IOTA-AI - AI-assisted automated detection of ovarian cancer on ultrasound imaging

From 01-09-2021 to 31-08-2023

Description

Ovarian cancer is the seventh most common cancer in women and constitutes the most lethal gynecological malignancy. Timely diagnosis and appropriate referral to gynecological oncology expert centers is pivotal to improve patient outcomes. Ultrasonography (US) is a readily available, cheap and harmless technique, and is widely accepted as first-line imaging modality for assessment of ovarian masses.

Currently, the ADNEX model is the best available ultrasound-based mathematical model to differentiate between benign and several types of malignant ovarian tumors. However, it relies on the ability of the US operator to reliably (manually) locate, delineate and measure the tumor area and its associated features. Previously, we developed automated feature detection methods in collaboration with ESAT-STADIUS and General Electric, where we demonstrated the effectiveness of a deep convolutional neural network (DCNN) approach. The objective of this C3 project is to clinically validate these models, and to develop a fully-automated classification model for ovarian cancer detection based on 2D and 3D ultrasound data.

Team

• Bart De Moor, Associated partner
• Nikhil Sindhwani, Co-promoter
• Dirk Timmerman, Co-promoter (External)
• Wouter Froyman, Coordinator (External)

Financing

Funding: KU Leuven - Internal Funding KU Leuven

Program/Grant Type: C3 - KU Leuven Category 3 Funding