Can we use an artificial intelligence system to improve the quality and efficiency of breast cancer screening?

Artificial Intelligence in Mammography Study (AIMS) Part C - Feasibility of an artificial intelligence system to improve the quality and efficiency of breast cancer screening

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN88754382

Enrollment

14000

Registered

2022-06-20

Start date

2023-11-27

Completion date

Unknown

Last updated

2026-03-23

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

Conditions

Decision support in breast cancer screening Other

Interventions

Data will be collected prospectively from the breast screening programme (NBSS) Mammography Image Database, with patient consent. There will be no impact on patient care. The intervention is the AI sy

Eligibility

Sex/Gender

Female

Age

50 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1. Women undergoing routine breast cancer screening (age 50–70), as part of the national breast screening programme at Imperial College Healthcare NHS Trust and St George’s University Hospital NHS Foundation Trust between the study dates. 2. Mammography images acquired using Hologic/Lorad, Siemens, or GE devices.

Exclusion criteria

Exclusion criteria: 1. Women that opt-out of this study 2. Women who have registered with the NHS national data opt-out

Design outcomes

Primary

Measure	Time frame
1. Time taken for the AI system to return results from mammograph images over the study dataset time period 2. Analysis of number of failure cases ( such as such as model errors, software errors, integration errors, use errors, and hardware errors) for the study dataset time period. Accuracy will be measured as proportion of true results (both true positives and true negatives) among whole instances. Area under the receiver operating characteristic curve (ROC) will be measured for AI 3. Percentage of cases correctly excluded during eligibility checks and reasons do excursion during the study period	—

Measure

Time frame

1. Time taken for the AI system to return results from mammograph images over the study dataset time period 2. Analysis of number of failure cases ( such as such as model errors, software errors, integration errors, use errors, and hardware errors) for the study dataset time period. Accuracy will be measured as proportion of true results (both true positives and true negatives) among whole instances. Area under the receiver operating characteristic curve (ROC) will be measured for AI 3. Percentage of cases correctly excluded during eligibility checks and reasons do excursion during the study period

—

Secondary

Measure	Time frame
1. Accuracy measures including AI recall rate measured as proportion of true results (both true positives and true negatives) 2. AI sensitivity and specificity with respect to arbitrated recall decisions (measured as the number of positive cases (cases considered positive if they received a biopsy-confirmed diagnosis of cancer within 3 months following the screening visit. Negative cases will require a negative result from the study screening visit) 3. AI sensitivity for biopsy-proven cancer u(true positive rate in percentage(%) derived by ROC analysis) 4. AI specificity for biopsy or diagnostic imaging-proven benign lesions (true negative rate in percentage (%) derived by ROC analysis)	—

Measure

Time frame

1. Accuracy measures including AI recall rate measured as proportion of true results (both true positives and true negatives) 2. AI sensitivity and specificity with respect to arbitrated recall decisions (measured as the number of positive cases (cases considered positive if they received a biopsy-confirmed diagnosis of cancer within 3 months following the screening visit. Negative cases will require a negative result from the study screening visit) 3. AI sensitivity for biopsy-proven cancer u(true positive rate in percentage(%) derived by ROC analysis) 4. AI specificity for biopsy or diagnostic imaging-proven benign lesions (true negative rate in percentage (%) derived by ROC analysis)

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Countries

England, United Kingdom

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Apr 3, 2026