Hematologic DiseasesNCT04551235
Establishing Automatic Method of Counting and Classify Bone Marrow and Peripheral Blood Cells
An observational study of Hematologic Diseases and Artificial Intelligence, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Hematologic Diseases, Artificial Intelligence, Acute Leukemia
- What is being tested?
- there are not any interventions in this study
- How many people
- 900 participants
- Where
- 1 siteTaiwan
- Study dates
- 28 Aug 2020to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who have suspected or confirmed hematological diseases and receive bone marrow or peripheral blood cell morphological examination in National Taiwan University Cancer Center
Who may be able to join
- 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
Who cannot join
- •Patients who are not willing to sign informed consents
Trial locations and contacts
1 site in 1 country.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University Tai-Chen Cell Therapy Center
Taipei
Bor-Sheng Ko, 886-9-726512, kevinkomd@gmail.com
About this study
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.
- Being tested
- there are not any interventions in this study
- Medical subject headings
- Hematologic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
there are not any interventions in this studyOther
there are not any interventions in this study
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Sep 2020
- Study start28 Aug 2020
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hematologic DiseasesStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Taiwan University Tai-Chen Cell Therapy Center, Biomdcare Corporation
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04551235
- Sponsor's protocol ID
- 202007086RIPB
- Status last verified
- Mar 2022
- First submitted
- 27 Aug 2020
- Last updated
- 10 Mar 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 10 Mar 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishing Automatic Method of Counting and Classify Bone Marrow and Peripheral Blood Cells
- ClinicalTrials.gov ID
- NCT04551235
- Acronym / sponsor ID
- 202007086RIPB
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- National Taiwan University Tai-Chen Cell Therapy Center, Biomdcare Corporation
- Study start
- 28 Aug 2020 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 16 Sep 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who have suspected or confirmed hematological diseases and receive bone marrow or peripheral blood cell morphological examination in National Taiwan University Cancer Center
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- •Patients who are not willing to sign informed consents
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| there are not any interventions in this studythere are not any interventions in this study | Other | – |
Primary outcome
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
Time frame: 3 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Mar 2022.
- Individual participant dataSharing planned: Undecided
Related trials for Hematologic Diseases
RecruitingPhase 2
Tafasitamab With Acalabrutinib and Venetoclax for the Treatment of Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma
Chronic Lymphocytic Leukemia, Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma
Not yet recruitingPhase 2
Study of YKST02 in Adults With Relapsed or Refractory Multiple Myeloma
Relapsed or Refractory Multiple Myeloma (RRMM)
RecruitingPhase 2
Engaging T-cells to Eliminate MRD in Newly Diagnosed Myeloma Optimizing Response With Talquetmab and Teclistamab (ROTATE)
Multiple Myeloma
Active, not recruitingPhase 3
A Phase III Study of Eque-cel in Subjects With Len-refractory RRMM (FUMANBA-03)
Multiple Myeloma
RecruitingObservational study
A Study in Children and, Adults With Congenital Thrombotic Thrombocytopenic Purpura (cTTP) Treated With Adzynma
Thrombotic Thrombocytopenic Purpura (TTP)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hematologic Diseases and Artificial Intelligence, run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 10 Mar 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Bor-Sheng Ko (886-0-72651297, kevinkomd@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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 (time frame: 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Mar 2022.