Abdominal Radiology, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 15, 2024
Язык: Английский
Computers in Biology and Medicine, Год журнала: 2024, Номер 173, С. 108337 - 108337
Опубликована: Март 24, 2024
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In past decade, artificial intelligence (AI) technology has undergone rapid development in field clinical medicine, bringing advantages efficient data processing accurate model construction. Promisingly, AI-based radiomics played increasingly important role decision-making HCC patients, providing new technical guarantees for prediction, diagnosis, prognostication. this review, we evaluated current landscape AI management HCC, including its individual treatment, survival Furthermore, discussed remaining challenges future perspectives regarding application HCC.
Язык: Английский
Процитировано
20
Japanese Journal of Radiology, Год журнала: 2024, Номер 42(10), С. 1100 - 1109
Опубликована: Июнь 10, 2024
Язык: Английский
Процитировано
6
Japanese Journal of Radiology, Год журнала: 2024, Номер unknown
Опубликована: Окт. 2, 2024
Abstract Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous ablation, for malignant tumors in a minimally invasive manner. As other medical fields, the application of artificial intelligence (AI) interventional has garnered significant attention. This narrative review describes current state AI applications based on recent literature. A literature search revealed rapid increase number studies relevant to this topic recently. Investigators have attempted use various tasks, automatic segmentation organs, tumors, treatment areas; simulation; improvement intraprocedural image quality; prediction outcomes; detection post-treatment recurrence. Among these, AI-based outcomes been most studied. Various deep conventional machine learning algorithms proposed these tasks. Radiomics often incorporated into models. Current suggests that is potentially useful aspects oncology, from planning follow-up. However, methods discussed are still at research stage, few implemented clinical practice. To achieve widespread adoption technologies procedures, further their reliability utility necessary. Nevertheless, considering progress field, will be integrated practices near future.
Язык: Английский
Процитировано
6
La radiologia medica, Год журнала: 2024, Номер 129(9), С. 1275 - 1287
Опубликована: Авг. 3, 2024
Язык: Английский
Процитировано
5
Japanese Journal of Radiology, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 14, 2024
To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), learning-based (DLR), hybrid iterative (HIR) algorithms.
Язык: Английский
Процитировано
4
La radiologia medica, Год журнала: 2024, Номер unknown
Опубликована: Авг. 30, 2024
Acute pancreatitis (AP) is a common disease, and several scores aim to assess its prognosis. Our study aims automatically recognize mild AP from computed tomography (CT) images in patients with acute abdominal pain but uncertain diagnosis clinical serological data through Radiomic model based on formal methods (FMs).
Язык: Английский
Процитировано
3
Japanese Journal of Radiology, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 16, 2024
Abstract In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan’s contributions to field. first part introduce various AI in optimizing different aspects MRI process, including scan protocols, patient preparation, image acquisition, reconstruction, and postprocessing techniques. Additionally, examine AI’s growing influence decision-making, particularly areas such as segmentation, radiation therapy planning, reporting assistance. By emphasizing studies conducted Japan, highlight nation’s advancement MRI. latter characteristics that make Japan unique environment for development implementation examinations. healthcare landscape is distinguished by several key factors collectively create fertile ground research development. Notably, boasts one highest densities scanners per capita globally, ensuring widespread access exam. national health insurance system plays pivotal role providing scans all citizens irrespective socioeconomic status, which facilitates collection inclusive unbiased data across diverse population. extensive screening programs, coupled collaborative initiatives like Medical Imaging Database (J-MID), enable aggregation sharing large, high-quality datasets. With its technological expertise infrastructure, well-positioned meaningful MRI–AI domain. The efforts researchers, clinicians, technology experts, those will continue advance future MRI, potentially leading improvements care efficiency.
Язык: Английский
Процитировано
3
Japanese Journal of Radiology, Год журнала: 2025, Номер unknown
Опубликована: Янв. 25, 2025
Язык: Английский
Процитировано
0
Academic Radiology, Год журнала: 2025, Номер unknown
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
BMC Medical Imaging, Год журнала: 2024, Номер 24(1)
Опубликована: Апрель 12, 2024
Abstract Background Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics staging fibrosis. Method After literature search electronic databases (Embase, Ovid, Science Direct, Springer, Web Science), studies were selected by following precise eligibility criteria. The quality included was assessed, meta-analyses performed to achieve pooled estimates area under receiver-operator curve (AUROC), accuracy, sensitivity, specificity fibrosis compared histopathology. Results Fifteen (3718 patients; age 47 years [95% confidence interval (CI): 42, 53]; 69% CI: 65, 73] males) included. AUROC values for detecting significant (F2-4), advanced (F3-4), cirrhosis (F4) 0.91 [95%CI: 0.89, 0.94], 0.92 0.90, 0.95], 0.94 0.93, 0.96] training cohorts 0.89 0.83, 0.91], 0.93 0.91, 0.95] validation cohorts, respectively. For diagnosing fibrosis, sensitivity 84.0% 76.1, 91.9], 86.9% 76.8, 97.0], 92.7% 89.7, 95.7] 75.6% 67.7, 83.5], 80.0% 70.7, 89.3], 92.0% 87.8, 96.1] Respective 88.6% 83.0, 94.2], 88.4% 81.9, 94.8], 91.1% 86.8, 95.5] 86.8% 83.3, 90.3], 94.0% 89.5, 98.4], 88.3% 84.4, 92.2] cohorts. Limitations use several methods feature selection classification, less availability evaluating a particular radiological modality, lack direct comparison between radiology radiomics, external validation. Conclusion Although offers good diagnostic accuracy its clinical practice is not as clear at present due comparability constraints.
Язык: Английский
Процитировано
3