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Artificial Intelligence-Based Machine Learning to Diagnose and Classify Adenomyosis from Ultrasound Scans: a Multicentre Model Development Study

NCT06765512 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 10000

Last updated 2025-01-09

No results posted yet for this study

Summary

The aim of this study is to use the vast dataset of annotated ultrasound images of normal uterus and of adenomyosis of varying severity to train a neural network using deep learning framework (Pytorch) and automated machine learning tool (Vertex AI). The main question it aims to answer are:

1. Diagnostic performance of automated (Google Vertex AI (Artificial intelligence) vision) and deep learning (Pytorch) machine learning model
2. Time saved in assessment of adenomyosis per healthcare professional

Conditions

  • Adenomyosis of Uterus

Interventions

OTHER

use of deep learning and automated machine learning to diagnose and classify adenomyosis

Vertex AI Vision V1 software will be used as an automated machine learning tool and Pytorch 2.5 as deep learning framework. The complete set of reviewed, formatted and labelled images will be uploaded and split manually into two different datasets in 9:1 ratio; 90% of the selected images will be used as training dataset (training + validation) and 10% as test dataset.

Sponsors & Collaborators

  • University of Birmingham

    collaborator OTHER

  • CARE Fertility UK

    lead OTHER

Principal Investigators

  • Mohammed Khairy · CARE Fertility, Birmingham

Eligibility

Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-06-04
Primary Completion
2025-12-04
Completion
2026-02-06

Countries

  • United Kingdom

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06765512 on ClinicalTrials.gov