Wednesday, December 4 | 10:40 a.m.-10:50 a.m. | SSK18-02 | Room E353C
A team of investigators from Wisconsin has developed an automated informatics-based method for identifying repeat CT exams that may help refine CT protocols and reduce the rate of unnecessary scans.The researchers from the University of Wisconsin School of Medicine and Public Health implemented their technique at a rural hospital and an academic hospital to determine repeat/reject CT rates over a three-year period. The technique additionally calculated the effective radiation doses used for the 10 CT protocols with the highest repeat rate at each site.
Overall, the repeat CT rates at both sites turned out to be the same at 1.4% -- much lower than the average repeat rate for x-ray exams. The method estimated that each of the hospitals could have safely avoided repeating roughly 50 CT exams through protocol-specific interventions.
The method also showed that average radiation dose increased by roughly 108% at the rural hospital and 65% at the academic hospital due to repeat exams for the 10 most frequently repeated CT protocols.
"This is the first automated, informatics-based method for calculating repeat/reject rates in CT," presenter Sean Rose, PhD, told AuntMinnie.com. "We predict that the willingness of federal agencies, insurers, and accrediting bodies to mandate repeat/reject analysis for the tomographic modalities will increase, now that a robust and automated informatics solution exists for repeat/reject analysis."
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
