David P. Ng
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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

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

Selected Publications

Journal Article

  1. Marotti, J.D., Johncox, V., Ng, D., Gonzalez, J.L., 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.
  2. Ng, D.P., Wu, D., Wood, B.L., Fromm, J.R. (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.
  3. Ng DP; Miles RR; Anderson EF; Toydemir R (2021). Flow Cytometry Is More Sensitive Than Fluorescence In Situ Hybridization for Detecting Minimal Residual Disease. American journal of clinical pathology,
  4. Ng D, Polito FA, Cervinski M (2016). Optimization of a Moving Averages Program Using a Simulated Annealing Algorithm: The Goal is to Monitor the Process Not the Patients. Clinical chemistry, 62(10), 1361-71.
  5. Yeung CCS, McElhone S, Chen XY, Ng D, Storer BE, Deeg HJ, Fang (2018). Impact of copy neutral loss of heterozygosity and total genome aberrations on survival in myelodysplastic syndrome. Modern pathology, 31(4), 569-580.
  6. Reeves JG, Suriawinata AA, Ng DP, Holubar SD, Mills JB, Barth RJ J (2013). Short-term preoperative diet modification reduces steatosis and blood loss in patients undergoing liver resection. Surgery, 154(5), 1031-7.
  7. Bean J, Ng D, Demirtas H, Guinan (2008). Medical Students¿ attitudes towards torture. Torture, 18(2), 99-103.
  8. Ng D (2021). Flow Cytometric Myeloma Measurable Residual Disease testing in the Era of Targeted Therapies. International journal of laboratory hematology, 43(S1), 71-77.
  9. 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 (2023). Systematic single amino acid affinity tuning of CD229 CAR T cells retains efficacy against multiple myeloma and eliminates on-target off-tumor toxicity. Science translational medicine, 15(705), eadd7900.
  10. Ng DP, Karner K (2022). BCR-ABL1 (p210) Transcript Kinetics. Archives of pathology & laboratory medicine, 146(9), 1140-1143.
  11. Ng DP, Simonson PD, Tarnok A, Lucas F, Kern W, Rolf N, Bogdanoski G, Green C, Brinkman RR, Czechowska (2024). Recommendations for using artificial intelligence in clinical flow cytometry. Cytometry. Part B, Clinical cytometry, 106(4), 228-238.
  12. Dinalankara W, Ng DP, Marchionni L, Simonson P (2024). Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data. Cytometry. Part B, Clinical cytometry, 106(4), 282-293.
  13. Kuceki G,Nguyen C,Ng D,Wada D,Mathis (2023). Oral diffuse large B-cell lymphoma presenting as a bland nodule. JAAD case reports, 36, 34-37.
  14. Gociman S,Wada DA,Bowen AR,Florell SR,Ng D,Madigan L (2023). Young Woman With Annular and Purpuric Plaques in the Setting of High Fevers: Challenge. The American Journal of dermatopathology, 45(5), E32-E34.
  15. Gociman S,Wada DA,Bowen AR,Florell SR,Ng D,Madigan L (2023). Young Woman With Annular and Purpuric Plaques in the Setting of High Fevers: Answer. The American Journal of dermatopathology, 45(5), 344-345.
  16. Ng DP,Zuromski L (2021). Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies. American journal of clinical pathology, 155(4), 597-605.
  17. Davis PM, Ravkov E, de Geus M, Clauss Z, Lee J, Nguyen AT, Hartmann M, Kim J, George TI, Lin L, Ng D (2024). Synthetic abnormal mast cell particles successfully mimic neoplastic mast cells by flow cytometry. Cytometry. Part B, Clinical cytometry,
  18. Ravkov EV, Ventura MF, Gudipaty S, Ng D, Delgado JC, Lin (2025). Converting an HLA-B27 flow assay from the BD FACSCanto to the BD FACSLyric. Cytometry. Part B, Clinical cytometry, 108(1), 67-76.
  19. Spies NC, Rangel A, English P, Morrison M, O'Fallon B, Ng D (2025). Machine Learning Methods in Clinical Flow Cytometry. Cancers, 17(3),
  20. Zuromski LM, Durtschi J, Aziz A, Chumley J, Dewey M, English P, Morrison M, Simmon K, Whipple B, O'Fallon B, Ng D (2025). Clinical validation of a real-time machine learning-based system for the detection of acute myeloid leukemia by flow cytometry. Cytometry. Part B, Clinical cytometry,
  21. English P, Morrison MJ, Mathison B, Enrico E, Shean R, O'Fallon B, Rupp D, Knight K, Rangel A, Gilivary J, Vance A, Hatch H, Lin L, Ng DP, Shakir S (2025). Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens. Microbiology spectrum, 13(8), e0060225.
  22. Spies NC, Ng D (2025). Performance metrics for machine learning solutions in laboratory medicine. Laboratory medicine,
  23. 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 O (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. American journal of hematology,
  24. Shean RC, George TI, Ng D (2026). Characterization of CD123 expression by mast cells in systemic mastocytosis with multicolor flow cytometry. Cytometry. Part B, Clinical cytometry,
  25. Almiski M (2021). Pax-5 negative B-cell Lymphoma. Human pathology (New York), 25,

Review

  1. Ng DP, Werner D, Oak J, Devitt K, Oldaker T (2021). Challenges in Transitioning from 5 color to 10 color Flow Cytometry. 22,
  2. Lu KL, Menke JR, Ng D, Ruiz-Cordero R, Marinoff A, Stieglitz E, Gollapudi S, Singh K, Ohgami RS, Vohra (2022). Cytomorphologic features of pediatric-type follicular lymphoma on fine needle aspiration biopsy: case series and a review of the literature. Journal of the American Society of Cytopathology, 11(5), 281-294.
  3. Dadelahi A, Jackson T, Agarwal AM, Lin L, Rets AV, Ng D (2024). Applications of Flow Cytometry in Diagnosis and Evaluation of Red Blood Cell Disorders. Clinics in laboratory medicine, 44(3), 495-509.
  4. Alnoor F, Spies NC, Kumar J, Samghabadi P, Silva O, Luo MX, Chisholm KM, Zhang J, Rangel A, Ng D, Li P, Ohgami R (2025). The Evolution and Recent Advances in Diagnostic Criteria for Idiopathic Multicentric Castleman Disease. American journal of hematology, 100(11), 2064-2073.
  5. Spies N.C (2025). Machine Learning Methods in Clinical Flow Cytometry. Cancers, 17(3),

Book Chapter

  1. Ng (2016). BOB1. 31-2.
  2. Ng (2016). OCT2. 343-4.
  3. Ng (2016). PAX-5. 373-4.

Conference Proceedings

  1. Zhang X, Mahesh V, Ng D, Hubbard R, Ailiani A, O'hare B, Benesi A, Webb (2005). Design, construction and NMR testing of a 1 tesla Halbach Permanent Magnet for Magnetic Resonance.

Case Report

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

Editorial

  1. Ng DP,Herman D (2020). How to Implement Patient-Based Quality Control: Trial and Error. The journal of applied laboratory medicine, 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 O (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. American journal of hematology,

Abstract

  1. Willams M, Li P, Ng DP (2020). Ogata Scores Show Similar Performance Across Platforms in Predicting Myelodysplastic Syndromes.
  2. Mohlman J., Kohan J, Ng D (2020). Grading Follicular Lymphomas Using Augmented Human Intelligence. .
  3. 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.

Research Lab