Healthy VolunteersNAO-WBC-YOLO-0NCT07862231
AI-Assisted Fluorescence Microscopy of Leukocytes in Fresh Whole Blood
An observational study of Healthy Volunteers and Leukocyte Fluorescence Profiling in Whole Blood, run by MIPO Clinic at 1 site in Kazakhstan.
Trial summary
The goal of this observational study is to learn whether a microscope-based method with artificial intelligence (AI) can detect and classify white blood cells in fresh, unstained blood and measure the color of fluorescent light inside each cell, in healthy adult volunteers. Read more about this study
- Who is it for?
- Ages 18 to 45 years, men onlyHealthy volunteers can take part
- Conditions the study is for
- Healthy Volunteers, Leukocyte Fluorescence Profiling in Whole Blood
- What is being tested?
- AI-Assisted bright-field and fluorescence microscopy and Romanowsky-Giemsa stained blood smear (reference)
- How many people
- 5 participants
- Where
- 1 siteKazakhstan
- Study dates
- 1 May 2026to 23 Sep 2026
- How long will I take part?
- 6 Weeks
- 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 18 to 45 years
- Men only
- Healthy volunteers accepted
- Adult
Study population: Healthy adult volunteers aged 18 to 45 years recruited at the study site (LMB clinic, Almaty, Kazakhstan). Each volunteer donates a venous blood sample about once a week for 3 to 6 weeks, and a fresh blood smear and a Romanowsky-Giemsa stained reference smear are prepared from each sample. Volunteers are not selected for any disease or condition.
Who may be able to join
- Healthy adult volunteers aged 18 to 65 years
- Self-reported good health, confirmed by a screening questionnaire
- Willing to give a venous blood sample about once a week for 3 to 6 weeks
- Able to read and sign the informed consent form
Who cannot join
- Acute infection or fever within the last 14 days, or any symptoms of illness on the day of blood draw
- Known blood disease (for example, anemia, leukemia, or a blood clotting disorder) or white blood cell count outside the laboratory reference range, if a complete blood count is available
- Chronic inflammatory, autoimmune, metabolic (including diabetes), or cancer disease
- Use in the last 30 days of corticosteroids, immunosuppressive or chemotherapy drugs, or growth factors that change white blood cell counts (for example, G-CSF)
- Vaccination in the last 14 days
- Pregnancy or breastfeeding
Show all 9Show fewer
- Blood donation or significant blood loss in the last 8 weeks
- Intense physical exercise or alcohol use in the 24 hours before blood draw
- Condition that makes venipuncture unsafe or unreasonable, as judged by the investigator
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Fluorescence Microscopy With Artificial Intelligence for Automated Single-Cell Analysis of Leukocytes in Native Whole Blood
The goal of this observational study is to learn whether a microscope-based method with artificial intelligence (AI) can detect and classify white blood cells in fresh, unstained blood and measure the color of fluorescent light inside each cell, in healthy adult volunteers. The fluorescent dye is thought to reflect mitochondria (the parts of a cell that produce energy). The main questions it aims to answer are:
- How accurately can the AI program detect white blood cells in fresh blood in the tile images?
- Does the pattern of green, yellow, and red fluorescence inside individual white blood cells differ between dyes?
Read more
Researchers will take microscope images of the same areas of each blood smear in regular light and in fluorescent light. The AI program outlines each white blood cell in the regular-light image, and a trained operator accepts, corrects, or rejects every outline. The accepted outlines are then used to measure the fluorescent colors in the same cells. As a reference, results are checked against standard stained blood smears (Romanowsky-Giemsa) read by laboratory staff.
Participants will:
Give a small blood sample by venipuncture (a needle in a vein of the arm) Repeat the blood draw about once a week for 3 to 6 weeks
This study does not test any treatment, and participants will not receive any intervention.
Read the detailed description
Background:
Automated blood analyzers and digital morphology systems count and classify white blood cells quickly, but they give little information about the functional state of the cells. Flow cytometry can measure markers such as mitochondrial mass or membrane potential, but it does not show where the signal is located inside a cell and does not preserve cell shape. Fluorescence microscopy keeps this information, but segmenting (outlining) fluorescent cells is difficult. Working with fresh whole blood, without isolating cells, also avoids artifacts caused by cell separation and storage. Neutrophils are of particular interest because they are the most numerous white blood cells and are renewed quickly, so their mitochondrial status may reflect current changes in the body.
