人工智能在前列腺癌检测和分层中的应用:文献综述

IF 0.1 0 HUMANITIES, MULTIDISCIPLINARY Vestnik Sankt-Peterburgskogo Universiteta-Iskusstvovedenie Pub Date : 2023-01-01 DOI:10.21638/spbu11.2023.204
Ali Talyshinskii, Irina Kamyshanskaya, Andrey Mischenko, Bakhman Guliev, Rustam Bakhtiozin
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引用次数: 0

摘要

本文综述了目前使用磁共振成像数据利用人工智能识别和分类前列腺癌的方法。它概述了所使用的数据量,并突出了最常用的用于训练神经网络的序列。本文进一步介绍了所分析的神经网络的精度指标,并对每个指标进行了简明的解释。此外,该综述指出了使用磁共振成像数据检测和分类前列腺癌的当代神经网络的局限性,以及在其创建和实施过程中遇到的挑战。总之,这项综合分析深入研究了利用人工智能通过磁共振成像数据进行前列腺癌检测和分层的现有方法。它解决了用于神经网络训练的数据规模和首选磁共振成像序列。这篇综述提供了被评估的神经网络准确度指标的细分,阐明了它们各自的能力。此外,该综述还指出了目前通过磁共振成像数据开发的用于前列腺癌检测和分层的神经网络模型的缺陷,同时也认识到其开发和实际应用的复杂性。
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Application of artificial intelligence in the detection and stratification of prostate cancer: Literature review
This review examines the current methodologies employed in utilizing artificial intelligence for the identification and classification of prostate cancer using magnetic resonance imaging data. It outlines the volume of data utilized and highlights the most commonly sought-after sequences employed for training neural networks. The review further presents the accuracy metrics of the neural networks analyzed, accompanied by a succinct explanation of each metric. Furthermore, the review pinpoints the limitations associated with contemporary neural networks devised for the detection and classification of prostate cancer using magnetic resonance imaging data, as well as the challenges encountered during their creation and implementation.In summary, this comprehensive analysis delves into the existing approaches in leveraging artificial intelligence for prostate cancer detection and stratification through magnetic resonance imaging data. It addresses the data scale and preferred magnetic resonance imaging sequences employed for neural network training. The review provides a breakdown of accuracy indicators for the neural networks evaluated, elucidating their respective capabilities. Moreover, the review identifies the drawbacks associated with current neural network models developed for prostate cancer detection and stratification via magnetic resonance imaging data, while also recognizing the complexities involved in their development and practical application.
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来源期刊
CiteScore
0.20
自引率
50.00%
发文量
9
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