NCT06957587

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

Recruiting now Last updated 17 November 2025

What this trial tests

trial in Blood Volume Analysis in 800 participants. Currently enrolling.

Timeline

1 October 2025

Primary endpoint
31 July 2027

31 August 2027

Quick facts

Lead sponsor	Shanghai 6th People's Hospital
Status	Recruiting now
Study type	OBSERVATIONAL
Enrollment	800
Start date	1 October 2025
Primary completion	31 July 2027
Estimated completion	31 August 2027
Sites	2 locations across China

Conditions studied

• Blood Volume Analysis — all drugs for Blood Volume Analysis →
• Ultrasound — all drugs for Ultrasound →
• Machine Learning — all drugs for Machine Learning →

Sponsor

Shanghai 6th People's Hospital

Who can join

Adults 18 to 75, any sex, with Blood Volume Analysis or Ultrasound. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.