-Author name in bold denotes the presenting author
-Asterisk * with author name denotes a Non-ASH member
Clinically Relevant Abstract denotes an abstract that is clinically relevant.

PhD Trainee denotes that this is a recommended PHD Trainee Session.

Ticketed Session denotes that this is a ticketed session.

2285 Advancing Hematological Point-of-Care Diagnostics: Development and Multi-Site Evaluation of a Novel AI-Enabled Diagnostic Device for CBC Analysis with Differential

Program: Oral and Poster Abstracts
Session: 803. Emerging Tools, Techniques and Artificial Intelligence in Hematology: Poster I
Hematology Disease Topics & Pathways:
artificial intelligence (AI), assays, emerging technologies, Technology and Procedures, imaging, machine learning, Pathology
Saturday, December 9, 2023, 5:30 PM-7:30 PM

Renee Higgins, PhD, Michael Adams, MS*, Nick Haase, PhD*, Jessica Igoe, MS*, Rachel Krupa*, Ali Lashgari, PhD*, Ian Levine, MS*, Aaron Palumbo, MS*, Robin Richardson*, Astrid Schroeder, PhD*, Florence Lee, PhD* and Dena Marrinucci, PhD*

Truvian, San Diego, CA

Introduction: A complete blood count (CBC) with differential is a routine blood test that provides essential health information that is fundamental for maintaining wellness. While many other routine tests can be run in Point of Care (POC) settings, a CBC with differential is largely confined to central laboratory testing environments. Truvian is in late-stage development of a fully automated device that runs a TruWellness Panel(TM) incorporating all routine tests – a CBC with 3-part differential, comprehensive metabolic panel (CMP), lipid panel, HbA1c and TSH in a single run with 300uL of whole blood. This device incorporates high-throughput cell imaging, Computer Vision (CV) and Artificial Intelligence (AI), including machine learning and deep learning, for on board CBC analysis with 3-part and 5-part differentials. Here, we summarize our development and evaluation of the AI-enabled CBC sub-panel through method comparison studies.

Methods: An AI-Enabled 5-part white blood cell (WBC) differential was developed using transfer learning. WBC cells were detected through classical object detection algorithms and extracted from fluorescent images of normal donor blood samples acquired on the Truvian device. Each cell in the set was classified by hematopathologists into one of five categories – Neutrophil, Lymphocyte, Monocyte, Eosinophil, and Basophil – to train a deep learning model. The algorithm currently outputs the data in a 3-part differential format (neutrophils, lymphocytes and “others”) with refinement of the full 5-part differential in progress. The final training dataset consisted of ~2000 – 5000 cells in each class. The training set was divided into training (80%) and test (20%) datasets. Data augmentation techniques were leveraged to improve model invariance to cell orientation and reduce the risk of overfitting. Performance of each iteration of the algorithm was assessed using accuracy of the model on test data. Following incorporation of the algorithm into Truvian's comprehensive and automated blood testing platform, comparability between an external reference laboratory and Truvian’s system was performed. Samples from over 150 donors were measured and analytical concordance was evaluated by Passing-Bablok regression, and bias was evaluated using the TOST equivalency test.

Results: WBC differential models were improved over a two-year period to incorporate cell image refinement associated with hardware and software iterations. The final algorithm was able to accurately detect and differentiate WBCs into 3 main categories: neutrophils, lymphocytes, and "others” (monocytes, eosinophils, and basophils). Neutrophils, lymphocytes, and “others” demonstrated R correlations of 0.98, 0.96 and 0.49 respectively. Slopes of 1.0 were observed for both neutrophils and lymphocytes while “others” had a slope of 1.37. Intercepts ranged from -0.07 to 0.04 x103 cells/μL for the various cell classes. The RBC, WBC and platelet cell counting algorithms were able to produce excellent correlation compared to predicate devices with correlation coefficients ≥ 0.95 and slopes between 0.96 to 1.03.

Conclusions: In conclusion, this novel diagnostic device represents a breakthrough in point of care diagnostics. The compact, fully automated blood diagnostic device can run a CMP, lipid panel, CBC with 3-part differential, HbA1c and TSH assays simultaneously. By incorporating Computer Vision algorithms for cell detection and AI techniques such as machine and deep learning for differentiation on board the instrument, it offers an efficient tool for accurate and precise CBC analysis with 3-part differential at the point-of-action. Further development and refinement of the differential algorithm is currently underway.

Disclosures: No relevant conflicts of interest to declare.

*signifies non-member of ASH