Session: 613. Acute Myeloid Leukemias: Clinical and Epidemiological: Poster II
Hematology Disease Topics & Pathways:
Research, Translational Research
We then utilized this data to develop DeepHeme, a convolutional neural network classifier designed for bone marrow cell typing tasks. DeepHeme achieves state-of-the-art performance in both the breadth of differentiable classes and accuracy across these classes. By comparing its performance to that of individual hematopathologists from three premier academic medical centers, using our gold standard consensus-labelled images, we found our AI algorithm either matched or surpassed the average performance across all classes. In addition, we integrated DeepHeme with internally developed region classifier and cell detection algorithms, culminating in a comprehensive diagnostic pipeline for whole slide cell differential.
We next tested DeepHeme on slides from an external hospital system at a major cancer center to evaluate the generalizability of our model, a necessary precondition to widespread application. DeepHeme demonstrated a high level of generalizability, evidenced by a decrease of only 4% in the mean F-1 score, from 0.89 to 0.85, across all 23 cell classes. Lastly, to improve access to the DeepHeme algorithm results and encourage further real-world generalizability testing, we developed a web application that allows scientists and clinicians to test the DeepHeme algorithm on either test images from our study or their own user-uploaded aspirates.
Disclosures: Baik: Pauling.AI: Current Employment. Dogan: Seattle Genetics: Consultancy; Physicians' Education Resource: Consultancy, Honoraria; EUSA Pharma: Consultancy; Loxo: Consultancy; Peer View: Honoraria; Incyte: Consultancy; Takeda: Other: Research Funding; Roche: Other: Research Funding.
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