David P. Ng
No Rating Available
(Learn About Our Rating System)

David P. Ng, MD

Languages spoken: English

Dr. Ng is an associate professor of pathology at the University of Utah school of Medicine. He received his undergraduate degree at the University of Illinois at Urbana-Champaign in Electrical Engineering specializing in signal and image processing before attending medical school at the University of Illinois at Chicago. He completed an Anatomic and Clinical Pathology residency at Dartmouth-Hitchcock Medical Center followed by a fellowship in Hematopathology at the University of Washington. He is board certified in anatomic and clinical pathology (AP/CP) and hematopathology by the American Board of Pathology and was the 2014 ICCS Janis Giorgi Young Investigator Award winner. From 2015 to 2019, he was a hematopathologist and associate director of the flow cytometry lab at PhenoPath Laboratories in Seattle, WA. His research interests include high dimensional flow data analysis, machine learning, natural language processing, minimal residual disease testing, and applications of spectral flow cytometry in the clinical flow cytometry lab. He is currently medical director of hematologic flow cytometry at ARUP laboratories as well as Senior Medical Director of Applied Artificial Intelligence at ARUP's Research and Innovation Institute where he leads a diverse team of data scientists, software developers, and machine learning operations engineers.

Specialties

  • Flow Cytometry
  • Hematopathology
  • Pathology, Clinical

Board Certification

American Board of Pathology (Anatomic & Clinical)
American Board of Pathology (Sub: Hematology)

Dr. Ng is an associate professor of pathology at the University of Utah school of Medicine. He received his undergraduate degree at the University of Illinois at Urbana-Champaign in Electrical Engineering specializing in signal and image processing before attending medical school at the University of Illinois at Chicago. He completed an Anatomic and Clinical Pathology residency at Dartmouth-Hitchcock Medical Center followed by a fellowship in Hematopathology at the University of Washington. He is board certified in anatomic and clinical pathology (AP/CP) and hematopathology by the American Board of Pathology and was the 2014 ICCS Janis Giorgi Young Investigator Award winner. From 2015 to 2019, he was a hematopathologist and associate director of the flow cytometry lab at PhenoPath Laboratories in Seattle, WA. His research interests include high dimensional flow data analysis, machine learning, natural language processing, minimal residual disease testing, and applications of spectral flow cytometry in the clinical flow cytometry lab. He is currently medical director of hematologic flow cytometry at ARUP laboratories as well as Senior Medical Director of Applied Artificial Intelligence at ARUP's Research and Innovation Institute where he leads a diverse team of data scientists, software developers, and machine learning operations engineers.

Board Certification and Academic Information

Academic Departments Pathology -Associate Professor (Clinical)
Board Certification
American Board of Pathology (Anatomic & Clinical)
American Board of Pathology (Sub: Hematology)

Education history

Fellowship Hematopathology - University of Washington Medicine Senior Fellow
Residency Anatomic and Clinical Pathology - Dartmouth-Hitchcock Medical Center Resident
Professional Medical Medicine - University of Illinois at Chicago College of Medicine M.D.
Undergraduate Major: Electrical Engineering; Minors: Bioengineering, Chemistry - University of Illinois at Urbana-Champaign, College of Engineering B.S.

