State of the art review of AI in renal imaging.
Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading to a higher detection of incidental renal lesions. Differentiation between benign and malignant renal lesions is essential for effective treatment...
| Publicado en: | Abdominal Radiology Vol. 50; no. 11; pp. 5305 - 5324 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188952022&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188952022 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Nov2025 vid: 50 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188952022 184782100 10.1007/s00261-025-04963-3 188952022 ppf: 5305 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: State of the art review of AI in renal imaging. aug: au: Sheikhy, Ali Dehghani Firouzabadi, Fatemeh Lay, Nathan Jarrah, Negin Yazdian Anari, Pouria Malayeri, Ashkan affil: https://ror.org/01cwqze88 Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA sug: ab: Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading to a higher detection of incidental renal lesions. Differentiation between benign and malignant renal lesions is essential for effective treatment planning and prognosis. Renal tumors present numerous histological subtypes with different prognoses, making precise subtype differentiation crucial. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), shows promise in radiological analysis, providing advanced tools for renal lesion detection, segmentation, and classification to improve diagnosis and personalize treatment. Recent advancements in AI have demonstrated effectiveness in identifying renal lesions and predicting surveillance outcomes, yet limitations remain, including data variability, interpretability, and publication bias. In this review we explored the current role of AI in assessing kidney lesions, highlighting its potential in preoperative diagnosis and addressing existing challenges for clinical implementation. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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