Expanded Brain CT Dataset for the Development of AI Systems for Intracranial Hemorrhage Detection and Classification

Anna N. Khoruzhaya (), Tatiana M. Bobrovskaya, Dmitriy V. Kozlov, Dmitriy Kuligovskiy, Vladimir P. Novik, Kirill M. Arzamasov and Elena I. Kremneva
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Anna N. Khoruzhaya: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Russian Federation, Petrovka Street, 24, Building 1, 127051 Moscow, Russia
Tatiana M. Bobrovskaya: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Russian Federation, Petrovka Street, 24, Building 1, 127051 Moscow, Russia
Dmitriy V. Kozlov: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Russian Federation, Petrovka Street, 24, Building 1, 127051 Moscow, Russia
Dmitriy Kuligovskiy: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Russian Federation, Petrovka Street, 24, Building 1, 127051 Moscow, Russia
Vladimir P. Novik: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Russian Federation, Petrovka Street, 24, Building 1, 127051 Moscow, Russia
Kirill M. Arzamasov: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Russian Federation, Petrovka Street, 24, Building 1, 127051 Moscow, Russia
Elena I. Kremneva: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Russian Federation, Petrovka Street, 24, Building 1, 127051 Moscow, Russia

Data, 2024, vol. 9, issue 2, 1-9

Abstract: Intracranial hemorrhage (ICH) is a dangerous life-threatening condition leading to disability. Timely and high-quality diagnosis plays a huge role in the course and outcome of this disease. The gold standard in determining ICH is computed tomography. This method requires a prompt involvement of highly qualified personnel, which is not always possible, for example, in case of a staff shortage or increased workload. In such a situation, every minute counts, and time can be lost. The solution to this problem seems to be a set of diagnostic decisions, including the use of artificial intelligence, which will help to identify patients with ICH in a timely manner and provide prompt and quality medical care. However, the main obstacle to the development of artificial intelligence is a lack of high-quality datasets for training and testing. In this paper, we present a dataset including 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH, enriched with clinical and technical parameters, as well as the methodology of its generation utilizing natural language processing tools. The dataset is publicly available, which contributes to increased competition in the development of artificial intelligence systems and their advancement and quality improvement.

Keywords: computed tomography; intracranial hemorrhage; artificial intelligence; training dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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