Purpose:
This is a pilot, methodological study. It describes and tests a workflow that combines digital microscopy and an AI object-detection model (YOLO11) so that white blood cells can be located in regular (bright-field) images and the fluorescent signal can then be measured in the same cells. The study does not evaluate any treatment or diagnostic decision, and its results are intended for research use only.
Procedures:
Blood samples. Venous blood from healthy volunteers is collected into a tube with heparin and analyzed within 2 hours. Fresh, unfixed blood smears are stained with the fluorescent dye 10-N-nonyl acridine orange (NAO) together with a second dye (Janus Green B). Additional smears with other dyes (for example, pyronin Y, which stains RNA) are used to compare fluorescence patterns. Reference smears are stained by the Romanowsky-Giemsa method and used to count the white blood cell differential in the standard way.
Imaging. Each smear is scanned automatically with a motorized-stage microscope and a digital camera, with camera settings fixed manually for the entire session. The stage moves in a serpentine pattern across a grid of 45 positions (9 x 5, 12% overlap between neighboring frames) at 100x or 60x objective magnification. At every position, a stack of 5 bright-field layers is taken and the sharpest layer is chosen by an image sharpness metric (Tenengrad). The same positions are then scanned again in fluorescence mode (3 layers) using the same grid and the same focus map. Before scanning, the system is calibrated with a dark frame, an excitation background frame, and a slide of luminescent reference granules (green, yellow, and orange-red), which set the color classes and correct for uneven illumination and differences between sessions.
AI analysis with human review. The AI model finds white blood cells in the bright-field image and draws a contour around each one. A trained operator accepts, corrects, or rejects every contour before it is used; no AI result enters the analysis without operator approval. The accepted contours are copied to the fluorescence image of the same position without being recalculated. Inside each contour, fluorescent pixels are assigned to a green, yellow, or red class using thresholds defined by the reference granules; overlap of the yellow and red masks marks "orange" areas, which are provisionally considered to reflect mitochondria. Cells with overexposed pixels are flagged, and those pixels are excluded.
Model development and checking. The AI detector for native (unstained) blood is trained and validated on separate sets of annotated images. A second detector and classifier for stained smears is trained on public datasets (TXL-PBC, PBC) and then fine-tuned on images from the same microscope; it is checked on scans that were not used in training, against labels made by laboratory staff.
Data handling. Images are identified by scan numbers only. Cell-level measurements are kept as individual records and are not averaged before analysis.
- Being tested
- AI-Assisted bright-field and fluorescence microscopy, Romanowsky-Giemsa stained blood smear (reference)
- Keywords
- Leukocytes, Neutrophils, Mitochondria, Whole blood, Fluorescence microscopy, Artificial Intelligence, YOLO, Nonyl Acridine Orange, Pyronin Y, Bright-field microscopy, Digital microscopy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted bright-field and fluorescence microscopyDiagnostic test
Automated scanning of fresh, unfixed blood smears stained with 10-N-nonyl acridine orange and a second dye (Janus) on a motorized-stage microscope (100x or 60x objective, 45 positions per scan). Bright-field and fluorescence images of the same positions are taken. An AI detector (YOLO11) outlines white blood cells in the bright-field image; a trained operator accepts, corrects, or rejects each outline. Accepted outlines are copied to the fluorescence image, and green, yellow, and red signal is measured inside each cell. Smears with other dyes (e.g., pyronin Y) are scanned the same way for comparison. Research use only; no result is used for patient care.
Romanowsky-Giemsa stained blood smear (reference)Diagnostic test
Standard Romanowsky-Giemsa stained blood smear (monolayer, CLSI H20) prepared from the same blood samples. White blood cell differential is counted by laboratory staff and used as the reference for checking the AI program's detection and classification.
| Study group | Role | What they receive |
|---|---|---|
| Healthy adult volunteers | Healthy adult volunteers who donate a venous blood sample about once a week for 3 to 6 weeks. Fresh blood smears from each donation are imaged by bright-field and fluorescence microscopy and analyzed with an AI program to find white blood cells and measure the color of fluorescence inside them. Stained reference smears (Romanowsky-Giemsa) are prepared from the same samples. Receives: Diagnostic Test: AI-Assisted bright-field and fluorescence microscopy; Diagnostic Test: Romanowsky-Giemsa stained blood smear (reference) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection performance of the AI program for white blood cells in unstained blood images
Agreement between white blood cells found by the AI program (YOLO11) in bright-field tile images of fresh, unstained blood and cells outlined in the reference annotation of an independent validation set of images. Reported as precision, recall, and F1 score at the chosen confidence threshold, and as mean average precision (mAP@50 and mAP@50-95).
