利用人工智能检测和描述胰腺病变：SFR 2023 人工智能数据挑战赛。

IF 4.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging Pub Date : 2024-07-23 DOI:10.1016/j.diii.2024.07.002

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引用次数: 0

摘要

目的：2023 年 SFR 数据挑战的目的是邀请研究人员开发人工智能（AI）模型，以识别腹部计算机断层扫描（CT）检查中是否存在胰腺肿块并区分良性和恶性胰腺肿块：从法国 18 个中心收集了门静脉期采集的匿名腹部 CT 检查结果。腹部 CT 检查分为三组，包括无病变的 CT 检查、良性胰腺肿块的 CT 检查或恶性胰腺肿块的 CT 检查。每个小组至少包括一名放射科医生、一名数据科学家和一名工程师。胰腺病变由放射科专家标注。CT 检查数据通过健康数据托管认证平台均衡分批分发。数据分为四批，两批用于培训，一批用于内部评估，一批用于外部评估。培训使用了来自 14 个中心的 83% 的数据，外部评估使用了来自其他四个中心的数据。对参与者进行排名的指标（即最终得分）是平均灵敏度、平均精确度和平均曲线下面积的加权平均值：总共 1037 例腹部 CT 检查被分为两个训练集（包括 500 例和 232 例 CT 检查）、一个内部评估集（包括 139 例 CT 检查）和一个外部评估集（包括 166 例 CT 检查）。训练集于 2023 年 9 月 7 日和 10 月 13 日分发，评估集于 2023 年 10 月 15 日分发。10 个团队共 93 名成员参加了数据挑战赛，最终最好成绩为 0.72.Conclusion：这项基于多中心 CT 数据的 SFR 2023 数据挑战赛表明，在真实数据中使用人工智能检测胰腺病变是可行的，但区分胰腺良性病变和恶性病变仍具有挑战性。

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Detection and characterization of pancreatic lesion with artificial intelligence: The SFR 2023 artificial intelligence data challenge

Purpose

The purpose of the 2023 SFR data challenge was to invite researchers to develop artificial intelligence (AI) models to identify the presence of a pancreatic mass and distinguish between benign and malignant pancreatic masses on abdominal computed tomography (CT) examinations.

Materials and methods

Anonymized abdominal CT examinations acquired during the portal venous phase were collected from 18 French centers. Abdominal CT examinations were divided into three groups including CT examinations with no lesion, CT examinations with benign pancreatic mass, or CT examinations with malignant pancreatic mass. Each team included at least one radiologist, one data scientist, and one engineer. Pancreatic lesions were annotated by expert radiologists. CT examinations were distributed in balanced batches via a Health Data Hosting certified platform. Data were distributed into four batches, two for training, one for internal evaluation, and one for the external evaluation. Training used 83 % of the data from 14 centers and external evaluation used data from the other four centers. The metric (i.e., final score) used to rank the participants was a weighted average of mean sensitivity, mean precision and mean area under the curve.

Results

A total of 1037 abdominal CT examinations were divided into two training sets (including 500 and 232 CT examinations), an internal evaluation set (including 139 CT examinations), and an external evaluation set (including 166 CT examinations). The training sets were distributed on September 7 and October 13, 2023, and evaluation sets on October 15, 2023. Ten teams with a total of 93 members participated to the data challenge, with the best final score being 0.72.

Conclusion

This SFR 2023 data challenge based on multicenter CT data suggests that the use of AI for pancreatic lesions detection is possible on real data, but the distinction between benign and malignant pancreatic lesions remains challenging.

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来源期刊

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.