Literature DB >> 25378575

Detection of intracellular parasites by use of the CellaVision DM96 analyzer during routine screening of peripheral blood smears.

Lori D Racsa1, Rita M Gander2, Paul M Southern2, Erin McElvania TeKippe1, Christopher Doern1, Hung S Luu3.   

Abstract

Conventional microscopy is the gold standard for malaria diagnosis. The CellaVision DM96 is a digital hematology analyzer that utilizes neural networks to locate, digitize, and preclassify leukocytes and characterize red blood cell morphology. This study compared the detection rates of Plasmodium and Babesia species on peripheral blood smears utilizing the CellaVision DM96 with the rates for a routine red blood cell morphology scan. A total of 281 slides were analyzed, consisting of 130 slides positive for Plasmodium or Babesia species and 151 negative controls. Slides were blinded, randomized, and analyzed by CellaVision and microscopy for red cell morphology scans. The technologists were blinded to prior identification results. The parasite detection rate was 73% (95/130) for CellaVision and 81% (105/130) for microscopy for positive samples. The interobserver agreement between CellaVision and microscopy was fair, as Cohen's kappa coefficient equaled 0.36. Pathologist review of CellaVision images identified an additional 15 slides with parasites, bringing the total number of detectable positive slides to 110 of 130 (85%). Plasmodium ovale had the lowest rate of detection at 56% (5 of 9); Plasmodium malariae and Babesia spp. had the highest rate of detection at 100% (3/3 and 6/6, respectively). The detection rate by CellaVision was 100% (23/23) when the parasitemia was ≥2.5%. The detection rate for <0.1% parasitemia was 63% (15/24). Technologists appropriately classified all negative specimens. The percentage of positive specimens detectable by CellaVision (73%) approaches results for microscopy on routine scan of peripheral blood smears for red blood cell morphology.
Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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Year:  2014        PMID: 25378575      PMCID: PMC4290916          DOI: 10.1128/JCM.01783-14

Source DB:  PubMed          Journal:  J Clin Microbiol        ISSN: 0095-1137            Impact factor:   5.948


  17 in total

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Journal:  Int J Lab Hematol       Date:  2011-11-11       Impact factor: 2.877

2.  Malaria surveillance--United States, 2010.

Authors:  Sonja Mali; S Patrick Kachur; Paul M Arguin
Journal:  MMWR Surveill Summ       Date:  2012-03-02

3.  Automated detection of malaria pigment in white blood cells for the diagnosis of malaria in Portugal.

Authors:  T Hänscheid; J Melo-Cristino; B G Pinto
Journal:  Am J Trop Med Hyg       Date:  2001 May-Jun       Impact factor: 2.345

Review 4.  Malaria diagnosis: memorandum from a WHO meeting.

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Journal:  Bull World Health Organ       Date:  1988       Impact factor: 9.408

5.  Accuracy of routine laboratory diagnosis of malaria in the United Kingdom.

Authors:  L M Milne; M S Kyi; P L Chiodini; D C Warhurst
Journal:  J Clin Pathol       Date:  1994-08       Impact factor: 3.411

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Journal:  Am J Trop Med Hyg       Date:  2010-03       Impact factor: 2.345

Review 7.  Rapid diagnostic tests for malaria parasites.

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Journal:  Clin Microbiol Rev       Date:  2002-01       Impact factor: 26.132

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Authors:  Peter C Wever; Yvonne M C Henskens; Piet A Kager; Jacob Dankert; Tom van Gool
Journal:  J Clin Microbiol       Date:  2002-12       Impact factor: 5.948

9.  Treatment of asymptomatic carriers with artemether-lumefantrine: an opportunity to reduce the burden of malaria?

Authors:  Bernhards Ogutu; Alfred B Tiono; Michael Makanga; Zulfiqarali Premji; Adama Dodji Gbadoé; David Ubben; Anne Claire Marrast; Oumar Gaye
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10.  Identification of the four human malaria parasite species in field samples by the polymerase chain reaction and detection of a high prevalence of mixed infections.

Authors:  G Snounou; S Viriyakosol; W Jarra; S Thaithong; K N Brown
Journal:  Mol Biochem Parasitol       Date:  1993-04       Impact factor: 1.759

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  5 in total

1.  Deep Neural Networks Offer Morphologic Classification and Diagnosis of Bacterial Vaginosis.

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Journal:  J Clin Microbiol       Date:  2021-01-21       Impact factor: 5.948

2.  Development of a quantitative, portable, and automated fluorescent blue-ray device-based malaria diagnostic equipment with an on-disc SiO2 nanofiber filter.

Authors:  Takeki Yamamoto; Muneaki Hashimoto; Kenji Nagatomi; Takahiro Nogami; Yasuyuki Sofue; Takuya Hayashi; Yusuke Ido; Shouki Yatsushiro; Kaori Abe; Kazuaki Kajimoto; Noriko Tamari; Beatrice Awuor; George Sonye; James Kongere; Stephen Munga; Jun Ohashi; Hiroaki Oka; Noboru Minakawa; Masatoshi Kataoka; Toshihiro Mita
Journal:  Sci Rep       Date:  2020-04-20       Impact factor: 4.379

3.  Performance evaluation of machine learning-based infectious screening flags on the HORIBA Medical Yumizen H550 Haematology Analyzer for vivax malaria and dengue fever.

Authors:  Parag Dharap; Sebastien Raimbault
Journal:  Malar J       Date:  2020-11-23       Impact factor: 2.979

4.  Commentary: Improving the Efficiency of the Ova and Parasite Examination Using Cloud-Based Image Analysis.

Authors:  Daniel D Rhoads
Journal:  J Pathol Inform       Date:  2017-12-14

Review 5.  Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology.

Authors:  Hanadi El Achi; Joseph D Khoury
Journal:  Cancers (Basel)       Date:  2020-03-26       Impact factor: 6.639

  5 in total

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