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• W3084540266 abstract "Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With the technical development of new methods and the expansion to new fields of application, this trend is likely to become even more pronounced in future. Possible means of application range from automated image reading and classification to correlation with clinical outcomes and to technological applications in image processing and reconstruction. In the context of tumor imaging, that is, predominantly FDG or PSMA PET imaging but also bone scintigraphy, artificial intelligence approaches can be used to quantify the whole-body tumor volume, for the segmentation and classification of pathological foci or to facilitate the diagnosis of micro-metastases. More advanced applications aim at the correlation of image features that are derived by artificial intelligence with clinical endpoints, for example, whole-body tumor volume with overall survival. In nuclear medicine imaging of benign diseases, artificial intelligence methods are predominantly used for automated and/or facilitated image classification and clinical decision making. Automated feature selection, segmentation and classification of myocardial perfusion scintigraphy can help in identifying patients that would benefit from intervention and to forecast clinical prognosis. Automated reporting of neurodegenerative diseases such as Alzheimer's disease might be extended to early diagnosis—being of special interest, if targeted treatment options might become available. Technological approaches include artificial intelligence-based attenuation correction of PET images, image reconstruction or anatomical landmarking. Attenuation correction is of special interest for avoiding the need of a coregistered CT scan, in the process of image reconstruction artefacts might be reduced, or ultra low-dose PET images might be denoised. The development of accurate ultra low-dose PET imaging might broaden the method's applicability, for example, toward oncologic PET screening. Most artificial intelligence approaches in nuclear medicine imaging are still in early stages of development, further improvements are necessary for broad clinical applications. In this review, we describe the current trends in the context fields of body oncology, cardiac imaging, and neuroimaging while an additional section puts emphasis on technological trends. Our aim is not only to describe currently available methods, but also to place a special focus on the description of possible future developments." @default.
• W3084540266 created "2020-09-21" @default.
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• W3084540266 date "2021-03-01" @default.
• W3084540266 modified "2023-10-16" @default.
• W3084540266 title "Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives" @default.
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• W3084540266 cites W2121827537 @default.
• W3084540266 cites W2128225881 @default.
• W3084540266 cites W2155263737 @default.
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• W3084540266 doi "https://doi.org/10.1053/j.semnuclmed.2020.08.003" @default.
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