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使用机器学习和深度学习技术对内动脉瘤和出血检测进行系统审查

S Nafees Ahmed¹, P Prakasam¹

¹School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.

Progress in biophysics and molecular biology

|July 27, 2023

概括

这项研究的重点是使用人工智能检测内动脉瘤和脑下关节下出血. 机器学习模型旨在改善这些关键神经疾病的早期诊断和患者结果.

科学领域:

神经学神经学
医疗成像医学成像
人工智能的人工智能

背景情况:

内动脉瘤构成显著的风险,破裂导致潜在的致命的脑下关节下出血.
早期检测下关节出血对于及时干预和改善患者存活率至关重要.
尽管取得了进展,但了解动脉瘤病理生理学和管理未破裂病例仍然存在挑战.

结论:

机器和深度学习模型在提高动脉瘤和出血检测的准确性和效率方面表现有前途.
对病理生理学和人工智能集成的进一步研究对于推进临床管理至关重要.
增强的诊断能力可以带来更好的患者结果和与这些脑血管事件相关的死亡率降低.

关键词:

深度学习是一种深度学习.内动脉瘤是一个内动脉瘤.内出血内出血机器学习机器学习

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