An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL).

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Ultrasound Pub Date : 2025-01-22 DOI:10.1007/s40477-025-00983-3

Diana Donatello

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引用次数: 0

Abstract

Aim: o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease.

Material and methods: ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review.

Results: new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC.

Description: deline the main AI methods used in OL and OC that we can try to standardize in the clinical radiological and medical practice to ameliorate the patients diagnosis and theraphy.

Conclusion: through new AI methods it's possible to combine research into a SwarmDeepSurv, generate new data flow channels, create medical imaging data channels of OL and OC using AI and identify new biomarkers of OL and OC. .

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概述了在卵巢癌（OC）和卵巢淋巴瘤（OL）的分散临床机器学习（ML）中使用尖端人工智能（AI）建模来生成合成医疗数据（SMD）的情况。

目的：指出新的人工智能分析工具如何理解在OL和OC诊断和治疗过程中获得的数据，以帮助改善和规范这些疾病的患者途径。材料和方法：利用放射组学程序检测异质性OL和OC栖息地，并与基于成像的肿瘤分级相关，并进行文献综述。结果：在OL和OC中，使用尖端的人工智能（AI），已经产生了新的分析管道，用于整合成像和患者人口统计数据，并识别新的多组学生物标志物，用于反应预测和肿瘤分级。描述：列出OL和OC中使用的主要人工智能方法，我们可以在临床放射学和医学实践中尝试标准化，以改善患者的诊断和治疗。结论：通过新的AI方法，可以将研究结合到一个SwarmDeepSurv中，生成新的数据流通道，利用AI创建OL和OC的医学成像数据通道，识别OL和OC的新的生物标志物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Ultrasound RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.10

自引率

15.00%

发文量

133

期刊介绍： The Journal of Ultrasound is the official journal of the Italian Society for Ultrasound in Medicine and Biology (SIUMB). The journal publishes original contributions (research and review articles, case reports, technical reports and letters to the editor) on significant advances in clinical diagnostic, interventional and therapeutic applications, clinical techniques, the physics, engineering and technology of ultrasound in medicine and biology, and in cross-sectional diagnostic imaging. The official language of Journal of Ultrasound is English.