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Volume 34, 12 Issues, 2024
  Letter to the Editor     August 2023  

Digital Morphology: Bridging the Final Gap in Automated Haematology Testing

By Muhammad Shariq Shaikh, Zeeshan Ansar Ahmed


  1. Department of Pathology and Laboratory Medicine, The Aga Khan University, Karachi, Pakistan
doi: 10.29271/jcpsp.2023.08.949


Complete  blood  count  (CBC)  is  the  commonest  initial  investigation ordered to evaluate the overall health of an individual. Examination of peripheral blood smear (PBS) under the microscope is an integral component of a CBC report. Together, CBC and PBS  provide  enormous  information  on several blood and blood related  disorders.

Although modern cell counters can keep pace with high test volumes,  manual  microscopy  creates  bottlenecks  in  faster  result reporting. It is completely dependent on the availability of skilled morphologists and is a lengthy, strenuous process. More importantly, it is associated with high inter-observer variability and  lacks  standardisation  in  terms  of  repeatability  and  accuracy.1 Challenging slides require the physical presence of a morphology expert for a second opinion, resulting in delayed patient care. Hours-long manual microscopy is also a significant cause  of  staff’s  eye  discomfort  and  body  fatigue.

Digital  morphology  provides  an  impeccable  solution  to  traditional manual procedures. By producing an automated workflow, it allows laboratories to function more competently.2 The digitalised system automatically detects and captures cell images from labelled slides. Thus, uniformity is maintained and bias due to inter-observer variability is eliminated. An exclusive software sequesters cellular characteristics from the images. Red and white blood cells are then classified into sub-types before reviewing by a morphologist. Simultaneous display of all cell types on a single screen allows the staff to easily review the case and sign it out, saving precious time.

Digital morphology, therefore, replaces manual microscopy with a more standardised process. The desired quality is maintained, and patient results are reported with consistent precision and authenticity. Barcoding of slides further eliminates the risk of patient misidentification. Using digital morphology, sample review time can be reduced significantly allowing laboratories to take on a greater volume of samples. It also provides an opportunity to improve connectivity by sharing cell images and consultation from any workstation. Skilled morphologists can provide opinions on difficult smears remotely and final results can be dispatched from a different location. By archiving reference cell images, the accumulated database can be used not only for teaching and learning but for monitoring and promoting staff competency.3

A multicentric evaluation has a well-documented efficiency of digital imaging. Total imprecision in the study ranged from 5.21% to 20.60% and the mean evaluation time of 326 ± 110 s with manual microscopy was reduced to 191 ± 68 s with digital morphology.4 The European Leukemia Net WP10 also experienced substantial improvements in diagnostic accuracy and harmonisation; inter-observer concordance increased from 62.5% to 83.0%.5

A  couple  of  drawbacks  include  very  high  capital  costs and initial  staff  training  to  operate  software  and  hardware. Nevertheless, digital morphology qualifies as the filler to one of the remaining gaps in haematology laboratory automation contri-buting to the delivery of efficient, and reliable service to physicians and patients.

The authors declare no competing interest.

MSS:  Conceptualised  the  manuscript  and  wrote  the  initial  draft.
MSS, ZAA:  Critically  reviewed  and  approved  the  final  version  of  the  manuscript.


  1. Brereton M, De La Salle B, Ardern J, Hyde K, Burthem J. Do we know why we make errors in morphological diagnosis? An analysis of approach and decision-making in haematological morphology. EBio Medicine 2015; 2(9): 1224-34. doi: 10.1016/ j.ebiom.2015.07.020.
  2. El Achi H, Khoury JD. Artificial intelligence and digital micro-scopy applications in diagnostic hematopathology. Cancers 2020; 12(4):797. doi: 10.3390/cancers12040797.
  3. Kratz A, Lee Sh, Zini G, Riedl JA, Hur M, Machin S, et al. Digital morphology analyzers in hematology: ICSH review and recommendations. Int J Lab Hematol 2019; 41(4): 437-7. doi: 10.1111/ijlh.13042.
  4. Rin GD, Seghezzi M, Padoan A, Pajola R, Bengiamo A, Fabio AMD. Multicentric evaluation of the variability of digital morphology performances also respect to the reference methods by optical microscopy. Int J Lab Hematol 2022; 44(6):1040-9. doi: 10.1111/ijlh.13943.
  5. Zini G, Barbagallo O, Scavone F, Béné MC. Digital morphology in hematology diagnosis and education: The experience of the European LeukemiaNet WP10. Int J Lab Hematol 2022; 44 (Suppl 1):37-44. doi: 10.1111/ijlh. 13908.