AI can improve ovarian cancer diagnoses

AI-based models can outperform human experts at identifying ovarian cancer in ultrasound images, an international study has shown.

Researchers have developed and validated neural network models that are able to differentiate between benign and malignant ovarian lesions. The team has trained and tested the AI on over 17,000 ultrasound images from 3,652 patients across 20 hospitals in eight countries, comparing the models’ diagnostic capacity with a large group of experts and less experienced ultrasound examiners.

The results showed that the AI models outperformed both expert and non-expert examiners at identifying ovarian cancer, achieving an accuracy rate of 86.3 per cent, compared to 82.6 per cent and 77.7 per cent for the expert and non-expert examiners respectively.

“This suggests that neural network models can offer valuable support in the diagnosis of ovarian cancer, especially in difficult-to-diagnose cases and in settings where there’s a shortage of ultrasound experts,” said professor Elisabeth Epstein at Karolinska Institutet, which led the study, and senior consultant.

“Ovarian tumours are common and are often detected by chance. There is a serious shortage of ultrasound experts in many parts of the world, which has raised concerns of unnecessary interventions and delayed cancer diagnoses. We therefore wanted to find out if AI can complement human experts.”

Reducing the need for expert referrals

In a simulated triage situation, the AI support cut the number of referrals by 63 per cent and the misdiagnosis rate by 18 per cent. This can lead to faster and more cost-effective care for patients with ovarian lesions.

Despite the promising results, the researchers stress that further studies are needed before the full potential of the neural network models and their clinical limitations are fully understood.

“With continued research and development, AI-based tools can be an integral part of tomorrow’s healthcare, relieving experts and optimising hospital resources, but we need to make sure that they can be adapted to different clinical environments and patient groups,” says Filip Christiansen, doctoral student at Karolinska Institutet and joint first author.

The researchers are now conducting prospective clinical studies to evaluate the everyday clinical safety and usefulness of the AI tool. Future research will also include a randomised multicentre study to examine its effect on patient management and healthcare costs.