This course offers a comprehensive overview to digital pathology and its integration with artificial intelligence tools, covering the full diagnostic pipeline from tissue preparation and immunohistochemistry to image analysis and clinical decision support. Participants will explore current technologies such as Whole Slide Imaging (WSI), antigen retrieval techniques, and multiplexed imaging platforms with more than 20 markers simultaneously, alongside the use of AI-based tools for automated quantification and diagnostic prediction. Through hands-on modules and case studies, the course highlights how digital workflows and machine learning are transforming pathology into a more precise, scalable, and data-driven discipline in both research and healthcare contexts.
The course is structured into six modules that provide a practical and up-to-date learning pathway on digital technologies applied to pathology, with a specific focus on the integration of digital pathology, immunohistochemistry, mass cytometry, and artificial intelligence for data analysis and interpretation in both diagnostic and research settings:
Module 1 – Tissue Preparation and Immunohistochemistry
Module 2 – Fundamentals of Digital Pathology
Module 3 – Multiplex Imaging and Mass Cytometry
Module 4 – Artificial Intelligence Tools and Image Analysis
Module 5 – Clinical Applications and Case Studies
Module 6 – Validation and Future Perspectives
Each module comprises multiple lessons, including demonstrative videos, in-depth reading materials, slides, and self-assessment quizzes. The course is designed to equip participants with practical skills that can be immediately applied in diagnostic, clinical, and research settings, with particular emphasis on the use of advanced digital tools and intelligent algorithms to support diagnostic processes.
Upon course completion, students will be able to:
- Describe and compare the workflows of traditional pathology and digital imaging pathology, highlighting their advantages, limitations, and differences in relation to traditional pathology. (Remember – Understand)
- Correctly set up histological and immunohistochemical preparation protocols, including fixation, embedding, sectioning, and staining, while assessing their impact on morphological and antigenic integrity. (Apply – Evaluate)
- Analyze and interpret the outcomes of antigen retrieval techniques (HIER and PIER), identifying critical variables such as pH, temperature, duration, and tissue type. (Analyze – Evaluate)
- Utilize digital tools for the management, visualization, and analysis of histological images, including software such as QuPath and VisioPharm, performing annotations, quantifications, and comparative analysis of tissue regions. (Apply – Analyze)
- Examine real clinical case studies and evaluate the integration of artificial intelligence tools in diagnostic support (e.g., tumor grading, biomarker identification), recognizing algorithmic biases and limitations. (Evaluate – Understand)
- Plan and design integrated workflows combining digital pathology, multiplex imaging, and AI-based tools, including clinical settings, with attention to reproducibility, validation, and regulatory compliance (e.g., CE, FDA). (Create – Evaluate – Apply)
- Critically discuss the role of emerging technologies in transforming diagnostic pathology, developing an informed and responsible perspective on the use of AI, automation, and digital infrastructures. (Evaluate – Create)
Passing a test (Quiz)
Basic knowledge of biology
Paola Cappello
Claudia Curcio
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC: