法国放射性人工智能解决方案评估社区网格(DRIM法国人工智能倡议)。

IF 4.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Daphné Guenoun , Marc Zins , Pierre Champsaur , Isabelle Thomassin-Naggara , DRIM France AI Study Group
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引用次数: 0

摘要

目的:本研究的目的是验证放射学中人工智能(AI)解决方案的国家描述和分析网格。材料和方法:RAND-UCLA适当性方法是由DRIM法国IA小组的放射科医生专家为本声明论文选择的。这项研究由放射学界发起,涉及七个步骤,包括文献综述、模板开发、小组选择、小组会前调查、数据提取和分析、第二次也是最后一次小组会议以及数据报告。结果:该小组由七家软件供应商组成,其中三家使用传统放射学进行骨折检测,四家使用乳房X光检查进行乳腺癌症检测。在各个方面达成了共识,包括总体目标、主要目标、认证标记、集成、结果表达、取证方面和网络安全、性能和科学验证、公司和经济细节的描述、临床工作流程中可能的使用场景、数据库、人工智能工具的具体目标和指标。结论:本研究以癌症和骨折为实验指导,验证了由10个项目组成的放射学AI解决方案的描述和分析网格。该网格将帮助放射科医生选择相关且经过验证的人工智能解决方案。网格需要进一步发展,以包括其他机构和任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)

Purpose

The purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology.

Materials and methods

The RAND-UCLA Appropriateness Method was chosen by expert radiologists from the DRIM France IA group for this statement paper. The study, initiated by the radiology community, involved seven steps including literature review, template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting.

Results

The panel consisted of seven software vendors, three for bone fracture detection using conventional radiology and four for breast cancer detection using mammography. A consensus was reached on various aspects, including general target, main objective, certification marking, integration, expression of results, forensic aspects and cybersecurity, performance and scientific validation, description of the company and economic details, possible usage scenarios in the clinical workflow, database, specific objectives and targets of the AI tool.

Conclusion

The study validates a descriptive and analytical grid for radiological AI solutions consisting of ten items, using breast cancer and bone fracture as an experimental guide. This grid would assist radiologists in selecting relevant and validated AI solutions. Further developments of the grid are needed to include other organs and tasks.

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来源期刊
Diagnostic and Interventional Imaging
Diagnostic and Interventional Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
8.50
自引率
29.10%
发文量
126
审稿时长
11 days
期刊介绍: Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English. Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.
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