Reading and diagnosing chest x-ray images may be a relatively simple task for radiologists, but in reality, it is often a complex reasoning problem which often requires careful observation and knowledge of anatomical principles, physiology, and pathology. These factors make it difficult to develop a consistent and automated technique for reading chest x-ray images.
The NIH Clinical Center https://clinicalcenter.nih.gov recently released over 100,000 anonymized chest x-ray images and corresponding data to the scientific community with hopes of adding a large dataset of CT scans in the coming months.
This release enables researchers to freely access the datasets and increase their ability to enable computers to detect and diagnose disease. Ultimately, this artificial intelligence mechanism will enable clinicians to make better diagnostic decisions for patients.
By using this free dataset, the hope is that academic and research institutions will be able to enable computers to read and process extremely large amounts of scans, to confirm the results, and potentially identify other findings that may have been overlooked.
This advanced computer technology may also be able to:
- Help identify slow changes occurring over the course of multiple chest x-rays that might otherwise be overlooked
- Benefit patients in developing countries without access to radiologists to read their chest x-rays
- Create a virtual radiology resident that can later be taught to read more complex images