Acute Leukemia, Artificial Intelligence, Hematologic Diseases
Conditions
Brief summary
Counting and classification of blood cells in a bone marrow smear and peripheral blood smear are essential to clinical hematology. To this date, this procedure has been carried out in a manual manner in the great majority of clinical settings. There is often inconsistency in the counting result between different operators largely due to its manual nature. There has not been an effective and standard method for blood smear preparation and automatic counting and classification. The recent advent of deep neural network for medical image processing introduced new opportunities for an effective solution of this long-standing problem. Numerous results have been published on the effectiveness of convolutional neural network in clinical image recognition task.
Interventions
OTHERthere are not any interventions in this study
there are not any interventions in this study
Sponsors
National Taiwan University Tai-Chen Cell Therapy Center, Biomdcare Corporation
National Taiwan University Hospital
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patients who have suspected or confirmed hematological diseases and receive bone * marrow or peripheral blood cell morphological examination in National Taiwan University Cancer Center * Patients who are aged more than 20 y/o
Exclusion criteria
•Patients who are not willing to sign informed consents
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Evaluate the accuracy of cell counting and classifying between automatic method and manual method through digital microscopic photos of bone marrow smear and peripheral blood smear using deep convolutional neural networks | 3 years |
Countries
Taiwan
Contacts
Primary ContactBor-Sheng Ko
Outcome results
None listed