An artificial intelligence‐assisted diagnostic platform for rapid near‐patient hematology
Open accessPublished version of a preprint
Abstract
Hematology analyzers capable of performing complete blood count (CBC) have lagged in their prevalence at the point‐of‐care. Sight OLO (Sight Diagnostics, Israel) is a novel hematological platform which provides a 19‐parameter, five‐part differential CBC, and is designed to address the limitations in current point‐of‐care hematology analyzers using recent advances in artificial intelligence (AI) and computer vision. Accuracy, repeatability, and flagging capabilities of OLO were compared with the Sysmex XN‐Series System (Sysmex, Japan). Matrix studies compared performance using venous, capillary and direct‐from‐fingerprick blood samples. Regression analysis shows strong concordance between OLO and the Sysmex XN, demonstrating that OLO performs with high accuracy for all CBC parameters. High repeatability and reproducibility were demonstrated for most of the testing parameters. The analytical performance of the OLO hematology analyzer was validated in a multicenter clinical laboratory setting, demonstrating its accuracy and comparability to clinical laboratory‐based hematology analyzers. Furthermore, the study demonstrated the validity of CBC analysis of samples collected directly from fingerpricks.
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Recent advances in point-of-care testing include the availability of rapid haematology analysers, which offer results within 10 min.
“Moreover, recent advances in point-of-care testing include the availability of rapid haematology analysers, which offer results within 10 min,28”
From Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening (2022) · cites this paper as [28]
The Introduction (page not provided) discusses near-patient complete-blood-count analyzers, and Methods > The device describes Sight OLO as designed for point-of-care use and displaying results. Neither passage establishes that results are available within 10 minutes; searches for “10 min,” “minutes,” and turnaround time found no such result-time claim. (LLM, conf=0.84)
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- Authors
- Bachar, N., Benbassat, D., Brailovsky, D., Eshel, Y., Glück, D., Levner, D., Levy, S., Pecker, S., Yurkovsky, E., Zait, A., Sever, C., Kratz, A., & Brugnara, C.
- Affiliations
- S.D. Sight Diagnostics LTD Tel Aviv‐Jaffa Israel
- TriCore Reference Laboratories Albuquerque New Mexico USA
- NewYork–Presbyterian Hospital (US)
- Boston Children's Hospital (US)
- Corresponding author
- Brugnara, Carlo
- Venue
- American Journal of Hematology
- Publisher
- Wiley
- Published
- 2021-08-02
- Volume
- 96
- Issue
- 10
- Pages
- 1264-1274
- ISSN
- 0361-8609 (ISSN-L)
- 1096-8652
- PMC
- PMC9290600
- OpenAlex
- W3158050822
- Cited by
- 51
- References
- 18
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 8 · 2023: 9 · 2022: 7 · 2021: 2
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- American Journal of Hematology (publishedVersion, cc-by)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Health Sciences › Medicine › Emergency Medicine › Hematological disorders and diagnostics
- Keywords
- complete blood count, artificial intelligence, computer vision, capillary blood, venous blood, analytical accuracy, repeatability, automated blood cell counting
- MeSH terms
- Artificial Intelligence, Blood Cell Count, Equipment Design, Humans, Reproducibility of Results, Point-of-Care Systems
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