Can artificial intelligence, applied on ultrasound images, discriminate benign and malignant ovarian tumours, and thus be used in the triage of women with these lesions? An external international multicentre validation study by the Ovarian Tumour Machine Learning Collaboration (OMLC)

External validation of the deep learning models Ovry-Dx1 and Ovry-Dx2, applied on ultrasound images, to discriminate benign and malignant ovarian tumours. An external international multicentre validation study by the Ovarian Tumour Machine Learning Collaboration (OMLC)

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN51927471

Enrollment

1600

Registered

2020-07-24

Start date

2020-07-31

Completion date

Unknown

Last updated

2026-08-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ovarian tumours Cancer

Interventions

Observational study: Multi-centre (n=22) study, including at least 6,000 images from at least 2,000 cases (1,000 benign and 1,000 malignant) of adnexal lesions, with known histological outcome from su

benign or malignant and the certainty in the assessment will be used for comparative analysis. All cases will also undergo external review by 3 experts from other centres, evaluating tumours as benign

Eligibility

Sex/Gender

Female

Inclusion criteria

Inclusion criteria: 1. Women with adnexal lesions undergoing structured ultrasound examination prior to surgery 2. At least 3 good quality, representative ultrasound images per case 3. Histological outcome form surgery available

Exclusion criteria

Exclusion criteria: Does not meet inclusion criteria

Design outcomes

Secondary

Measure	Time frame
Data collected from patient records: 1. Case ID 2. Subjective expert assessment prior to surgery 3. Classification of tumours (benign, borderline or malignant) 4. The certainty in the assessment (uncertain vs. certain) 5. Histological outcome (benign/malignant) 6. Specific histological diagnosis form surgery 7. Date of examination 8. Ultrasound system used	—

Primary

Measure	Time frame
Diagnostic performance of the previously developed deep learning models (Ovry-Dx1 and Ovry-Dx2) in discriminating benign and malignant lesions. These models were created by transfer learning on three pre-trained DNNs: VGG16, ResNet50 and MobileNet. Each model was trained, and the outputs calibrated using temperature scaling. An ensemble of the three models was then used to estimate the probability of malignancy based on all images from a given case. Using DNNs, tumours were classified as benign or malignant (Ovry-Dx1); or benign, inconclusive or malignant (Ovry-Dx2).	—

Measure

Time frame

Diagnostic performance of the previously developed deep learning models (Ovry-Dx1 and Ovry-Dx2) in discriminating benign and malignant lesions. These models were created by transfer learning on three pre-trained DNNs: VGG16, ResNet50 and MobileNet. Each model was trained, and the outputs calibrated using temperature scaling. An ensemble of the three models was then used to estimate the probability of malignancy based on all images from a given case. Using DNNs, tumours were classified as benign or malignant (Ovry-Dx1); or benign, inconclusive or malignant (Ovry-Dx2).

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Countries

Belgium, Czech Republic, Greece, Italy, Lithuania, Philippines, Poland, Spain, Sweden

Contacts

Public ContactElisabeth Epstein

Elisabeth.epstein@ki.se+46 706699019

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Aug 25, 2026