Artificial intelligence in adrenal imaging: A critical review of current applications - 04/01/23
, Martin Gaillard b, c, Anne-Ségolène Cottereau b, d, Elliot K. Fishman e, Guillaume Assié b, f, Anne Jouinot b, f, Christine Hoeffel g, Philippe Soyer a, b, Anthony Dohan a, b
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Highlights |
• | Artificial intelligence has currently potential applications in almost all fields of adrenal diseases. |
• | Although most studies are preliminary studies, they suggest that artificial intelligence may improve adrenal lesion classification, prognosis, and possibly management of patients. |
• | Some studies suggest that artificial intelligence algorithms should not be built using imaging data alone but should integrate biological data for better efficacy. |
• | Large prospective, studies with external validations are needed to build effective models that will help improve patients’ care. |
Abstract |
In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.
Il testo completo di questo articolo è disponibile in PDF.Keywords : Adrenal glands, Artificial intelligence, Deep learning, Machine learning
Abbreviations : AA, AI, AL, ACC, AUC, AUROC, CI, CNN, CT, DL, DNN, DSC, FDG, ML, MRI, PET/CT
Mappa
Vol 104 - N° 1
P. 37-42 - Gennaio 2023 Ritorno al numero
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