{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hsu CW"],"funding":["National Science and Technology Council"],"pagination":["4075-4089"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12701149"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["38(6)"],"pubmed_abstract":["The rapid advancement of digital pathology comes with significant challenges due to the diverse data formats from various scanning devices creating substantial obstacles to integrating artificial intelligence (AI) into the pathology imaging workflow. To overcome performance challenges posed by large AI-generated annotations, we developed an open-source project named Mainecoon for whole slide images (WSIs) using the Digital Imaging and Communications in Medicine (DICOM) standard. Our solution incorporates an AI model to detect non-alcoholic steatohepatitis (NASH) features in liver biopsies, validated with the DICOM Workgroup 26 Connectathon dataset. AI-generated results are encoded using the Microscopy Bulk Simple Annotations standard, which provides a standardized method supporting both ma"],"journal":["Journal of imaging informatics in medicine"],"pubmed_title":["Mainecoon: Implementing an Open-Source Web Viewer for DICOM Whole Slide Images with AI-Integrated PACS for Digital Pathology."],"pmcid":["PMC12701149"],"funding_grant_id":["112-2634-F-006-002","112-2321-B-075-003"],"pubmed_authors":["Lee YT","Chung PC","Kuo CT","Lien CY","Chu YC","Hsu CW","Yang SW","Hsu TH","Yao KH"],"additional_accession":[]},"is_claimable":false,"name":"Mainecoon: Implementing an Open-Source Web Viewer for DICOM Whole Slide Images with AI-Integrated PACS for Digital Pathology.","description":"The rapid advancement of digital pathology comes with significant challenges due to the diverse data formats from various scanning devices creating substantial obstacles to integrating artificial intelligence (AI) into the pathology imaging workflow. To overcome performance challenges posed by large AI-generated annotations, we developed an open-source project named Mainecoon for whole slide images (WSIs) using the Digital Imaging and Communications in Medicine (DICOM) standard. Our solution incorporates an AI model to detect non-alcoholic steatohepatitis (NASH) features in liver biopsies, validated with the DICOM Workgroup 26 Connectathon dataset. AI-generated results are encoded using the Microscopy Bulk Simple Annotations standard, which provides a standardized method supporting both ma","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-05-26T11:08:49.305Z","creation":"2026-05-24T03:12:14.712Z"},"accession":"S-EPMC12701149","cross_references":{"pubmed":["39966222"],"doi":["10.1007/s10278-025-01425-6"]}}