Artificial Intelligence in Healthcare Competition (TEKNOFEST-2021): Stroke Data Set

被引：8

作者：

Koc, Ural ^{[1
]}

Sezer, Ebru Akcapinar ^{[2
]}

Ozkaya, Yasar Alper ^{[3
]}

Yarbay, Yasin ^{[4
]}

Taydas, Onur ^{[5
]}

Ayyildiz, Veysel Atilla ^{[6
]}

Kiziloglu, Huseyin Alper ^{[7
]}

Kesimal, Ugur ^{[8
]}

Cankaya, Imran ^{[9
]}

Besler, Muhammed Said ^{[1
]}

Karakas, Emrah ^{[4
]}

Karademir, Fatih ^{[10
]}

Sebik, Nihat Baris ^{[4
]}

Bahadir, Murat ^{[11
]}

Sezer, Ozgur ^{[4
]}

Yesilyurt, Batuhan ^{[10
]}

Varli, Songul ^{[10
,12
]}

Akdogan, Erhan ^{[10
,13
]}

Ulgu, Mustafa Mahir ^{[4
]}

Birinci, Suayip ^{[14
]}

机构：

[1] Ankara City Hosp, Dept Radiol, Ankara, Turkey

[2] Hacettepe Univ, Dept Comp Engn, Artificial Intelligence Div, Ankara, Turkey

[3] Simplex IT Inc, Telemed & Teleradiol, Ankara, Turkey

[4] Minist Hlth, Hlth Informat Syst, Ankara, Turkey

[5] Sakarya Univ, Dept Radiol, Fac Med, Sakarya, Turkey

[6] Isparta Suleyman Demirel Univ, Dept Radiol, Fac Med, Isparta, Turkey

[7] GOP Univ, Fac Med, Dept Radiol, Tokat, Turkey

[8] Ankara Numune Training & Res Hosp, Dept Radiol, Ankara, Turkey

[9] Van Training & Res Hosp, Dept Radiol, Van, Turkey

[10] Hlth Inst Turkey, Istanbul, Turkey

[11] Konya Tech Univ, Dept Comp Engn, Fac Engn & Nat Sci, Konya, Turkey

[12] Yildiz Tech Univ, Dept Comp Engn, Istanbul, Turkey

[13] Yildiz Tech Univ, Dept Mechatron Engn, Fac Mech Engn, Istanbul, Turkey

[14] Minist Hlth, Ankara, Turkey

来源：

EURASIAN JOURNAL OF MEDICINE | 2022年 / 54卷 / 03期

关键词：

Computer vision; stroke; data set; artificial intelligence; competition;

D O I：

10.5152/eurasianjmed.2022.22096

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Turkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Turkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared. pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a nondisclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to I of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable.

引用

页码：248 / 258

页数：11

共 29 条

[1]

[Anonymous], 2021, TEKNOFEST OFF WEBS

[2]

[Anonymous], 2011, AM ASTRONOMICAL SOC, P218

[3] A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT [J].