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Elvira Grandone Announces the 1st ThrombUS+ Data Challenge

Elvira Grandone/LinkedIn

Dec 10, 2025, 14:57

Elvira Grandone Announces the 1st ThrombUS+ Data Challenge

Elvira Grandone, Associate Professor at University of Foggia, shared on LinkedIn:

”Happy to announce that the 1st ThrombUS+ Data Challenge is NOW LIVE, available in Kaggle and as part of the BIOSTEC 20026 Conference.

Overview
DVT diagnosis relies on experts manually assessing vein compressibility in ultrasound videos. This data challenge aims to foster the development of AI models for DVT detection without the need for exhaustive pixel-wise annotations, reducing the burden of manual labeling.

Challenge participants are expected to submit their responses to one or both of the respective data challenges in Kaggle, submit a paper in the AI4DVT workshop in BIOSTEC conference.

There will be a monetary prize for the first 3 most successful submissions.

The Dataset
Compression ultrasound videos of lower limbs collected during a multi-center cohort study across European hospitals. Patients suspected of DVT were scanned using conventional ultrasound machines according to a dedicated protocol, with full ethics approval and informed consent.

Videos capture four key anatomical sites: Near the inguinal ligament (common femoral vein); a few centimeters distally (great saphenous vein junction); Mid-thigh (femoral vein); Below the knee (popliteal vein).

All videos have been anonymized and tagged with anatomical site and limb laterality.

Medical experts have assessed each video for vein compressibility (No/Partial/Yes) and thrombosis presence (Yes/No), providing ground truth annotations for model training.

Training data set of 2341 videos acquired from 594 patients from 5 hospitals.

Challenge 1.1: Detect DVT Presence
Your task is to build a machine learning model that analyzes compression videos and calculates the probability that the video corresponds to a thrombosis case, evaluated on Log Loss.

Challenge 1.2: Anatomical Site Identification
Your task is to build a machine learning model that can look at compression videos and accurately identify the anatomical site where scan was taken from (choice out of 4 anatomical sites).

Paper Submission
The data challenge is an official component of the 2026 BIOSTEC Conference which will take place in Marbella, Spain, 2-4 March 2025. Challenge participants are expected to submit a paper to the conference workshop “ThrombUS+ Data Challenge: A Machine Learning Challenge for Automated DVT diagnosis based on Compression Ultrasound Videos. – AI4DVT 2026”
Conference page.
Workshop link.

Related links:
Training data set.
Testing data set.
Challenge 1.1.
Challenge 1.2.
BIOSTEC workshop.”

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