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客观视觉:用于眼科超声波解释和诊断的临床调整生成AI

Xiaocong Liu¹, An Shao², Bingtao Guan¹

¹State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine, and Transvascular Implantation Devices Research Institute, Hangzhou, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|January 8, 2026

概括

一个新的AI工具,OBUSight,通过生成诊断报告来帮助解释眼部B扫描超声波 (OBU) 图像. 这种AI提高了诊断效率和准确性,特别是对于眼科超声波实践中经验较少的临床医生来说.

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:

眼部B扫描超声波 (OBU) 对于诊断后部眼部疾病至关重要.
解读OBU图像给眼科医生带来了重大挑战.
现有的诊断方法需要专家解释,导致潜在的延迟和变化.

研究的目的:

开发和评估OBUSight,一个用于全面OBU图像解释的生成AI模型.
为了实现OBU图像的联合报告生成和疾病诊断.
提高眼科超声波实践中的诊断效率和准确性.

主要方法:

在一个大型的多中心OBU数据集上进行了OBUSight的训练和验证 (39654张图像,17586份报告,11381名患者).
模型性能使用自然语言生成 (NLG) 和临床疗效 (CE) 指标进行评估.
进行了专家评级和临床医生 (眼科医生,住院医生,学生) 的比较评估.

主要成果:

在报告生成质量和临床疗效方面,OBUSight的表现优于八种最先进的模型.
人工智能在多中心和多病态数据集中表现出强的性能.
专家审查证实了OBUSight能够以最小的纠正产生临床一致的报告的能力.
在评估中,OBUSight显著优于住院人员和学生,与眼科医生的表现相匹配,并缩短诊断时间.

结论:

OBUSight是一个有前途的AI工具,用于提高眼科超声波的诊断效率.
人工智能通过提供准确的诊断和生成报告来协助临床医生.
OBUSight对经验较少的临床医生特别有利,提高了整体实践质量.

关键词:

疾病诊断疾病诊断生成型的人工智能 (GAI)多模式学习是多模式学习.眼部B扫描超声波学术报告的生成报告的生成

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