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

Quantitative Estimation of Preoperative Blood Volume Using Multi-modal Ultrasound and Deep Learning

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.

Study Overview

• Status: Recruiting
• Conditions: Machine Learning; Ultrasound; Blood Volume Analysis

• Study Type: Observational
• Enrollment (Estimated): 800

Contacts and Locations

• Study Contact: Name: xiuxiu sun, MD; Phone Number: 56428 021-64369181; Email: liuyuanec@163.com

Study Locations

• China
  • Shanghai Municipality
    • Shanghai, Shanghai Municipality, China, 200235
      • Not yet recruiting
      • Shanghai Jiao Tong University Affiliated Sixth People's Hospital
      • Contact: xiuxiu sun, Mrs; Phone Number: 021-64369181-56428; Email: liuyuanec@163.com
    • Shanghai, Shanghai Municipality, China, 200235
      • Recruiting
      • Shanghai Jiao Tong University Affiliated Sixth People's Hospital
      • Contact: aizhong wang, PhD; Phone Number: 56980 021-64369181; Email: 18930177431@163.com
      • Principal Investigator: xiaofeng wang, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients prepare to receive surgery.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Quantitative estimation of blood volume	We will estimate the basic blood volume of the patients quantitatively with the Acute Hemodilution technique before the surgery.	within 30mins before the surgery

Collaborators and Investigators

• Sponsor: Shanghai 6th People's Hospital

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2025
• Primary Completion (Estimated): July 31, 2027
• Study Completion (Estimated): August 31, 2027

Study Registration Dates

• First Submitted: April 26, 2025
• First Submitted That Met QC Criteria: April 26, 2025
• First Posted (Actual): May 4, 2025

Study Record Updates

• Last Update Posted (Actual): November 17, 2025
• Last Update Submitted That Met QC Criteria: November 13, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Other Study ID Numbers: 2025-KY-228(K)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No