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Updated: Jul 4, 2025

Flexible Colonoscopy in Mice to Evaluate the Severity of Colitis and Colorectal Tumors Using a Validated Endoscopic Scoring System

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一个ASER AI/ML专家小组对可解释的交互性脏AAST分级图形用户界面原型进行了形成性用户研究.

Nathan Sarkar¹, Mitsuo Kumagai², Samantha Meyr²

¹University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA.

Emergency radiology

|February 1, 2024

概括

此摘要是机器生成的。

人工智能工具SpleenPro提高了脏损伤分级的客观性,并减少了放射科医生的解释时间. 用户反强调了需要集成的临床工作流特征,以提高诊断效用.

关键词:

一个AAST等级的年级.人工智能的人工智能是人工智能.发生出血发生出血脏脏是指脏的部分.创伤是一个创伤.用户接受用户的接受.

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科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

美国创伤外科手术协会 (AAST) 器官损伤量表是脏损伤分级的标准.
目前对AAST骨损伤分级的评审者间的协议仅为中度.
需要工具来提高骨损伤评估的客观性和一致性.

研究的目的:

评估SpleenPro,一个交互式可解释的AI/ML诊断辅助工具.
评估SpleenPro对放射科医生的停留时间,同意,临床实用性和AAST脏损伤分级的用户接受度的影响.
确定AI中的可解释性如何影响用户的感知和采用.

主要方法:

两位专家创伤放射科医生独立评分了76项CT研究的形脏损伤,有或没有AI/ML协助,在洗期后.
在四位专家小组成员面前,SpleenPro接口的三个版本具有不同的可解释性.
用户接受度通过使用利克特尺度和免费响应的结构化访谈来评估,重点是诊断效用,心理支持,工作量,信任和未来使用.

主要成果:

对于两位放射科医生来说,SpleenPro显著减少了解释时间.
在AI/ML的帮助下,AAST等级的加权科恩卡帕从0.53提高到0.70.
人工智能解释性增加与心理支持,努力/工作负载/丧,信任/可靠性以及未来使用可能性的更高得分相关.

结论:

SpleenPro在提高AAST骨损伤分级的客观性和为放射科医生提供心理支持方面表现出实用性.
用户研究表明,需要结合早期通知和分级功能,PACS集成和报告自动化.
形成性研究确定了将人工智能集成到临床工作流程中的关键概念,以改善骨损伤评估.