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相关概念视频

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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相关实验视频

Updated: Jun 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于深度学习的眼睛表面瘤疾病诊断模型.

Rie Sakata1, Taiyo Shijo1, Yuta Ueno2

  • 1Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan.

American journal of ophthalmology
|February 26, 2026
PubMed
概括

一种深度学习 (DL) 模型在诊断常见的眼睛表面瘤 (如皮和肢体皮质瘤) 中显示出高准确性. 然而,它对黑色素瘤等罕见恶性瘤的性能需要进一步改进.

关键词:
人工智能的人工智能是人工智能.马尔特淋巴瘤 (MALT) 是一种淋巴瘤.和pterygium,这是一个很好的方法.肢体皮质体的皮质体恶性黑色素瘤是一种恶性黑色素瘤.一个神经 (nevus)眼睛表面状瘤 (OSSN) 是一种疾病.眼球表面瘤 眼球表面瘤这是一个 pinguecula.

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 眼表面瘤包括一系列良性和恶性疾病.
  • 准确和及时的诊断对于有效的治疗和患者的结果至关重要.
  • 深度学习 (DL) 为医学诊断中的自动图像分析提供了潜力.

研究的目的:

  • 开发一种深度学习 (DL) 模型,用于诊断眼表面瘤.
  • 评估开发的DL模型的诊断性能.
  • 为了比较DL模型的性能与人类专家诊断.

主要方法:

  • 开发了一个基于YOLOv5架构的DL模型.
  • 该模型在7个疾病类别的1491张眼睛表面图像上进行了训练.
  • 使用299张外部图像验证了表现,并与角膜专家,眼科医生和住院医生的评估进行了比较.

主要成果:

  • DL模型实现了96.0%的正预测值 (PPV),超过了人类专家.
  • 对于pterygium (96.7%) 和肢体皮质 (93.5%) 观察到高度疾病特异性的PPV.
  • 该模型对大多数常见疾病具有高灵敏度和特异性,对罕见恶性瘤 (PPV 38.5%) 和MALT淋巴瘤 (PPV 57.9%) 等罕见恶性瘤的性能较低.

结论:

  • 该DL模型显示显著的希望作为一个支持的诊断工具,用于常见的眼睛表面瘤.
  • 需要进一步改进,以提高罕见和侵袭性恶性瘤的诊断准确度.
  • 这项研究强调了人工智能在增强眼科诊断能力方面的潜力.