AI enabling new workflows to expand access to care (part 1)
AI enabling new workflows to expand access to care (part 1)
Originally published on 4/25/2022 on byte2bedside.com
Examples of AI in healthcare typically involve machine learning (ML) models that can perform the same task a human expert performs manually (eg. diagnosing pneumonia on a chest X-ray). The hope is that these systems can be useful for augmenting (or even replacing) existing workflows for clinicians, but more or less preserve the same structure and approach towards diagnosing and treating disease.
I am intrigued by the next generation of how AI can impact healthcare: not by just performing a task that a human already is doing, but enabling new tasks that previously did not exist and fundamentally changing the structure of how healthcare can be delivered to expand access to high quality care.
Over the next few posts, I will be highlighting several examples of ML in published literature that I believe represents this potential opportunity. This post will describe an example of a use case in medical imaging.
First generation of AI in imaging: augment existing diagnostic tasks
Early examples of ML image classifiers in healthcare demonstrated promise in performing tasks that could potentially augment or even replace the workflows of human expert clinicians, such as identifying pneumonia on chest X-rays, pulmonary embolism on CT scans, or skin cancer from photos of skin lesions. The idea is that an AI system could help radiologists and dermatologists perform these existing diagnostic tasks more quickly and efficiently. There are already examples of these capabilities being operationalized into clinical workflows: Viz.ai developed ML classifiers that detect ischemic stroke and large vessel occlusions on head CT images and has even been successful at attaining a Medicare reimbursement code for their technology when used on hospitalized patients.
Next generation: AI enabling new diagnostic tasks
The earlier examples of AI all performed tasks that already existed in clinical medicine (eg. radiologists already routinely diagnose pulmonary emboli and strokes on CT scans, the AI is trained to perform the same task). Yet, we may only be scratching the surface in understanding the complex, hidden relationships between the information from imaging studies and an individual’s physiological and disease state.
The disease phenotypes that we traditionally diagnose from these studies are simply from the accumulated experiences of expert physicians who learned to interpret these images under existing mental models and knowledge structures (as well as the limitations of the human eye). What if we allow an AI system to take a second look, which has the benefit of accessing and interpreting every pixel, voxel, and frame of an imaging study, as well the ability to learn without these pre-existing mental models?
The following study illustrates one example of attempting to apply AI to enable a new diagnostic task on a CT scan: Automated coronary calcium scoring using deep learning with multicenter external validation.
A new way of detecting coronary artery calcification on routine non gated chest CTs
Measuring coronary artery calcium (CAC) can be a useful non-invasive method for detecting coronary artery disease and help risk stratify for treatment decisions. Calcium is radio-opaque, so can be visualized on CT scans. However, because the heart is a dynamically beating organ, the coronary arteries and surrounding muscle and tissue are not stationary, which makes it difficult for radiologists to precisely quantify CAC on regular CTs. To solve this problem, “cardiac gating” is added to the CT image acquisition process, which means that the CT scan is triggered during a specific portion of the cardiac cycle (ie. a point in time during the heartbeat) using attached electrocardiogram leads. Medications to slow down the patient’s heart rate are also typically given during the scanning process.
This specialized CT scan, known as a “gated coronary CT,” is currently required for patients who need to measure their CAC, but are much less widely available (and more expensive) compared to regular CT scans, which do capture coronary calcium but cannot be formally interpreted by radiologists to measure CAC. The result is a significant underutilization of CAC screening to detect latent coronary artery disease, representing a missed opportunity in the early detection and prevention of cardiovascular disease.
What if AI can calculate CAC from non gated routine CT scans?
This is what the research team that published this paper demonstrated with some early data. They trained a deep learning model that automatically calculates the CAC score from regular non gated chest CTs using data from Stanford Health Care, a tertiary academic medical center, and the Multi-Ethnic Study of Atherosclerosis (MESA) study, a multi-center prospective cohort. In a validation set of 42 patients from Stanford (which is also where the data for the training set came from) who also received gated coronary CTs, there was almost perfect agreement between the AI generated CAC score from regular CTs and the radiologist interpreted score from the gated coronary CTs. Agreement was moderate in a validation set of 46 patients from the MESA study. External validation at other sites also showed moderate to substantial agreement. The models also performed well in a binary classification task of detecting CAC greater or equal to 100.
The authors also reported the development and validation of a ML model that automatically calculates CAC from gated coronary CTs with high concordance with radiologist interpretations, which is exciting work and represents the opportunity to augment an existing diagnostic task.
I am, however, particularly excited about the results from the non gated CT model, which indicate the potential for AI to enable new diagnostic tasks and workflows, rather than just augment existing ones. Under the current standard of care, access to CAC screening is fairly limited, especially among more vulnerable populations (who are also often at higher risk of cardiovascular disease). Relying on only gated coronary CTs also misses all the opportunities for CAC detection from regular non coronary CT scans that people receive for other reasons. An AI system that could capture diagnostic and screening information from these regular CT scans could fundamentally change how we screen for CAC: by shifting from a centralized system gated (no pun intended) by a limited number of specialized centers to a more distributed system that can spread much more widely in the community.
There is much more work to do beyond this proof of concept paper. Models have to be developed on larger, more representative datasets and assessed for generalizability. Implementation feasibility, ranging from technical and clinical integration, will need to be explored and tested. It may even be possible that it turns out the ML is not as good as expected when stress tested under other conditions. However, I believe this foundational concept of AI extracting insights from previously untapped diagnostic tests to enable new workflows that broadly expand access to care is will drive important future work in AI.
Reference
Eng, D. et al. (2021). Automated coronary calcium scoring using deep learning with multicenter external validation. Npj Digital Medicine, 4(1). https://doi.org/10.1038/s41746-021-00460-1