使用公共数据集的放射肿瘤学应用人工智能

IF 2.6 3区 医学 Q3 ONCOLOGY
Kareem A. Wahid , Enrico Glerean , Jaakko Sahlsten , Joel Jaskari , Kimmo Kaski , Mohamed A. Naser , Renjie He , Abdallah S.R. Mohamed , Clifton D. Fuller
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引用次数: 6

摘要

人工智能(AI)具有对放射肿瘤学领域产生积极影响的非凡潜力。然而,开发放射肿瘤学人工智能模型需要大型策划数据集(通常涉及成像数据和相应的注释)。重要的是,最近建立的可查找、可访问、可互操作、可重用(FAIR)科学数据管理原则,使越来越多的放射肿瘤学相关数据集能够通过数据存储库传播,从而作为人工智能模型构建的丰富数据源。本文回顾了放射肿瘤学数据传播的当前和未来状态,特别强调了已发布的成像数据集、人工智能数据挑战和相关基础设施。此外,我们提供了FAIR数据传播协议的历史背景,当前放射肿瘤学数据分布的困难,以及关于数据传播的建议,以便最终在人工智能模型中使用。通过公平原则和标准化的数据传播方法,放射肿瘤学人工智能研究不会有任何损失,反而会有所收获。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Artificial Intelligence for Radiation Oncology Applications Using Public Datasets

Artificial Intelligence for Radiation Oncology Applications Using Public Datasets

Artificial Intelligence for Radiation Oncology Applications Using Public Datasets

Artificial Intelligence for Radiation Oncology Applications Using Public Datasets

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.

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来源期刊
CiteScore
5.80
自引率
0.00%
发文量
48
审稿时长
>12 weeks
期刊介绍: Each issue of Seminars in Radiation Oncology is compiled by a guest editor to address a specific topic in the specialty, presenting definitive information on areas of rapid change and development. A significant number of articles report new scientific information. Topics covered include tumor biology, diagnosis, medical and surgical management of the patient, and new technologies.
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