■雪旺氏细胞鞘是良性的来源,缓慢扩张的肿瘤被称为听神经瘤(AN)。AN的诊断和治疗方法必须以患者为中心,考虑到独特的因素和偏好。
■这项研究的目的是研究机器学习和人工智能(AI)如何彻底改变AN管理和诊断程序。
■进行了全面的系统审查,其中包括来自公共数据库的同行评审材料。关于AN的出版物,AI,直到2023年12月的深度学习都被纳入了审查的范围。
■根据我们的分析,用于体积估计的AI模型,分割,肿瘤类型分化,与健康组织的分离已经成功开发。计算生物学的发展意味着人工智能可以有效地用于各个领域,包括生活质量评估,监测,机器人辅助手术,特征提取,影像组学,图像分析,临床决策支持系统,和治疗计划。
■为了更好的诊断和治疗,各种成像方式需要强大的发展,灵活的AI模型,可以处理异构成像数据。随后的调查应该集中于再现发现,以便标准化人工智能方法,这可以改变它们在医疗环境中的用途。
UNASSIGNED: Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences.
UNASSIGNED: The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures.
UNASSIGNED: A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review\'s purview.
UNASSIGNED: Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning.
UNASSIGNED: For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.