The Efficacy of AI in Detecting Interval Cancers in the National Screening Program of a Middle-Income Country

Purpose We aimed to investigate the efficiency and accuracy of an AI algorithm in detecting interval cancers in a middle-income country national screening program. Material and Methods A total of 2,129,486 mammograms reported as BIRADS 1 and 2 were matched with the national cancer registry for interval cancers (IC). IC group consisted of 442 cases, of which 36 were excluded due to having mammograms incompatible with the AI system. A control group of 446 women with two negative consequent mammograms was defined as time-proven normal and constituted the normal group. Cancer risk scores of both groups were determined from 1 to 10 with the AI system. The sensitivity and specificity values of the AI system were defined in terms of IC detection. IC group was divided into subgroups with six months intervals according to their time from screening to diagnosis as 0-6 months, 6-12 months, 12-18 months, and 18-24 months. The diagnostic performance of the AI system for all patients was evaluated using ROC curve analysis. The diagnostic performance of the AI system for major and minor findings that expert readers determined was re-evaluated. Results AI labeled 53% of ICs with the highest score of 10. The sensitivity of AI in detecting ICs was 53.7% and 38.5% at specificities of 90% and 95%, respectively. AUC of AI in detecting major signs was 0.93 (95% CI: 0.90-0.95) with a sensitivity of 81.6% and 72.4% at specificities of 90% and 95%, respectively (95% CI: 0.73-0.88 and 95% CI: 0.60-0.82 respectively) and minor signs was 0.87 (95% CI: 0.87-0.92) with a sensitivity of 70% and 53% at a specificity of 90% and 95%, respectively (95% CI: 0.65-0.82 and 95% CI: 0.52-0.71 respectively).In subgroup analysis for time to diagnosis, the AUC value of the AI system was higher in the 0-6 month period than in later periods. Conclusion This study showed the potential of AI in detecting ICs in the initial mammograms and reducing human errors and undetected cancers.