使用计算机视觉的垂体腺瘤的放射学:一篇评论
Tomas Zilka1,2, Wanda Benesova3
1Saint Michal's Hospital, Bratislava, Slovakia.
Medical & biological engineering & computing
|July 16, 2024
概括
放射学,使用人工智能 (AI) 和计算机视觉 (CV),从放射图像中提取特征,以评估垂体腺瘤 (PA). 深度学习方法看起来很有前途,但需要大量的数据集和改进的可解释性,以便在未来临床使用.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 垂体腺瘤 (PA) 是常见的细胞瘤,需要准确的评估.
- 放射性图像分析对于PA决策支持和了解疾病进展至关重要.
- 放射学从医学图像中提取高维特征,用于定量分析.
研究的目的:
- 进行对 pituitary adenomas 的放射学研究进行系统的审查.
- 通过使用计算机视觉 (CV) 和人工智能,从放射学数据中分析自动化信息挖掘.
- 评估PA的放射学现状和未来潜力.
主要方法:
- 对34篇关于PA放射学和基于CV的分析的出版物的系统审查.
- 探索传统的CV,机器学习和深度学习 (DL) 方法.
- 综合比较和评估审查的研究作品.
主要成果:
- 分析论文的整体性能高,最佳准确度高达96%和AUC高达0.99.
- 深度学习 (DL) 方法在PA放射学方面显示出显著的前景.
- 放射性特征显示出成功临床应用的乐观潜力.
结论:
- 基于深度学习的放射学是一种非常有前途的途径,用于垂体腺瘤分析.
- 在为训练DL模型创建大型,高质量的数据集方面仍然存在挑战.
- 提高深度放射学的解释性对于临床翻译至关重要.
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