ArticleThe Eurasian journal of medicine2022
Artificial Intelligence in Healthcare Competition (TEKNOFEST-2021): Stroke Data Set.
Article in The Eurasian journal of medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
- A wavelet-enhanced state space model for multi-disease diagnosis across chest X-ray, CT, and MRI.Quantitative imaging in medicine and surgery · 2026Article
- AGTS-Net: Anatomically Guided Two-Stage Network for Actionable Stroke Classification in Complete NCCT Studies.Bioengineering (Basel, Switzerland) · 2026Article
- Multi-type stroke lesion segmentation: comparison of single-stage and hierarchical approach.Medical & biological engineering & computing · 2025Article
- The Aphasia Recovery Cohort, an open-source chronic stroke repository.Scientific data · 2024Article
- Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022.European radiology · 2024Article
- Optimization of Radiology Diagnostic Services for Patients with Stroke in Multidisciplinary Hospitals.Materia socio-medica · 2024Review
- Artificial Intelligence in Stroke Imaging: A Comprehensive Review.The Eurasian journal of medicine · 2023Article
- Current trends and future perspectives of stroke management through integrating health care team and nanodrug delivery strategy.Frontiers in cellular neuroscience · 2023Review
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, 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
methodsData set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, 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.
resultsThe competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 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.
conclusionArtificial 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.