The use of artificial intelligence software in breast screening (Transpara®) makes it possible to identify studies with a very low probability of cancer. The hypothesis raised in this work is that reading strategies based on artificial intelligence (single or double reading only of cases with a score\> 7 with Transpara®), allow reducing the workload of a screening program by more than 50 % with respect to the standard reading of the program (double reading of all cases without Transpara®), without presenting inferiority in terms of detection rates and recalls of the program, both with the use of 2D digital mammography and with the use of tomosynthesis or 3D mammogram.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
31,301
In the women participating in the study, two strategies for reading mammograms will be carried out: Strategy 1: Standard reading of the program. Double independent and non-consensual reading of all cases, without any artificial intelligence system (standard strategy). Strategy 2: Reading strategy based on the global Score granted by Transpara® (strategy based on artificial intelligence): * In studies with a Score \<8 (studies with a low probability of cancer): They will not be evaluated by any radiologist. * In studies with a Score\> 7 (studies with a high probability of cancer): double reading will be carried out, assisted by Transpara®.
Hospital Universitario Reina Sofia
Córdoba, Córdoba, Spain
Assessment of Workload of each strategy
The workload of each strategy shall be assessed by multiplying the average time for a reading of that strategy by the total number of readings of that strategy. The average reading time of a case in each strategy shall be calculated from the measurement of the individual reading time in a sample of 500 cases in each strategy.
Time frame: In the middle of the study, at 1 year.
Assessment of Workload of each strategy
The workload of each strategy shall be assessed by multiplying the average time for a reading of that strategy by the total number of readings of that strategy. The average reading time of a case in each strategy shall be calculated from the measurement of the individual reading time in a sample of 500 cases in each strategy.
Time frame: At the end of the study, at 2 years.
Detection rate
Proportion of women diagnosed with breast cancer among those screened.
Time frame: In the middle of the study, at 1 year.
Detection rate
Proportion of women diagnosed with breast cancer among those screened.
Time frame: At the end of the study, at 2 years.
Recall or referral rate
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.
Time frame: In the middle of the study, at 1 year.
Recall or referral rate
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.
Time frame: At the end of the study, at 2 years.
Positive predictive value of referrals
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Time frame: In the middle of the study, at 1 year.
Positive predictive value of referrals
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Time frame: At the end of the study, at 2 years.
Positive predictive value of biopsies
Proportion of women with breast cancer among all women undergoing biopsy.
Time frame: In the middle of the study, at 1 year.
Positive predictive value of biopsies
Proportion of women with breast cancer among all women undergoing biopsy.
Time frame: At the end of the study, at 2 years.
Positive predictive value of Transpara® scores
Proportion of breast cancers diagnosed among women with a given score.
Time frame: In the middle of the study, at 1 year.
Positive predictive value of Transpara® scores
Proportion of breast cancers diagnosed among women with a given score.
Time frame: At the end of the study, at 2 years.
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