Digital Morphology for Peripheral Blood Smears in Leukemia Detection:A Systematic Review

Abstract

Background:
Peripheral blood smear examination is foundational for leukemia detection, yet manual review is laborintensive and variable. Digital morphology and artificial intelligence (AI) systems promise faster triage and standardized classification. This review synthesized diagnostic accuracy for detecting leukemia on peripheral smears.
Methods:
PubMed was searched from inception to April 2025. Eligible studies were observational diagnosticaccuracy cohorts evaluating digital morphology or AI on peripheral blood smear images against manual microscopy or integrated clinical diagnosis. The primary outcome was sensitivity/specificity; secondary outcomes included predictive values, agreement, and time to result.
Results:
Of 1,245 records, 245 duplicates were removed and 1,000 titles/abstracts were screened; 80 full texts were reviewed and 10 cohorts were included. Digital analyzers showed high specificity for common leukocytes (often >90-95%); blast sensitivity varied by platform and case-mix. A compact analyzer reported specificity >94% with blast sensitivity 21-86%. Another platform achieved blast sensitivity 98.4% and specificity 64.0%. AI-assisted APL screening yielded sensitivity 95.8% and specificity 100.0%. Image-classification studies reported sensitivity 97.86% and specificity 100.0% on held-out tests, with APL recall 97.4%. Post-verification correlations for abnormal differentials exceeded 0.93, and PPV/NPV were frequently ≥95%.
Conclusions:
Digital morphology and AI reliably triaged peripheral smears with high specificity and context-dependent blast sensitivity. They are best deployed as screening tools with mandatory expert confirmation, supported by local validation and external prospective AI verification.