Unmet Need
In the United States, acute myeloid leukemia affects approximately 20,000 individuals with a subset of these cancers, about 5-10%, presenting with acute promyelocytic leukemia (see ACS, RD). Acute promyelocytic leukemia is caused by a genetic translocation between chromosomes 15 and 17 [t(15:17)] and clinically presents as a cancer that progresses rapidly with poor survival. Due to the acute nature of its presentation, prompt and accurate diagnosis is required to initiate appropriate therapy that can be curative. However, the gold standard genetic tests can take days to confirm a diagnosis and thus therapy is often initiated on high clinical suspicion based on both clinical presentation as well as direct visualization of the peripheral smear. While there are described cellular morphological features that distinguish acute promyelocytic leukemia, there is still considerable difficulty in diagnosing from direct visualization of a peripheral smear by a hematopathologist. Therefore, there is a strong need for a reliable diagnostic test for acute promyelocytic leukemia that can rapidly inform clinicians with decisions that benefit patient survival.
Technology Overview
Researchers at Johns Hopkins have developed a novel approach to detect the presence of acute promyelocytic leukemia (APL) in peripheral blood smears by leveraging artificial intelligence. By applying both cell-level and patient-level classification, this invention demonstrated the capability of distinguishing APL in both a discovery and prospective independent cohort of patients. Furthermore, this approach extracts learned information from the trained network to identify previously undescribed morphological features of APL. The use of artificial intelligence in this method allows a quick, explainable, and accurate physician aid for diagnosing APL at time of presentation in any medical setting capable of generating a peripheral smear.
Stage of Development
Proof of concept.
Publication
N/A
