A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

800 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-10-01

Study Completion Date

2027-08-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Blood Volume Analysis Ultrasound Machine Learning

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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patients prepare to receive surgery

The patients aged 18-75 years old prepare to receive surgery will be assigned into the cohort.

No interventions assigned to this group

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No