Measured: Up to 6 weeks (from the first blood draw to completion of image analysis)
Distribution of green, yellow, and red fluorescence classes in individual white blood cells, by dye
For each cell outlined by the AI program and accepted by the operator, fluorescent pixels are sorted into green, yellow, and red classes using thresholds set with luminescent reference granules (g = 1.10 x r; r = 1.35 x g). Each cell is assigned a color class by program rule. Reported for each dye as the number and percent of cells in each class (green, yellow, red) among the first 200 cells analyzed.
Measured: Up to 6 weeks (from the first blood draw to completion of image analysis)
Secondary outcomes (2)
Number of orange (yellow and red overlap) areas per cell, by dye
Orange areas are regions where the yellow and red signal masks overlap inside the cell outline and are provisionally considered to reflect mitochondria. Reported for each dye as the number of orange areas per cell and the percent of cells with at least one orange area.
Measured: Up to 6 weeks (from the first blood draw to completion of image analysis)
Agreement between AI classification and laboratory staff labels for white blood cells on Romanowsky-Giemsa stained smears
On stained control scans that were not used for AI training, a cell found by the AI is counted as matched to the laboratory label when the overlap of outlines (IoU) is at least 0.3. Reported as the number of labeled cells found by the AI (recall), the share of AI findings that match a labeled cell (precision), and the share of matched cells with the same class (neutrophil, eosinophil, basophil, lymphocyte, monocyte).
Measured: Up to 6 weeks (from the first blood draw to completion of image analysis)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Oct 2026
- Study start1 May 2026
- Primary completion12 Sep 2026
- Study completion23 Sep 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Healthy VolunteersStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 5 participants (actual)
- Target duration
- 6 Weeks
Sponsor and investigators
- Lead sponsor
- MIPO Clinic
- Sponsor type
- University, hospital or other
- Collaborators
- Research Institute of Cellular Diagnostics and Mitochondrial Medicine, LMB Clinic
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07862231
- Sponsor's protocol ID
- 9
- Acronym
- NAO-WBC-YOLO-0
- Status last verified
- Oct 2026
- First submitted
- 1 Oct 2026
- Last updated
- 7 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual-level data (image tiles with operator-accepted cell outlines, per-cell fluorescence measurements, and AI training and validation annotations) will be shared with qualified researchers on request. Images are identified by scan numbers only; no personal data are included. The data are not placed in a public repository.
Study at a glance
From the registry record, last updated by the sponsor on 7 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Fluorescence Microscopy With Artificial Intelligence for Automated Single-Cell Analysis of Leukocytes in Native Whole Blood
- ClinicalTrials.gov ID
- NCT07862231
- Acronym / sponsor ID
- NAO-WBC-YOLO-0 / 9
- Keywords
- Leukocytes, Neutrophils, Mitochondria, Whole blood, Fluorescence microscopy, Artificial Intelligence, YOLO, Nonyl Acridine Orange
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 5 participants (actual)
- Lead sponsor
- MIPO ClinicUniversity, hospital or other
- Collaborators
- Research Institute of Cellular Diagnostics and Mitochondrial Medicine, LMB Clinic
- Study start
- 1 May 2026 (actual)
- Primary completion
- 12 Sep 2026 (actual)
- Study completion
- 23 Sep 2026 (actual)
- First posted
- 7 Oct 2026 (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.
The goal of this observational study is to learn whether a microscope-based method with artificial intelligence (AI) can detect and classify white blood cells in fresh, unstained blood and measure the color of fluorescent light inside each cell, in healthy adult volunteers. The fluorescent dye is thought to reflect mitochondria (the parts of a cell that produce energy). The main questions it aims to answer are:
- How accurately can the AI program detect white blood cells in fresh blood in the tile images?
- Does the pattern of green, yellow, and red fluorescence inside individual white blood cells differ between dyes?
