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Published: 2023-12-15
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DOI: https://doi.org/10.52852/tcncyh.v171i10.2005
Issue
Vol. 171 No. 10 (2023)
Section
Các bài báo
How to Cite
Hạnh, B. M. ., Ngân, V. T. ., & Trang, N. T. T. . (2023). 11. Effectiveness of artificial intelligence for convey non-dicom to dicom signal and automation in electroencephalogram interpretation. Journal of Medical Research, 171(10), 95-105. https://doi.org/10.52852/tcncyh.v171i10.2005

11. Effectiveness of artificial intelligence for convey non-dicom to dicom signal and automation in electroencephalogram interpretation

Bui My Hanh, Vuong Thi Ngan, Nguyen Thi Thuy Trang

Main Article Content

Abstract

EEG analysis faces many difficulties, especially for non-specialist doctors because this is a complex type of non-dicom data that has not been converted synchronously on the HIS system. Our study evaluates the results of artificial intelligence applications that convert data to the HIS system in dicom form and automatically identify and extract results. The research applies artificial intelligence to 900 records of normal people and people with neurological diseases from January 2021 to June 2023 in Hanoi Medical University Hospital. Our results show that c AI converted and synchronized 100% of data directly from specialized recorders to the HIS system as well as identified, analyzed, and displayed brain waves based on the characteristics of frequency, amplitude, and localization according to recorded milestones with an accuracy of up to 98% and automatically extracted 100% accurate results into answer sheets. Component time and total time are shortened by 8.75 times, saving 465 working hours with nearly 4.6 billion data points stored compared to the manual process. This is an effective support tool for doctors to access results easily, quickly, and accurately, especially for medical facilities lacking specialists and equipment.

Article Details

Keywords

Electroencephalogram, EEG, automation, artificial intelligence, machine learning, deep learning, efficiency, accuracy

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