探索生成预训练变压器-4-视觉的尼斯塔格木斯分类:发展和验证一个学生跟踪过程的验证
Masao Noda1,2, Ryota Koshu2, Reiko Tsunoda1
1Department of Otolaryngology, Mejiro University Ear Institute Clinic, 320 Ukiya, Iwatsuki-ku, Saitama-shi, Saitama, 339-8501, Japan, 81 48 797 3341.
JMIR formative research
|June 6, 2025
概括
这项研究探讨了使用生成预训练变压器4视觉 (GPT-4V) 进行自动化阴囊的分类,显示了改善管理的潜力. 虽然准确性各不相同,但GPT-4V提供了一种新的方法来诊断阴囊.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 传统的阴囊的分类依赖于主观的专家观察,导致变化和时间低效.
- 深度学习模型虽然有望实现自动分类,但面临着大型数据集和有限的适应性挑战.
- 现有的方法与特定的图像条件和模型复杂性作斗争.
研究的目的:
- 评估生成预训练变压器4视觉 (GPT-4V) 模型用于自动化阴囊的分类.
- 通过视频数据评估GPT-4V在分类各种阴囊类型方面的能力.
- 探索一种新的人工智能驱动的方法来改进尼斯塔格木斯诊断.
主要方法:
- 通过nystagmus录制视频开发了一个学生跟踪过程.
- 对GPT-4V分类模型的准确性进行了验证,并与尼斯塔格木斯记录进行了对比.
- 在六个尼斯塔格姆症类别中,使用二维坐标数据或学生轨迹图像评估了上下文学习方法.
主要成果:
- 通过GPT-4V模型,通过瞳孔追踪图像实现了总体分类准确率为37%.
- 使用瞳孔坐标作为输入结果的最大准确率为24.6%.
- 横向阴影的分类准确率达到69%,但垂直和扭曲部件的分类准确率较低.
结论:
- 像GPT-4V这样的生成人工智能模型显示出提高阴囊分类的准确性和效率的潜力.
- 进一步的改进需要扩大数据集和增强输入模式,以在所有阴囊类型中获得更好的性能.
- 最初对静止图像进行验证的GPT-4V可以适应视频分类,呈现出一种新的诊断方法.
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