人工智能和深度学习方法在CT脊柱成像中的瘤应用-系统性审查
Wilson Ong1, Aric Lee1, Wei Chuan Tan1
1Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore.
Cancers
|September 14, 2024
概括
计算机断层扫描 (CT) 成像中的人工智能 (AI) 显示出脊柱瘤学的前景,有助于癌症检测,分类和结果预测. 需要进一步的研究来确认其临床有效性和将其纳入实践.
科学领域:
- 医疗成像医学成像
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 深度学习和人工智能在使用CT成像的脊柱瘤学中显示出潜力.
- 提高诊断准确性,治疗规划和患者的治疗结果是重点关注的关键领域.
研究的目的:
- 系统地审查人工智能 (AI) 在脊髓瘤计算机断层扫描 (CT) 成像中的应用.
- 综合关于人工智能在检测,分类,预后和治疗计划中的作用的证据.
主要方法:
- 在PRISMA指导的系统搜索中,发现了33项相关研究.
- 研究根据人工智能应用进行了分类:检测,分类,预后和治疗计划.
主要成果:
- 人工智能应用包括检测脊髓恶性瘤 (36.4%),分类 (33.3%) 和预后 (18.2%).
- 分类研究利用机器学习进行病变分化和放射学进行生物标志物分析.
- 预后研究专注于预测并发症和治疗结果.
结论:
- 在CT成像中的AI为脊椎瘤学工作流程效率和决策提供了潜在的好处.
- 局限性包括概括性,解释性和临床整合挑战.
- 进一步的研究对于验证AI的临床有效性和优化其常规使用至关重要.
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