberger. It was published in the journal Skeletal Radiology. The study evaluates the diagnostic performance of a deep learning–based reconstruction method in 3D MR neurography for the assessment of the brachial and lumbosacral plexus.

"AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis"

This research was conducted by V. Romeo, P. Clauser, S. Rasul, P. Kapetas, P. Gibbs, P. A. T. Baltzer, M. Hacker, R. Woitek, T. H. Helbich, and K. Pinker. It was published in the European Journal of Nuclear Medicine and Molecular Imaging. The study assesses whether a radiomics and machine learning model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions.

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