Blood Volume AnalysisNCT06957587
A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound
An observational study of Blood Volume Analysis and Ultrasound, run by Shanghai 6th People's Hospital at 2 sites in China.
Trial summary
1. Background \& Rationale: Accurate assessment of a patient's blood volume (BV) status before surgery is critical for preventing perioperative complications. However, there is currently no clinically feasible, accurate, and non-invasive method for direct BV quantification. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Blood Volume Analysis, Ultrasound, Machine Learning
- How many people
- 800 participants
- Where
- 2 sitesChina
- Study dates
- 1 Oct 2025to 31 Aug 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients prepare to receive surgery.
Who may be able to join
- Agree to join this study and sign the informed consent form;
- Age between 18 and 75 years old (inclusive);
- BMI (body mass index) is between 18 and 30 kg/m2;
- American Society of Anesthesiologists (ASA) grades I-II
Who cannot join
- Preoperative hemoglobin (Hb) <10g/dl
- Cardiac dysfunction (NYHA class III-IV), respiratory dysfunction (ATS class 2-4), history of liver and kidney dysfunction (such as transaminase / albumin / bilirubin abnormalities, hepatitis history, serum creatinine / urea nitrogen rise, etc.), nervous system abnormalities (those who cannot cooperate due to stroke or its sequelae, Alzheimer, etc.);
- The ultrasonic display of inferior vena cava, internal jugular vein, subclavian vein or common carotid artery is extremely poor, venous thrombosis or anatomical abnormalities;
- Multiple injury with chest, abdomen or brain;
- Pregnant woman
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Not yet recruiting
Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Shanghai, Shanghai Municipality, 200235
xiuxiu sun, Mrs, 021-64369181-56428, liuyuanec@163.com
- Recruiting
Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Shanghai, Shanghai Municipality, 200235
aizhong wang, PhD, 021-64369181 ext. 56980, 18930177431@163.com
xiaofeng wang, MD
About this study
Official title: Quantitative Estimation of Preoperative Blood Volume Using Multi-modal Ultrasound and Deep Learning
- Background \& Rationale: Accurate assessment of a patient's blood volume (BV) status before surgery is critical for preventing perioperative complications. However, there is currently no clinically feasible, accurate, and non-invasive method for direct BV quantification. We hypothesize that dynamic ultrasound videos of major blood vessels contain rich, sub-visual spatiotemporal information about vascular compliance and filling that can be leveraged to estimate BV.
- Objective: To develop and validate a deep learning model that integrates multi-modal ultrasound video data to achieve non-invasive, quantitative estimation of preoperative blood volume.
- Study Design: A prospective, single-center, observational study.
- Methods: Participants: Adult patients scheduled for surgery. Data Acquisition: Input (Features): Preoperative ultrasound video clips will be recorded in standardized views of four key vessels: the Internal Jugular Vein (IJV), Subclavian Vein (SCV), Inferior Vena Cava (IVC), and Common Carotid Artery (CA). Target (Label): The true Blood Volume (BV) will be calculated for each patient using the acute normovolemic hemodilution (ANH) method. The change in hemoglobin concentration before and after this process is used to calculate the total blood volume with high clinical reliability. Model Development: A hybrid deep learning architecture (e.g., CNN + LSTM/Transformer) will be trained to extract features from the ultrasound videos and learn the complex, non-linear mapping to the BV value derived from ANH. The model will be trained and internally validated using a k-fold cross-validation approach.
- Expected Outcome \& Significance:
We anticipate the development of a novel, end-to-end deep learning model capable of providing a quantitative BV estimate from routine ultrasound scans. This technology has the potential to revolutionize perioperative fluid management by offering a rapid, non-invasive, and accurate tool for objective volume status assessment, ultimately guiding personalized therapy and improving patient outcomes.
- Conditions
- Blood Volume Analysis, Ultrasound, Machine Learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| patients prepare to receive surgery | The patients aged 18-75 years old prepare to receive surgery will be assigned into the cohort. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Quantitative estimation of blood volume
We will estimate the basic blood volume of the patients quantitatively with the Acute Hemodilution technique before the surgery.