Selected Publications

Journal Article

  1. OFallon B, Morrison M, Grespan MM, Spies NC, Ng DP (2026). DinoFlow: Self-supervised pretraining in flow cytometry enables accurate detection of common hematopathological disorders. Cytometry B Clin Cytom.
  2. Shean RC, Davis P, Jacobsen J, Ng DP (2026). 27-color flow cytometry for measurable residual disease detection in B-cell lymphoblastic leukemia. Cytometry B Clin Cytom.
  3. Shean RC, George TI, Ng DP (2026). Characterization of CD123 expression by mast cells in systemic mastocytosis with multicolor flow cytometry. Cytometry B Clin Cytom.
  4. Kirtek TJ, Chen W, Harris JC, Bagg A, Foucar K, Tam W, Orazi A, Hsi ED, Hasserjian RP, Wang SA, Ng DP, George TI, Shi M, Reichard KK, Symes E, Zhang X, Arber DA, Weinberg OK (2025). Acute Leukemias of Ambiguous Lineage With RUNX1 Mutations Show Similar Prognosis Compared to Acute Myeloid Leukemia With RUNX1 Mutations: A Study From the Bone Marrow Pathology Group. Am J Hematol.
  5. English P, Morrison MJ, Mathison B, Enrico E, Shean R, OFallon B, Rupp D, Knight K, Rangel A, Gilivary J, Vance A, Hatch H, Lin L, Ng DP, Shakir SM (2025). Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens. Microbiol Spectr, 13(8), e0060225.
  6. Spies NC, Ng DP (2025). Performance metrics for machine learning solutions in laboratory medicine. Lab Med, 56(6), 597-607.
  7. Zuromski LM, Durtschi J, Aziz A, Chumley J, Dewey M, English P, Morrison M, Simmon K, Whipple B, OFallon B, Ng DP (2025). Clinical validation of a real-time machine learning-based system for the detection of acute myeloid leukemia by flow cytometry. Cytometry B Clin Cytom.
  8. Spies NC, Rangel A, English P, Morrison M, OFallon B, Ng DP (2025). Machine Learning Methods in Clinical Flow Cytometry. Cancers (Basel), 17(3).
  9. Ravkov EV, Ventura MF, Gudipaty S, Ng D, Delgado JC, Lin L (2024). Converting an HLA-B27 flow assay from the BD FACSCanto to the BD FACSLyric. Cytometry B Clin Cytom, 108(1), 67-76.
  10. Davis PM, Ravkov E, de Geus M, Clauss Z, Lee J, Nguyen AT, Hartmann M, Kim J, George TI, Lin L, Ng DP (2024). Synthetic abnormal mast cell particles successfully mimic neoplastic mast cells by flow cytometry. Cytometry B Clin Cytom.
  11. Dinalankara W, Ng DP, Marchionni L, Simonson PD (2024). Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data. Cytometry B Clin Cytom, 106(4), 282-293.
  12. Ng DP, Simonson PD, Tarnok A, Lucas F, Kern W, Rolf N, Bogdanoski G, Green C, Brinkman RR, Czechowska K (2024). Recommendations for using artificial intelligence in clinical flow cytometry. Cytometry B Clin Cytom, 106(4), 228-238.
  13. Vander Mause ER, Baker JM, Dietze KA, Radhakrishnan SV, Iraguha T, Omili D, Davis P, Chidester SL, Modzelewska K, Panse J, Marvin JE, Olson ML, Steinbach M, Ng DP, Lim CS, Atanackovic D, Luetkens T (2023). Systematic single amino acid affinity tuning of CD229 CAR T cells retains efficacy against multiple myeloma and eliminates on-target off-tumor toxicity. Sci Transl Med, 15(705), eadd7900.
  14. Kuceki G, Nguyen C, Ng D, Wada D, Mathis J (2023). Oral diffuse large B-cell lymphoma presenting as a bland nodule. JAAD Case Rep, 36, 34-37.
  15. Gociman S, Wada DA, Bowen AR, Florell SR, Ng D, Madigan LM (2023). Young Woman With Annular and Purpuric Plaques in the Setting of High Fevers: Answer. Am J Dermatopathol, 45(5), 344-345.
  16. Gociman S, Wada DA, Bowen AR, Florell SR, Ng D, Madigan LM (2023). Young Woman With Annular and Purpuric Plaques in the Setting of High Fevers: Challenge. Am J Dermatopathol, 45(5), E32-E34.
  17. Ng DP, Karner KH (2021). BCR-ABL1 (p210) Transcript Kinetics. Arch Pathol Lab Med, 146(9), 1140-1143.
  18. Ng DP Miles RR Anderson EF Toydemir RM (2021). Flow Cytometry Is More Sensitive Than Fluorescence In Situ Hybridization for Detecting Minimal Residual Disease. Am J Clin Pathol.
  19. Almiski M (2021). Pax-5 negative B-cell Lymphoma. Hum Pathol (N Y), 25.
  20. Ng DP (2021). Flow Cytometric Myeloma Measurable Residual Disease testing in the Era of Targeted Therapies. Int J Lab Hematol, 43(S1), 71-77.
  21. Ng DP, Zuromski LM (2021). Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies. Am J Clin Pathol, 155(4), 597-605.
  22. Yeung CCS, McElhone S, Chen XY, Ng D, Storer BE, Deeg HJ, Fang M (2018). Impact of copy neutral loss of heterozygosity and total genome aberrations on survival in myelodysplastic syndrome. Mod Pathol, 31(4), 569-580.
  23. Ng D, Polito FA, Cervinski MA (2016). Optimization of a Moving Averages Program Using a Simulated Annealing Algorithm: The Goal is to Monitor the Process Not the Patients. Clin Chem, 62(10), 1361-71.
  24. Ng DP, Wu D, Wood BL, Fromm JR (2015). Computer-aided detection of rare tumor populations in flow cytometry: an example with classic Hodgkin lymphoma. Am J Clin Pathol, 144(3), 517-24.
  25. Reeves JG, Suriawinata AA, Ng DP, Holubar SD, Mills JB, Barth RJ Jr (2013). Short-term preoperative diet modification reduces steatosis and blood loss in patients undergoing liver resection. Surgery, 154(5), 1031-7.
  26. Marotti JD, Johncox V, Ng D, Gonzalez JL, Padmanabhan V (2012). Implementation of telecytology for immediate assessment of endoscopic ultrasound-guided fine-needle aspirations compared to conventional on-site evaluation: analysis of 240 consecutive cases. Acta Cytol, 56(5), 548-53.
  27. Bean J, Ng D, Demirtas H, Guinan P (2008). Medical Students’ attitudes towards torture. Torture, 18(2), 99-103.