Researchers will take microscope images of the same areas of each blood smear in regular light and in fluorescent light. The AI program outlines each white blood cell in the regular-light image, and a trained operator accepts, corrects, or rejects every outline. The accepted outlines are then used to measure the fluorescent colors in the same cells. As a reference, results are checked against standard stained blood smears (Romanowsky-Giemsa) read by laboratory staff.
Participants will:
Give a small blood sample by venipuncture (a needle in a vein of the arm) Repeat the blood draw about once a week for 3 to 6 weeks
This study does not test any treatment, and participants will not receive any intervention.
Detailed description
Background:
Automated blood analyzers and digital morphology systems count and classify white blood cells quickly, but they give little information about the functional state of the cells. Flow cytometry can measure markers such as mitochondrial mass or membrane potential, but it does not show where the signal is located inside a cell and does not preserve cell shape. Fluorescence microscopy keeps this information, but segmenting (outlining) fluorescent cells is difficult. Working with fresh whole blood, without isolating cells, also avoids artifacts caused by cell separation and storage. Neutrophils are of particular interest because they are the most numerous white blood cells and are renewed quickly, so their mitochondrial status may reflect current changes in the body.
Purpose:
This is a pilot, methodological study. It describes and tests a workflow that combines digital microscopy and an AI object-detection model (YOLO11) so that white blood cells can be located in regular (bright-field) images and the fluorescent signal can then be measured in the same cells. The study does not evaluate any treatment or diagnostic decision, and its results are intended for research use only.
Procedures:
Blood samples. Venous blood from healthy volunteers is collected into a tube with heparin and analyzed within 2 hours. Fresh, unfixed blood smears are stained with the fluorescent dye 10-N-nonyl acridine orange (NAO) together with a second dye (Janus Green B). Additional smears with other dyes (for example, pyronin Y, which stains RNA) are used to compare fluorescence patterns. Reference smears are stained by the Romanowsky-Giemsa method and used to count the white blood cell differential in the standard way.
Imaging. Each smear is scanned automatically with a motorized-stage microscope and a digital camera, with camera settings fixed manually for the entire session. The stage moves in a serpentine pattern across a grid of 45 positions (9 x 5, 12% overlap between neighboring frames) at 100x or 60x objective magnification. At every position, a stack of 5 bright-field layers is taken and the sharpest layer is chosen by an image sharpness metric (Tenengrad). The same positions are then scanned again in fluorescence mode (3 layers) using the same grid and the same focus map. Before scanning, the system is calibrated with a dark frame, an excitation background frame, and a slide of luminescent reference granules (green, yellow, and orange-red), which set the color classes and correct for uneven illumination and differences between sessions.
AI analysis with human review. The AI model finds white blood cells in the bright-field image and draws a contour around each one. A trained operator accepts, corrects, or rejects every contour before it is used; no AI result enters the analysis without operator approval. The accepted contours are copied to the fluorescence image of the same position without being recalculated. Inside each contour, fluorescent pixels are assigned to a green, yellow, or red class using thresholds defined by the reference granules; overlap of the yellow and red masks marks "orange" areas, which are provisionally considered to reflect mitochondria. Cells with overexposed pixels are flagged, and those pixels are excluded.
Model development and checking. The AI detector for native (unstained) blood is trained and validated on separate sets of annotated images. A second detector and classifier for stained smears is trained on public datasets (TXL-PBC, PBC) and then fine-tuned on images from the same microscope; it is checked on scans that were not used in training, against labels made by laboratory staff.
Data handling. Images are identified by scan numbers only. Cell-level measurements are kept as individual records and are not averaged before analysis.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Weeks
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Healthy adult volunteers | – | Diagnostic Test: AI-Assisted bright-field and fluorescence microscopy, Diagnostic Test: Romanowsky-Giemsa stained blood smear (reference)Healthy adult volunteers who donate a venous blood sample about once a week for 3 to 6 weeks. Fresh blood smears from each donation are imaged by bright-field and fluorescence microscopy and analyzed with an AI program to find white blood cells and measure the color of fluorescence inside them. Stained reference smears (Romanowsky-Giemsa) are prepared from the same samples. |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- Male only
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Healthy adult volunteers aged 18 to 45 years recruited at the study site (LMB clinic, Almaty, Kazakhstan). Each volunteer donates a venous blood sample about once a week for 3 to 6 weeks, and a fresh blood smear and a Romanowsky-Giemsa stained reference smear are prepared from each sample. Volunteers are not selected for any disease or condition.
