Development and operation of a digital platform for sharing pathology image data.
Background: Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing a...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 9 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/3/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149631021&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149631021 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/3/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 149631021 149631021 NLM33812383 149631021 10.1186/s12911-021-01466-1 NLM33812383 149631021 ppf: 1 ppct: 8 formats: tig: atl: Development and operation of a digital platform for sharing pathology image data. aug: au: Kang, Yunsook Kim, Yoo Jung Park, Seongkeun Ro, Gun Hong, Choyeon Jang, Hyungjoon Cho, Sungduk Hong, Won Jae Kang, Dong Un Chun, Jonghoon Lee, Kyoungbun Kang, Gyeong Hoon Moon, Kyoung Chul Choe, Gheeyoung Lee, Kyu Sang Park, Jeong Hwan Jeong, Won-Ki Chun, Se Young Park, Peom Choi, Jinwook affil: Department of Biomedical Engineering, Seoul National University Hospital, Seoul, Republic of Korea sug: subj: Neoplasms Artificial Intelligence Algorithms Human Male Comparative Studies Multicenter Studies Evaluation Research Validation Studies Ferrans and Powers Quality of Life Index Male ab: Background: Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists.Methods: Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists' workload, AI-assisted annotation was established in collaboration with university AI teams.Results: A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models.Discussion: Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition.Conclusions: Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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