Actively Recruiting
A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound
Led by Shanghai 6th People's Hospital · Updated on 2025-11-17
800
Participants Needed
2
Research Sites
99 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Agree to join this study and sign the informed consent form
- Age between 18 and 75 years old (inclusive)
- Body mass index (BMI) between 18 and 30 kg/m2
- American Society of Anesthesiologists (ASA) physical status grades I or II
You will not qualify if you...
- Preoperative hemoglobin (Hb) less than 10 g/dl
- Cardiac dysfunction classified as NYHA class III-IV
- Respiratory dysfunction classified as ATS class 2-4
- History of liver or kidney dysfunction, including abnormal transaminase, albumin, bilirubin, hepatitis history, or elevated serum creatinine/urea nitrogen
- Nervous system abnormalities preventing cooperation, such as stroke, Alzheimer disease, or related sequelae
- Poor ultrasound imaging of inferior vena cava, internal jugular vein, subclavian vein, or common carotid artery
- Presence of venous thrombosis or anatomical abnormalities in these vessels
- Multiple injuries involving chest, abdomen, or brain
- Pregnancy
AI-Screening
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Trial Site Locations
Total: 2 locations
1
Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Shanghai, Shanghai Municipality, China, 200235
Not Yet Recruiting
2
Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Shanghai, Shanghai Municipality, China, 200235
Actively Recruiting
Research Team
X
xiuxiu sun, MD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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