Elana Gotkine

THURSDAY, Jan. 26, 2023 (HealthDay Information) — Deep learning (DL) reconstructed knee magnetic resonance imaging (MRI) is diagnostically equal to traditional accelerated MRI for evaluating inner derangement of the knee in a scientific setting, in accordance with a research printed on-line Jan. 17 in Radiology.

Patricia M. Johnson, Ph.D., from the New York College Grossman College of Medication in New York Metropolis, and colleagues examined the diagnostic equivalence of prospectively accelerated DL-reconstructed knee MRI versus standard accelerated MRI for assessing inner derangement of the knee in a scientific setting. A DL mannequin was educated utilizing photographs from 298 scientific 3-T knee examinations. Between January 2020 and February 2021, sufferers clinically referred for knee MRI underwent a standard accelerated knee MRI protocol at 3 T adopted by an accelerated DL protocol. The equivalence of DL reconstruction of the pictures was assessed relative to the traditional photographs for detection of an abnormality. Six musculoskeletal radiologists reviewed every examination.

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General, 170 contributors have been assessed. The researchers discovered that for detection of abnormalities, the DL-reconstructed photographs had diagnostic equivalence to traditional photographs. For the DL versus standard photographs, the general picture high quality rating, averaged over six readers, was considerably higher.

“This work demonstrates that DL reconstruction of accelerated knee MRI at 3 T does work reliably in a real clinical setting with real patients,” the authors write. “Important future steps include correlating reader assessments of pathologic findings with a surgical ground truth and establishing robustness across a variety of scanners and vendors.”

A number of authors disclosed monetary ties to medical know-how and know-how industries.

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The Obsessed Guy
Hi, I'm The Obsessed Guy and I am passionate about artificial intelligence. I have spent years studying and working in the field, and I am fascinated by the potential of machine learning, deep learning, and natural language processing. I love exploring how these technologies are being used to solve real-world problems and am always eager to learn more. In my spare time, you can find me tinkering with neural networks and reading about the latest AI research.


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