An automated image analysis system can be beneficial in preclassification of leucocytes in children with hematological disease

• Published in: Journal of clinical laboratory analysis 2011, Vol.25 (2), p.71-75
• Main Authors: Portakal, Oytun, Tavil, Betul, Kuşkonmaz, Barış, Aytaç, Selin, Hasçelik, Gülşen
• Format: Article
• Language: English
• Subjects: artificial neural networks; Blast; Child; Children; Diff Master™ Octavia; Hematologic Diseases - blood; Hematologic Diseases - diagnosis; Hematological diseases; Humans; Image processing; Image Processing, Computer-Assisted - instrumentation; Image Processing, Computer-Assisted - methods; Leukocytes; Leukocytes - classification; Leukocytes - pathology; Microscopy; Microscopy - methods; Original; Pediatrics; preclassification of leucocytes; Predictive Value of Tests; Reproducibility of Results; Time Factors
• ISSN: 0887-8013; 1098-2825; 1098-2825
• DOI: 10.1002/jcla.20433

This study was aimed to evaluate the analytical performance of an automated image analysis system (a pilot model of Diff Master™ Octavia) for the preclassification of leucocytes in children with hematological disease. Manual microscopy performed by pediatric hematologists was used as the reference method. Five mature cell class and blasts were evaluated. Diff Master Octavia correctly preclassified 87.4% of all leucocytes with a high reproducibility. The overall accuracy was found to be 93.0%. Clinical sensitivity was 97.7% and specificity was 76.0%. The average time per slide for Diff Master™ Octavia was 2.3 min lower than that of manual method. Our results indicated that the Diff Master™ Octavia can detect and preclassify leucocytes accurately; therefore, it can be used as an efficient and fast method in pediatric hematology routine. J. Clin. Lab. Anal. 25:71–75, 2011. © 2011 Wiley‐Liss, Inc.