Digitizing Pathology: Enhancing Tumor Diagnosis with AI and Whole Slide Imaging

Until recently, pathology has relied heavily on manual methods, such as creating glass slides and examining tissue samples with a microscope, without much help from digital technology. The conversion of physical glass slides to digital whole slide images makes storage easier, enables remote access, and allows the use of advanced computational tools like Augmented Intelligence (AI) to assist in analysis, improving diagnostic accuracy, consistency, and collaboration among pathologists. Our project builds on these advancements by digitizing traditional pathology workflows and integrating AI to assist pathologists in diagnosing and assessing a variety of tumors. Certain features, such as anaplasia can be difficult to define consistently between pathologists, but AI has the potential to provide a more standardized and accurate assessment. The Children’s Brain Tumor Network (CBTN) whole slide image collection will play a crucial role in enhancing the AI model by providing a diverse and expansive dataset. This diversity helps reduce biases in the model, ensuring that it is more reliable and generalizable across different tumor types. Through the adoption of digital pathology and AI, we aim to improve diagnostic accuracy and ultimately contribute to better patient care and outcomes.