AI improving digestive cancer diagnosis, but data-sharing obstacles remain
Artificial intelligence is helping to deliver earlier and better diagnoses of digestive cancers, but many challenges remain to widespread clinical application, not least limited sharing of medical imaging data between hospitals, and lack of standardization of protocols for medical imaging for AI, a group of researchers has concluded after a comprehensive survey of recent applications of the technology to these most deadly of cancers.
A paper describing their findings appeared in the journal Health Data Science on February 6.
The group wanted to survey the state of research into use of AI systems to assist with DSN diagnosis, and how experimental efforts thus far had worked across the four most common digestive system cancers. Their paper gives an overview of how far such research has developed and lays out the challenges yet to be overcome.
They note that there are two main AI approaches to DSN medical imaging: radiomics and deep learning. The first involves an AI that uses data-characterization algorithms to extract imaging features from the image. This involves segmentation of an image, or detailed ‘chunking’ out different sections of an image into different parts. Which pixel in an image is part of a tumor and which is something else? With radiomics, radiologists often manually label different parts to train the AI to understand and categorize these segments.
Both radiomics and deep learning profoundly depend on large, well-annotated datasets from a great many hospitals in order to build a robust internal model of tumors that can then be generalized to any patient.
This is where the first major challenge appears. Practices of acquisition and parameterization of such medical images vary substantially between different hospitals, affecting the robustness and thus generalizability of any AI model. In addition, there are a large number of medical images of DSNs, well-annotated image data are limited.
The researchers conclude, however, that there are multiple methods that can mitigate this problem, including image resampling; rotation, flipping, and shifting of images; and careful blurring of images to reduce image noise. In addition, standardization of protocols for medical imaging for AI should be able to improve reproducibility and comparability.
Moreover, high-quality datasets are usually not publicly available, which can hinder the validation and comparison of different AI models.
