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 manual and AI-generated annotations, promoting seamless integration of structured metadata with WSIs. We proposed a method by leveraging streaming and batch processing, significantly improving data loading efficiency, reducing user waiting times, and enhancing frontend performance. The web services of the AI model were implemented via the Flask framework, integrated with our viewer and an open-source medical image archive, Raccoon, with secure authentication provided by Keycloak for OAuth 2.0 authentication and node authentication at the National Cheng Kung University Hospital. Our architecture has demonstrated robustness, interoperability, and practical applicability, addressing real-world digital pathology challenges effectively.