Measured: within 30mins before the surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 May 2025
- Study start1 Oct 2025
- Primary completion31 Jul 2027 (estimated)
- Study completion31 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Blood Volume AnalysisStudies 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
- 800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai 6th People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiaofeng WANG, Shanghai 6th People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06957587
- Sponsor's protocol ID
- 2025-KY-228(K)
- Status last verified
- Nov 2025
- First submitted
- 26 Apr 2025
- Last updated
- 17 Nov 2025
- 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 17 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Quantitative Estimation of Preoperative Blood Volume Using Multi-modal Ultrasound and Deep Learning
- ClinicalTrials.gov ID
- NCT06957587
- Acronym / sponsor ID
- 2025-KY-228(K)
- Conditions
- Blood Volume Analysis, Ultrasound, Machine Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 800 participants (estimated)
- Lead sponsor
- Shanghai 6th People's HospitalUniversity, hospital or other
- Study start
- 1 Oct 2025 (actual)
- Primary completion
- 31 Jul 2027 (estimated)
- Study completion
- 31 Aug 2027 (estimated)
- First posted
- 4 May 2025 (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.
- Background \& Rationale: Accurate assessment of a patient's blood volume (BV) status before surgery is critical for preventing perioperative complications. However, there is currently no clinically feasible, accurate, and non-invasive method for direct BV quantification. We hypothesize that dynamic ultrasound videos of major blood vessels contain rich, sub-visual spatiotemporal information about vascular compliance and filling that can be leveraged to estimate BV.
- Objective: To develop and validate a deep learning model that integrates multi-modal ultrasound video data to achieve non-invasive, quantitative estimation of preoperative blood volume.
- Study Design: A prospective, single-center, observational study.
- Methods: Participants: Adult patients scheduled for surgery. Data Acquisition: Input (Features): Preoperative ultrasound video clips will be recorded in standardized views of four key vessels: the Internal Jugular Vein (IJV), Subclavian Vein (SCV), Inferior Vena Cava (IVC), and Common Carotid Artery (CA). Target (Label): The true Blood Volume (BV) will be calculated for each patient using the acute normovolemic hemodilution (ANH) method. The change in hemoglobin concentration before and after this process is used to calculate the total blood volume with high clinical reliability. Model Development: A hybrid deep learning architecture (e.g., CNN + LSTM/Transformer) will be trained to extract features from the ultrasound videos and learn the complex, non-linear mapping to the BV value derived from ANH. The model will be trained and internally validated using a k-fold cross-validation approach.
- Expected Outcome \& Significance:
We anticipate the development of a novel, end-to-end deep learning model capable of providing a quantitative BV estimate from routine ultrasound scans. This technology has the potential to revolutionize perioperative fluid management by offering a rapid, non-invasive, and accurate tool for objective volume status assessment, ultimately guiding personalized therapy and improving patient outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| patients prepare to receive surgery | – | –The patients aged 18-75 years old prepare to receive surgery will be assigned into the cohort. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients prepare to receive surgery.
Key inclusion criteria (4 listed)
- Agree to join this study and sign the informed consent form;
- Age between 18 and 75 years old (inclusive);
- BMI (body mass index) is between 18 and 30 kg/m2;
- American Society of Anesthesiologists (ASA) grades I-II
Key exclusion criteria (5 listed)
- Preoperative hemoglobin (Hb) <10g/dl
- Cardiac dysfunction (NYHA class III-IV), respiratory dysfunction (ATS class 2-4), history of liver and kidney dysfunction (such as transaminase / albumin / bilirubin abnormalities, hepatitis history, serum creatinine / urea nitrogen rise, etc.), nervous system abnormalities (those who cannot cooperate due to stroke or its sequelae, Alzheimer, etc.);
- The ultrasonic display of inferior vena cava, internal jugular vein, subclavian vein or common carotid artery is extremely poor, venous thrombosis or anatomical abnormalities;
- Multiple injury with chest, abdomen or brain;
- Pregnant woman
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Quantitative estimation of blood volume
Time frame: within 30mins before the surgery
We will estimate the basic blood volume of the patients quantitatively with the Acute Hemodilution technique before the surgery.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Nov 2025.
- Individual participant dataSharing planned: Undecided
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.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Blood Volume Analysis and Ultrasound, run by Shanghai 6th People's Hospital at 2 sites in China.
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 5 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: looking for participants now, as of the sponsor's last update on 17 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact xiuxiu sun, MD (021-64369181 ext. 56428, liuyuanec@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Quantitative estimation of blood volume (time frame: within 30mins before the surgery). 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 17 Nov 2025; AzTrials last refreshed its copy on 27 Oct 2026.