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相关概念视频

Oral Cavity01:11

Oral Cavity

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The oral cavity, or the mouth, is a complex structure in humans that plays a vital role in our day-to-day lives. Its role is not only in chewing and swallowing food; it also plays a role in speech and facial expressions.
Teeth: The teeth are the hardest structures in our bodies. Humans have two sets of teeth throughout their lifetime: deciduous (baby) teeth and permanent teeth. Each tooth consists of several parts: the crown (visible part), the root (embedded in the jaw), enamel (hard outer...
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Oral Hypoglycemic Agents: Glinides01:06

Oral Hypoglycemic Agents: Glinides

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Repaglinide (Prandin) and Nateglinide (Starlix), known as glinides, are oral insulin secretagogues that stimulate insulin release from pancreatic β cells by closing the ATP-sensitive potassium channels (KATP channel). Repaglinide controls insulin release from pancreatic β cells by managing potassium efflux. It shares two binding sites with sulfonylureas and also has a unique site, indicating overlapping mechanisms of action. With a rapid onset and a 4-7 hour duration, it effectively...
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Psychosexual Stages of Personality: Oral01:16

Psychosexual Stages of Personality: Oral

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The oral stage is the initial phase of Sigmund Freud's theory of psychosexual development, occurring from birth to approximately 12 to 18 months. During this period, the infant's mouth serves as the primary source of pleasure, with actions such as sucking, chewing, biting, and drinking playing a crucial role in reducing tension. These activities are essential not only for nourishment but also for the infant's psychological and emotional satisfaction.
Weaning, typically occurring...
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Oral Hypoglycemic Agents: Sulfonylureas01:17

Oral Hypoglycemic Agents: Sulfonylureas

808
Sulfonylureas are oral hypoglycemic agents utilized in treating type 2 diabetes. They are characterized by their unique sulfonylurea chemical structure. The family of sulfonylureas is divided into generations. First-generation sulfonylureas, including tolbutamide (Orinase), chlorpropamide (Diabinese), and tolazamide (Tolinase), trigger insulin release from pancreatic β cells and enhance peripheral tissues' insulin sensitivity. The second-generation members, such as glipizide...
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Assessing Body Temperature - Oral01:14

Assessing Body Temperature - Oral

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Here are the steps to accurately measure oral temperature using an electronic thermometer:
Step 1:
Start by practicing proper hand hygiene to prevent the spread of microorganisms.
Step 2:
Take the thermometer out of the charging unit, switch it on, and wait for the ready sign.
Step 3:
Gently slide the probe cover until a click is heard. This simple action prevents cross-contamination and ensures the correct placement of the probe cover.
Step 4:
Instruct the patient to open their mouth and place...
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Non-Oral Extravascular Drug Absorption Routes01:15

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Non-oral extravascular routes, which encompass sublingual, buccal, topical, intramuscular, and inhalation methods, primarily utilize passive diffusion to transport drugs into the systemic circulation. The absorption rates and effectiveness of these routes depend on the drug's physicochemical properties, as well as the patient's anatomical and pathophysiological state.
Lipophilic drugs that are stable at salivary pH (6) and exhibit minimal binding to the oral mucosa are absorbed more...
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相关实验视频

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Modeling Oral-Esophageal Squamous Cell Carcinoma in 3D Organoids
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对口腔状细胞癌的深度视觉检测系统.

Kainat Akram1, Muhammad Aslam1, Talha Waheed1

  • 1Department of Computer Science, University of Engineering and Technology, Lahore, 54000, Pakistan.

Scientific reports
|January 19, 2026
PubMed
概括

这项研究介绍了一种使用EfficientNetB3的深度视觉检测系统 (DVDS),用于自动检测口腔状细胞癌 (OSCC) 从基因病理图像. 该系统实现了高精度,证明了更快,更一致的OSCC诊断的潜力,以改善患者的治疗结果.

关键词:
二元分类二元分类二元分类.癌症检测 癌症检测计算机辅助诊断是一种计算机辅助的诊断.有效的网络B3危害健康的风险 危害健康的风险多类检测检测多类检测口腔状细胞癌的癌症.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算病理学计算病理学

背景情况:

  • 口腔状细胞癌 (OSCC) 是一个重大的健康挑战,需要准确和及时的诊断.
  • 传统的组织病理学方法用于OSCC诊断是主观的和耗时的.
  • 人工智能 (AI) 的进步为对病原体图像的客观和高效分析提供了潜力.

研究的目的:

  • 开发和评估用于口腔状细胞癌 (OSCC) 检测的自动化深度视觉检测系统 (DVDS).
  • 为了比较不同深度学习模型 (EfficientNetB3,DenseNet121,ResNet50) 的性能,用于OSCC分类.
  • 评估系统的可靠性和稳定性在诊断OSCC从他的病理图像.

主要方法:

  • 我们使用了三个卷积神经网络 (CNN) 模型:EfficientNetB3,DenseNet121和ResNet50.
  • 在两个公共数据集上训练和评估模型:Kaggle口腔癌检测和NDB-UFES.
  • 采用数据增强,图像预处理和训练策略,如早期停止和减少LROnPlateau.

主要成果:

  • EfficientNetB3表现出卓越的性能,在二进制分类中达到97.05%的准确性,在多类分类中达到97.16%的准确性.
  • 该系统在两个数据集中表现出高精度,回忆,F1得分和特异性.
  • 与EfficientNetB3.3.相比,DenseNet121和ResNet50的准确性显著降低,而EfficientNetB3.3的准确性则明显降低.

结论:

  • 由EfficientNetB3驱动的深度视觉检测系统 (DVDS) 在OSCC诊断中显示出高可靠性.
  • 由人工智能驱动的方法可以显著简化诊断工作流程,并帮助病理学家.
  • 这项技术具有很强的临床部署潜力,以支持早期干预和增强患者护理.