Review

  1. Alnoor F, Mukherjee S, Menon MP, Ng D, Li P, Ohgami RS (2026). Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations. [Review]. Diagnostics (Basel), 16(6).
  2. Alnoor F (2026). Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations. [Review]. Diagnostics (Basel), 16(6).
  3. Alnoor F, Spies NC, Kumar J, Samghabadi P, Silva O, Luo MX, Chisholm KM, Zhang J, Rangel A, Ng D, Li P, Ohgami RS (2025). The Evolution and Recent Advances in Diagnostic Criteria for Idiopathic Multicentric Castleman Disease. [Review]. Am J Hematol, 100(11), 2064-2073.
  4. Dadelahi A, Jackson T, Agarwal AM, Lin L, Rets AV, Ng DP (2024). Applications of Flow Cytometry in Diagnosis and Evaluation of Red Blood Cell Disorders. [Review]. Clin Lab Med, 44(3), 495-509.
  5. Lu KL, Menke JR, Ng D, Ruiz-Cordero R, Marinoff A, Stieglitz E, Gollapudi S, Singh K, Ohgami RS, Vohra P (2022). Cytomorphologic features of pediatric-type follicular lymphoma on fine needle aspiration biopsy: case series and a review of the literature. [Review]. J Am Soc Cytopathol, 11(5), 281-294.
  6. Ng DP, Werner D, Oak J, Devitt K, Oldaker T (2021). Challenges in Transitioning from 5 color to 10 color Flow Cytometry. [Review]. 22.

Book Chapter

  1. Ng D (2016). BOB1. In Chetty R, Cooper K, Gown AM (Eds.), Leong’s Manual of Diagnostic Antibodies forImmunohistology (Third Edition, pp. 31-2). New York: Cambridge University Press.
  2. Ng D (2016). OCT2. In Chetty R, Cooper K, Gown AM (Eds.), Leong’s Manual of Diagnostic Antibodies forImmunohistology (Third Edition, pp. 343-4). New York: Cambridge University Press.
  3. Ng D (2016). PAX-5. In Chetty R, Cooper K, Gown AM (Eds.), Leong’s Manual of Diagnostic Antibodies forImmunohistology (Third Edition, pp. 373-4). New York: Cambridge University Press.

Conference Proceedings

  1. Zhang X, Mahesh V, Ng D, Hubbard R, Ailiani A, Ohare B, Benesi A, Webb A (2005). Design, construction and NMR testing of a 1 tesla Halbach Permanent Magnet for Magnetic Resonance. Proceedings of the COMSOL Conference, Boston, MA.

Commentary

  1. Ng, DP (2025). Technology Spotlight Faster, Cheaper, Better: Spectral Flow Cytometry in the Clinical Laboratory. The Hematologist, 22(5).

Case Report

  1. Kuceki G, Nguyen C, Ng D, Wada D, Mathis J (2023). Oral diffuse large B-cell lymphoma presenting as a bland nodule. JAAD Case Rep, 36, 34-37.
  2. Almiski M, Ng DP, Moltzan C, Francischetti IM, Sellen LD (2021). Pax-5 negative B-cell Lymphoma. Hum Pathol (N Y).
  3. Dunbar NM, Marx-Wood CR, Maynard KJ, Ng DP, Szczepiorkowski ZM, Dumont LJ (2012). Retrograde patient blood flow and rouleaux preventing red blood cell transfusion. Transfusion, 52(11), 2284.

Editorial

  1. Ng DP, Herman DS (2020). How to Implement Patient-Based Quality Control: Trial and Error. J Appl Lab Med, 5(6), 1153-1155.

Letter

  1. Kirtek TJ, Chen W, Harris JC, Bagg A, Foucar K, Tam W, Orazi A, Hsi ED, Hasserjian RP, Wang SA, Ng DP, George TI, Shi M, Reichard KK, Symes E, Zhang X, Arber DA, Weinberg OK (2024). Acute Leukemias of Ambiguous Lineage With MDS-Associated Mutations Show Similar Prognosis Compared to Acute Myeloid Leukemia With MDS-Associated Mutations: A Study From the Bone Marrow Pathology Group. [Letter to the editor]. Am J Hematol.

Abstract

  1. Willams M, Li P, Ng DP (2020). Number of Variants and Pathogenic Variants in ASXL1, STAG2, and RUNX1 Correlate with High Ogata Score by Flow Cytometry in Myelodysplastic Syndromes: A National Reference Laboratory Experience [Abstract].
  2. Mohlman J, Kohan JNg DP (2020). Grading Follicular Lymphomas Using Augmented Human Intelligence. [Abstract].
  3. Willams M, Li P, Ng DP (2020). Ogata Scores Show Similar Performance Across Platforms in Predicting Myelodysplastic Syndromes. [Abstract].

Research Lab