Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload

Abstract

Objective To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. Methods Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. Results AI as a supporting reader was found to be superior or non-inferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). Conclusion The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree.

Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning’s Role in Breast Imaging beyond Screening Mammography

“…In this vein of continuous improvement the proficiency of AI has drastically exceeded the performance of traditional CAD techniques, approaching or exceeding the accuracy of radiologists at various tasks in mammography including lesion detection [ 1 , 8 , 9 ]. AI is also gaining increasing traction for a role as a digital second reader [ 10 , 11 ], with some studies proposing the use of AI to sort confidently negative studies for worklist reduction [ 12 , 13 ].…”

Comparing Prognostic Factors of Cancers Identified by Artificial Intelligence (AI) and Human Readers in Breast Cancer Screening

“…Cancers 2023, 15, 3069 2 of 12 Studies have reported that AI performance is comparable to or might even outperform humans in the interpretation of screening studies [4][5][6][7][8]. In these studies, performance is often assessed in terms of general outcome metrics such as cancer detection rate and recall rate [9], but information on the prognostic features of screen-detected breast cancers is frequently not provided.…”