ArticleJournal of pathology informatics2022
BestCyte® Cell Sorter Imaging System: Primary and adjudicative whole slide image rescreening review times of 500 ThinPrep Pap test thin-layers - An intra-observer, time-surrogate analysis of diagnostic confidence potentialities.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Assessment of the efficiency and accuracy of an artificial intelligence assistive system in the diagnosis of Pap cervical atypical glandular cell cytology.Cancer cytopathology · 2025Article
- Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X.Cancer cytopathology · 2023Article
- BestCyte® primary screening of 500 ThinPrep Pap Test thin-layers: 3 Cytologists' Interobserver diagnostic concordance with predicate manual microscopy relative to Truth Reference diagnoses defining NILM, ASCUS+, LSIL+, and ASCH+ thresholds for specificity, sensitivity, and equivalency grading.Journal of pathology informatics · 2023Article
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Abstract
Background: The novel Artificial Intelligence-driven BestCyte® Cell Sorter Imaging System (BestCyte) enables hybrid digital screening through classification and sorting of tiles depicting cells in 8 galleries or whole slide image (WSI) reviews. Objectives: (1) Analyze expenditures of time (minutes) for primary BestCyte cell sorter screening and adjudicative WSI rescreening of 500 blinded, randomized ThinPrep thin-layers to determine review times per Bethesda nomenclature; (2) Analyze review times for NILM qualifier diagnoses reflecting increasing interpretive complexity (i.e., Inflammation, Reactive/Repair, Bacterial cytolysis, Bacterial vaginosis, Atrophy, and Atrophic vaginitis); (3) Challenge accuracy of primary diagnoses (Downgraded, Upheld, and Upgraded) following adjudicative WSI rescreening to assess correlated review times as surrogate indicators of diagnostic confidence in BestCyte functionality (i.e., learning curve); and (4) Correlate primary and adjudicative diagnoses to calculate intra-observer reproducibility Kappa coefficients per Bethesda nomenclature. Results: Of 500 thin-layers, the mean [primary/adjudicative rescreening review times (minutes)] were: Overall study [1.38/3.94], NILM [1.23/3.02], ASCUS [1.18/2.53], ASC-H [1.73/4.86], AGUS [1.84/6.34], LSIL [1.49/4.16], HSIL [1.52/4.10], CA [0.65/2.57]. Of 500 primary Bethesda diagnoses: 2 (0.40%) downgraded; 483 (96.6%) upheld; 15 (3.00%) upgraded after adjudicative WSI rescreening. Of 354 NILM diagnoses: 0 downgraded; 344 (97.2%) upheld; 10 (2.82%) upgraded. Of 34 ASCUS diagnoses: 2 (5.88%) downgraded; 28 (82.4%) upheld; 4 (11.8%) upgraded. Of 17 ASC-H diagnoses: 0 downgraded; 16 (94.1%) upheld; 1 (5.88%) upgraded. Of AGUS (n=1), LSIL (n=24), HSIL (n=52), CA (n=1), UNSAT (n=17): 100% upheld. Kappa coefficients with 95% (Confidence Intervals): Overall study 0.9305 (0.8983-0.9627), NILM 0.9429 (0.9110-0.9748), ASCUS 0.8378 (0.7393-0.9363), ASC-H 0.9112 (0.8113-0.9999), AGUS 1.0 (1.0-1.0), LSIL 0.9189 (0.8400-0.9978), HSIL 0.9894 (0.9685-0.9999), CA 1.0 (1.0-1.0), UNSAT 1.0 (1.0-1.0). Primary BestCyte cell image review time trends for NILM, ASCUS, LSIL, and HSIL, revealed plateaus relative to decreasing respective adjudicative WSI rescreening times. Conclusions: Given innovative robustness, BestCyte accommodates interpretive fundamentals, enabling shorter ThinPrep thin-layer review times with optimal intra-observer concordance per Bethesda nomenclature through classifying, ranking, sorting, and displaying clinically relevant cells efficiently in galleries. BestCyte fosters continuously optimizing diagnostic confidence learning curves; may supplant manual microscopy for primary screening.
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