人工智能应用于对良性妇科疾病的超声波成像:系统性审查
F Moro1,2, M T Giudice1, M Ciancia1
1Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
概括
妇科超声波中的人工智能 (AI) 主要集中在疾病检测的分类模型和测量细分工具上. 需要进一步的研究来解决偏见,并改进人工智能算法的外部验证.
科学领域:
- 妇科成像技术 妇科成像技术
- 医疗人工智能 医疗人工智能
- 超声波技术 超声波技术 超声波技术
背景情况:
- 人工智能 (AI) 越来越多地被整合到妇科超声波应用中.
- 在良性妇科疾病中对人工智能的现有证据综合不足.
- 本次审查解决了对人工智能在这个领域的作用进行全面评估的需要.
研究的目的:
- 系统地审查和评估现有的关于人工智能在超声波中用于良性妇科疾病的应用文献.
- 总结目前的研究状况,并确定AI应用的关键领域.
- 评估现有证据的质量并确定当前证据的局限性.
主要方法:
- 在Web of Science,PubMed和Scopus数据库中进行了系统的文献搜索,直到2024年8月.
- 包括涉及人工智能应用到超声波用于诊断和管理良性妇科疾病的研究.
- 包括的研究的质量评估是使用以人工智能为中心的诊断测试准确性研究 (QUADAS-AI) 质量评估工具进行的.
主要成果:
- 包括59项研究,重点关注诸如多囊性卵巢综合征 (PCOS),不孕不育,卵巢病理,子宫内膜问题,盆地疾病和子宫内膜异位症等疾病.
- 大多数研究 (40/59) 开发了AI分类模型,以区分正常病例和病理病例.
- 在对象选择和人工智能模型的外部验证中,注意到存在重大偏差风险,而参考标准和工作流域显示风险较低.
- 人工智能模型主要是开发和内部验证,重点是分类和自动测量任务.
结论:
- 目前对治疗良性疾病的妇科超声波人工智能的研究主要涉及开发分类模型和自动测量工具.
- 大多数研究的重点是区分正常和病态状态,量化特定结构.
- 确定了重要的方法限制,包括在受试者选择中存在高偏差风险和缺乏外部验证,突出了未来研究改进的领域.
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