A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Blood Volume Analysis, Ultrasound, Machine Learning. Basic parameters: 18 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Quantitative Estimation of Preoperative Blood Volume Using Multi-modal Ultrasound and Deep Learning
Overview
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. 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. 2. 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. 3. Study Design: A prospective, single-center, observational study. 4. 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. 5. 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.
Primary outcome measures
- Quantitative estimation of blood volume [Time frame: within 30mins before the surgery]
Eligibility criteria
Inclusion criteria
- 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
Exclusion criteria
- 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 2 centers
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital — Shanghai
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital — Shanghai
Identifiers
NCT: NCT06957587 · 2025-KY-228(K)