The Use of AI to Safely Reduce the Workload in Breast Cancer Screening With Mammography in Region Östergötland

NCT ID: NCT06187350

Last Updated: 2025-06-06

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

60000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-08-01

Study Completion Date

2025-03-20

Brief Summary

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The overall aim of the project is to investigate how artificial intelligence (AI) can be used to streamline and at the same time increase diagnostic safety in breast cancer screening with mammography. AI has been shown in a number of studies to have great potential for both increasing diagnostic certainty (e.g. reduced occurrence of interval cancers) and at the same time reducing the workload for doctors. However, much research remains to clinically validate these new tools and to increase the understanding of how they affect the work of doctors. The specific goal of the project is to investigate whether the implementation of AI in breast cancer screening in Östergötland, Sweden, can increase the sensitivity (the mammography examination's ability to find breast cancer) and the specificity (that is, the right case is selected for further investigation: a minimum of healthy women are recalled but so many breast cancer cases that are possible are selected for further investigation) and at the same time make screening more efficient through reduced workload. AI will be implemented in the clinical routine and performance metrics such as cancer detection rate etc will be closely monitored. The study do not assign specific interventions to the study participants.

Detailed Description

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The overall aim of the project is to study whether the use of artificial intelligence can improve breast cancer screening with mammography. AI will be implemented in the clinical routine and performance metrics such as cancer detection rate etc will be closely monitored. The study do not assign specific interventions to the study participants. The specific objective is to investigate whether the use of AI leads to increased diagnostic safety in mammography in Östergötland (measured as a reduced incidence of interval cancer) and at the same time leads to a reduced workload for the breast radiologists. Furthermore, the intention is to investigate how the use of AI affects the breast radiologists´ work in terms of reading time per examination and whether the radiologists' specificity and sensitivity are affected when they have access to the decision support based on AI during the review compared to if they do not have this support.

The hypotheses are that:

1. The use of AI in breast cancer screening in Östergötland Sweden improves the diagnostic quality. As a result, more breast cancer cases are detected early and the incidence of interval cancer decreases.
2. The reduced workload for the radiologists in Östergötland that could be demonstrated through the data collected in Östergötland 2021-2022 \[NCT05048095 - Artificial Intelligence in Breast Cancer Screening in Region Östergötland Linkoping (AI-ROL)\] can also be demonstrated in a large-scale prospective study.
3. Through the use of an AI-based decision support, not only can double review be eliminated for those cases where the AI assesses the cancer risk as low, but also each examination can be reviewed more quickly while maintaining or improving diagnostic certainty.
4. It is the least experienced radiologists who are most helped by the decision support, both for increased diagnostic certainty and increased efficiency.

Conditions

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Artificial Intelligence Breast Diseases Neoplasms Breast Neoplasms

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Screened women in Region Östergötland, Sweden

All screened women in Region Östergötland, Sweden.

AI cancer detection system

Intervention Type DEVICE

The AI system Transpara (Screenpoint Medical, The Netherlands) will be implemented for triaging two-image mammography examinations based on the probability of malignancy. Transpara assigns a score from 1 to 10 to each examination, indicating the risk of malignancy. A score between 1 and 7 indicates a low risk of cancer, 8-9 indicates an intermediate and 10 an elevated risk of cancer. Examinations with an AI score between 1 and 7 will be reviewed by only one radiologist, while examinations with an AI score \> 7 will be double-reviewed as normal.

Interventions

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AI cancer detection system

The AI system Transpara (Screenpoint Medical, The Netherlands) will be implemented for triaging two-image mammography examinations based on the probability of malignancy. Transpara assigns a score from 1 to 10 to each examination, indicating the risk of malignancy. A score between 1 and 7 indicates a low risk of cancer, 8-9 indicates an intermediate and 10 an elevated risk of cancer. Examinations with an AI score between 1 and 7 will be reviewed by only one radiologist, while examinations with an AI score \> 7 will be double-reviewed as normal.

Intervention Type DEVICE

Other Intervention Names

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Screenpoint Transpara

Eligibility Criteria

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Inclusion Criteria

Women participating in the regular Breast Cancer Screening Program in Region Östergötland

Exclusion Criteria

Women with breast implants or other foreign implants in the mammogram Women with symptoms or signs of suspected breast cancer
Minimum Eligible Age

40 Years

Maximum Eligible Age

74 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Ostergotland County Council, Sweden

OTHER

Sponsor Role lead

Responsible Party

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Håkan Gustafsson

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Håkan Gustafsson, Ph.D.

Role: PRINCIPAL_INVESTIGATOR

Region Östergötland

Locations

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Region Östergötland

Linköping, Östergötland County, Sweden

Site Status

Countries

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Sweden

Other Identifiers

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AIM-RÖ

Identifier Type: -

Identifier Source: org_study_id

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