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相关概念视频

Urinary Bladder01:23

Urinary Bladder

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The urinary bladder is a hollow, muscular sac that temporarily stores urine before it is expelled from the body. It can hold approximately 600 mL of urine prior to micturition. The bladder is retroperitoneal and located behind the pubic symphysis in the pelvic floor.
In males, the bladder is situated in front of the rectum, while in females, it is positioned anterior to the vagina and uterus. The bladder floor contains an inverted triangular area called the trigone, defined by the two ureteric...
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相关实验视频

Updated: Jan 9, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

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注意启用可解释的AI用于膀癌复发预测预测.

Saram Abbas, Naeem Soomro, Rishad Shafik

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    这项研究引入了一种可解释的深度学习模型,用于预测非肌肉侵入性膀癌复发. 人工智能框架提高了预测准确性,并为更好的患者管理提供了个性化的见解.

    科学领域:

    • 在瘤学瘤学.
    • 人工智能的人工智能
    • 医疗信息学 医疗信息学

    背景情况:

    • 非肌肉侵入性膀癌 (NMIBC) 的复发率很高 (70-80%),导致重复手术和增加成本.
    • 目前对NMIBC复发的预测工具往往不准确,缺乏个性化.
    • 有效的预测对于管理患者护理和医疗保健资源分配至关重要.

    研究的目的:

    • 开发一个可解释的深度学习框架,以改善NMIBC复发的预测.
    • 通过整合矢量嵌入和注意力机制来提高预测性能.
    • 为患者提供有关复发风险因素的具体见解.

    主要方法:

    • 开发了一个深度学习框架,其中包含了对分类变量 (例如吸烟状态,静脉内治疗) 的矢量嵌入.
    • 利用注意力机制来识别有影响力的特征以进行个性化风险评估.
    • 使用表格数据评估模型性能,将其与传统的统计方法进行比较.

    主要成果:

    • 在预测NMIBC复发方面取得了70%的准确性,超过了传统的统计模型.
    • 该模型通过特征关注提供了临床医生友好的,患者一级的复发风险解释.
    • 确定了新的复发因素,包括手术持续时间和住院时间,以前没有考虑过.

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    结论:

    • 拟议的可解释深度学习框架显著改善了NMIBC复发预测.
    • 该模型提供了宝贵的患者特定见解,有助于临床决策和个性化管理.
    • 这种方法通过结合以前被忽视的因素和提高模型透明度来推进NMIBC风险评估.