Self-teaching AI helpful in diagnosing rare diseases

Investigators from the Mahmood Lab at Brigham and Women's Hospital have developed a deep learning algorithm that can teach itself to learn features which can then be used to find similar cases in large pathology image repositories.

Known as SISH (Self-Supervised Image search for Histology), the new tool acts like a search engine for pathology images and has many potential applications, including identifying rare diseases and helping clinicians determine which patients are likely to respond to similar therapies.

Modern electronic databases can store an immense amount of digital records and reference images, particularly in pathology through whole slide images (WSIs). However scalability remains a pertinent roadblock for efficient use. To solve this issue, researchers developed SISH, which teaches itself to learn feature representations which can be used to find cases with analogous features in pathology at a constant speed regardless of the size of the database.

In their study, the researchers tested the speed and ability of SISH to retrieve interpretable disease subtype information for common and rare cancers. The algorithm successfully retrieved images with speed and accuracy from a database of tens of thousands of whole slide images from over 22,000 patient cases, with over 50 different disease types and over a dozen anatomical sites. The speed of retrieval outperformed other methods in many scenarios, including disease subtype retrieval, particularly as the image database size scaled into the thousands of images. Even while the repositories expanded in size, SISH was still able to maintain a constant search speed.

Reference:

Chen, C et al. "Fast and scalable search of whole-slide images via self-supervised deep learning". Nature Biomedical Engineering. DOI: 10.1038/s41551-022-00929-8