Key inclusion criteria (4 listed)
- Healthy adult volunteers aged 18 to 65 years
- Self-reported good health, confirmed by a screening questionnaire
- Willing to give a venous blood sample about once a week for 3 to 6 weeks
- Able to read and sign the informed consent form
Key exclusion criteria (9 listed)
- Acute infection or fever within the last 14 days, or any symptoms of illness on the day of blood draw
- Known blood disease (for example, anemia, leukemia, or a blood clotting disorder) or white blood cell count outside the laboratory reference range, if a complete blood count is available
- Chronic inflammatory, autoimmune, metabolic (including diabetes), or cancer disease
- Use in the last 30 days of corticosteroids, immunosuppressive or chemotherapy drugs, or growth factors that change white blood cell counts (for example, G-CSF)
- Vaccination in the last 14 days
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 |
|---|---|---|
| AI-Assisted bright-field and fluorescence microscopyAutomated scanning of fresh, unfixed blood smears stained with 10-N-nonyl acridine orange and a second dye (Janus) on a motorized-stage microscope (100x or 60x objective, 45 positions per scan). Bright-field and fluorescence images of the same positions are taken. An AI detector (YOLO11) outlines white blood cells in the bright-field image; a trained operator accepts, corrects, or rejects each outline. Accepted outlines are copied to the fluorescence image, and green, yellow, and red signal is measured inside each cell. Smears with other dyes (e.g., pyronin Y) are scanned the same way for comparison. Research use only; no result is used for patient care. | Diagnostic test | Healthy adult volunteers |
| Romanowsky-Giemsa stained blood smear (reference)Standard Romanowsky-Giemsa stained blood smear (monolayer, CLSI H20) prepared from the same blood samples. White blood cell differential is counted by laboratory staff and used as the reference for checking the AI program's detection and classification. | Diagnostic test | Healthy adult volunteers |
Primary outcome
Detection performance of the AI program for white blood cells in unstained blood images
Time frame: Up to 6 weeks (from the first blood draw to completion of image analysis)
Agreement between white blood cells found by the AI program (YOLO11) in bright-field tile images of fresh, unstained blood and cells outlined in the reference annotation of an independent validation set of images. Reported as precision, recall, and F1 score at the chosen confidence threshold, and as mean average precision (mAP@50 and mAP@50-95).
Distribution of green, yellow, and red fluorescence classes in individual white blood cells, by dye
Time frame: Up to 6 weeks (from the first blood draw to completion of image analysis)
For each cell outlined by the AI program and accepted by the operator, fluorescent pixels are sorted into green, yellow, and red classes using thresholds set with luminescent reference granules (g = 1.10 x r; r = 1.35 x g). Each cell is assigned a color class by program rule. Reported for each dye as the number and percent of cells in each class (green, yellow, red) among the first 200 cells analyzed.
2 secondary outcomes
Number of orange (yellow and red overlap) areas per cell, by dye
Time frame: Up to 6 weeks (from the first blood draw to completion of image analysis)
Agreement between AI classification and laboratory staff labels for white blood cells on Romanowsky-Giemsa stained smears
Time frame: Up to 6 weeks (from the first blood draw to completion of image analysis)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Oct 2026.
- CLSI H20, the standard for the reference leukocyte differential count on stained blood smears (Romanowsky-Giemsa method), which is used as the reference method in this studyLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. De-identified individual-level data (image tiles with operator-accepted cell outlines, per-cell fluorescence measurements, and AI training and validation annotations) will be shared with qualified researchers on request. Images are identified by scan numbers only; no personal data are included. The data are not placed in a public repository.
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Screening Protocol
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Healthy Volunteers and Leukocyte Fluorescence Profiling in Whole Blood, run by MIPO Clinic at 1 site in Kazakhstan.
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 4 inclusion and 9 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: finished, as of the sponsor's last update on 7 Oct 2026. 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 Kazakhstan. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection performance of the AI program for white blood cells in unstained blood images (time frame: Up to 6 weeks (from the first blood draw to completion of image analysis)). The study lists 2 primary outcomes in all. 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 7 Oct 2026.