Imagine having symptoms that could be leukemia, but you can't get to a specialist for the complex tests needed to confirm it. This is a reality for many people around the world. A team looked at whether an artificial intelligence (AI) tool could help by using just the results from standard blood tests to predict the type of leukemia a person might have. They tested it on a large, diverse group of over 6,200 patients from 20 different centers worldwide. The tool was very good at spotting certain types, like acute myeloid leukemia and a specific subtype called promyelocytic leukemia. But there was a big catch: to get that high accuracy, the tool had to refuse to make a prediction for the vast majority of patients—between 71% and 93% of the time. That's not very helpful for doctors. So, they refined the tool using a different method. This new version was less likely to refuse a prediction, excluding only about 12% of patients, and its accuracy for spotting acute myeloid leukemia in those uncertain cases improved. They also specifically retrained the tool to work better for children. The work shows that AI could one day be a useful support tool, helping more people get a faster, initial indication of their condition using tests they can already get.
Can a simple blood test help diagnose leukemia? An AI tool shows promise but still needs work.
Plain Language Summary
What this means for you:
An AI tool can predict leukemia types from basic lab work, but it needs to be more confident to help most patients. What this means for you:
An AI tool can predict leukemia types from basic lab work, but it needs to be more confident to help most patients. View Original Abstract ↓
Despite advances for patients with acute leukemia health disparities limit access to diagnosis and treatment. Artificial Intelligence (AI) approaches may address some disparities. We retrospectively assemble a diverse, international cohort of 6206 leukemia patients from 20 centers to test an AI tool designed to support leukemia diagnosis using standard laboratory results. Executing the pretrained algorithm results in varying accuracy metrics. With confidence cutoff predictions, 2000-fold bootstrapped area under the curve (AUROC) metrics are 0.94 for acute myeloid leukemia (AML), 0.98 for the promyelocytic subtype and 0.84 for acute lymphoblastic leukemia. However, this cutoff excludes 70.8-92.5% of patients from predictions. We improve accuracy and robustness, while maintaining generalizability via an ensemble of Isolation Forest and Local Outlier Factor increasing AUROC for AML from 0.72 to 0.84 (hold-out test set, patients below confidence threshold), while excluding only 12.1% of patients. Furthermore, we retrain the algorithm for pediatric patients.