- Title
- A comparative evaluation of three consecutive artificial intelligence algorithms released by Techcyte for identification of blasts and white blood cells in abnormal peripheral blood films
- Creator
- Lincz, Lisa F.; Makhija, Karan; Attalla, Khaled; Scorgie, Fiona E.; Enjeti, Anoop K.; Prasad, Ritam
- Relation
- International Journal of Laboratory Hematology Vol. 46, Issue 1, p. 92-98
- Publisher Link
- http://dx.doi.org/10.1111/ijlh.14180
- Publisher
- Wiley-Blackwell
- Resource Type
- journal article
- Date
- 2024
- Description
- Introduction: Digital pathology artificial intelligence (AI) platforms have the capacity to improve over time through "deep machine learning." We have previously reported on the accuracy of peripheral white blood cell (WBC) differential and blast identification by Techcyte (Techcyte, Inc., Orem, UT, USA), a digital scanner-agnostic web-based system for blood film reporting. The aim of the current study was to compare AI protocols released over time to assess improvement in cell identification. Methods: WBC differentials were performed using Techcyte's online AI software on the same 124 digitized abnormal peripheral blood films (including 64 acute and 22 chronic leukaemias) in 2019 (AI1), 2020 (AI2), and 2022 (AI3), with no reassignment by a morphologist at any time point. AI results were correlated to the "gold standard" of manual microscopy, and comparison of Lin's concordance coefficients (LCC) and sensitivity and specificity of blast identification were used to determine the superior AI version. Results: AI correlations (r) with manual microscopy for individual cell types ranged from 0.50-0.90 (AI1), 0.66-0.86 (AI2) and 0.71-0.91 (AI3). AI3 concordance with manual microscopy was significantly improved compared to AI1 for identification of neutrophils (LCC AI3 = 0.86 vs. AI1 = 0.77, p = 0.03), total granulocytes (LCC AI3 = 0.92 vs. AI1 = 0.82, p = 0.0008), immature granulocytes (LCC AI3 = 0.67 vs. AI1 = 0.38, p = 0.0014), and promyelocytes (LCC AI3 = 0.53 vs. AI1 = 0.16, p = 0.0008). Sensitivity for blast identification (n = 65 slides) improved from 97% (AI1), to 98% (AI2), to 100% (AI3), while blast specificity decreased from 24% (AI1), to 14% (AI2) to 12% (AI3). Conclusion: Techcyte AI has shown significant improvement in cell identification over time and maintains high sensitivity for blast identification in malignant films.
- Subject
- blasts; digital morphology; machine learning; Techcyte; white blood cell differential; SDG 3; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1496079
- Identifier
- uon:54124
- Identifier
- ISSN:1751-5521
- Rights
- x
- Language
- eng
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