Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer

E. Yu. Timofeeva, С. R. Azilgareeva, A. O. Morozov, M. S. Taratkin, D. V. Enikeev
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Abstract

Currently, artificial intelligence (AI) has developed greatly and has become the subject of active discussions. This is because artificial intelligence systems are constantly being improved by expanding their computing capabilities, as well as obtaining massive data. Due to this, AI can help to set a diagnosis and select the most effective treatment. The study aimed to analyse the possibilities of AI in the diagnosis, treatment and monitoring of patients with renal cell carcinoma (RCC). AI shows great prospects in the diagnosis urinary system lesions, in the ability to differentiate benign and malignant neoplasm (due to machine learning systems), as well as in predicting the histological subtype of the tumor. AI can be used at the intraoperative stage (thanks to the integration of virtual 3D models during surgical interventions), which reduces the frequency of thermal ischemia and damage to the kidney cavity system. AI finds its application in histopathological evaluation: the AI model reaches 100.0% sensitivity and 97.1% specificity in the differential diagnosis of normal tissue from RCC. AI model algorithms may be used to identify patients at high risk of relapse requiring long-term follow-up, as well as to develop individual treatment and follow-up strategies. All the above proves the possibility of using AI in all stages of the management of patients with RCC. The implementation of AI in medical practise opens new perspectives for the interpretation and understanding of complex data inaccessible to clinicians.
人工智能在肾癌患者诊断、治疗和监测中的应用
目前,人工智能(AI)得到了很大的发展,并成为人们讨论的热门话题。这是因为人工智能系统通过扩展其计算能力以及获取大量数据而不断得到改进。因此,人工智能可以帮助确定诊断并选择最有效的治疗方法。该研究旨在分析人工智能在肾细胞癌(RCC)患者的诊断、治疗和监测中的可能性。人工智能在诊断泌尿系统病变、区分良性和恶性肿瘤(由于机器学习系统)以及预测肿瘤的组织学亚型方面显示出巨大的前景。人工智能可以在术中使用(由于在手术干预过程中集成了虚拟3D模型),从而减少了热缺血和肾腔系统损伤的频率。人工智能在组织病理学评价中的应用:人工智能模型在鉴别RCC和正常组织时灵敏度达到100.0%,特异性达到97.1%。人工智能模型算法可用于识别需要长期随访的复发高风险患者,以及制定个性化治疗和随访策略。以上都证明了人工智能在RCC患者管理的各个阶段的可能性。人工智能在医疗实践中的实施为解释和理解临床医生无法获得的复杂数据开辟了新的视角。
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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