The digitization of electrocardiogram paper records is an essential step to preserve and analyze cardiac data. This digitization process is not flawless as it involves several challenges, such as skew correction, binarization, and signal extraction. Various approaches have been proposed to address these challenges and recent studies have introduced innovative solutions, such as deep learning models and automation processes. Although existing approaches have shown promising results, there is a lack of common databases and metrics where authors could evaluate and compare their methods. Furthermore, the limited accessibility of code or software hinders the comparison process. Overall, while digitization of paper ECG recordings is important in advancing cardiology research, additional efforts are needed to standardize the evaluation process while improving code accessibility. This article provides a systematic review of this process.
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http://dx.doi.org/10.1016/j.jelectrocard.2023.05.009 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.
View Article and Find Full Text PDFSci Rep
January 2025
School of Finance and Accounting, Chengdu Jincheng College, Chengdu, 610097, Sichuan, China.
China's digital economy is currently thriving, with the "dual carbon" targets representing a significant pursuit of economic development. The role of the digital economy in achieving these targets warrants detailed discussion. Using urban panel data from China spanning 2011 to 2021, this paper empirically examines the impact of the digital economy on urban carbon emissions.
View Article and Find Full Text PDFSci Data
January 2025
University of South Dakota, Department of Biology, Vermillion, SD, 57069, USA.
Freshwater management and research frequently rely on trophic data to manage freshwater fishes, yet it is difficult to perform a simple search of dietary information for any one species. FishBase represents the largest effort to organize freshwater dietary data into a singular, navigable dataset. Nonetheless, FishBase excludes a large portion of the ecological literature because it was developed before the creation of most modern scientific search engines.
View Article and Find Full Text PDFOral Oncol
February 2025
Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China. Electronic address:
Purpose: To investigate the prognostic value of post-chemoradiotherapy 2-[F]FDG PET/CT in locally advanced nasopharyngeal carcinoma (LANPC) and develop an accurate prognostic model based on the 2-[F]FDG PET/CT results.
Methods: 900 LANPC patients who underwent pretreatment and post-chemoradiotherapy 2-[F]FDG PET/CT from May 2014 to August 2022 were included in the study. We divided the patients into two distinct cohorts for the purpose of our study: a training cohort comprising 506 individuals, included from May 2008 to April 2020, and a validation cohort consisting of 394 individuals, included from May 2020 to August 2022.
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