Kheiron Medical Technologies, London, UK.
Imperial College London, Department of Computing, London, UK.
J Breast Imaging. 2023 May 22;5(3):267-276. doi: 10.1093/jbi/wbad010.
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.
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.
Artificial intelligence as a supporting reader was found to be superior or noninferior 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).
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.
评估一种新的人工智能（AI）辅助阅读策略在乳腺 X 线摄影基础的双重阅读筛查实践中用于检测乳腺癌的有效性。
使用大规模多站点、多供应商数据来回顾性评估 AI 辅助阅读的新范例。在此，仅当 AI 与第一人类读者的召回/不召回决策一致时，AI 才作为第二读者。否则，由第二位人类读者进行评估，然后按照标准临床工作流程进行操作。该数据包括来自两个国家七个筛查点的 180542 名女性参与者的 280594 例乳腺 X 线摄影筛查病例，使用来自四家硬件供应商的设备。统计分析包括癌症筛查性能的非劣效性和优效性检验，以及评估工作量减少，以仲裁率和需要第二个人阅读的病例数量来衡量。
与人类双重阅读相比，人工智能作为辅助读者在所有筛查指标上均表现出优越性或非劣效性，同时减少了高达 87%（245395/280594）需要第二个人阅读的病例数量。与 AI 作为独立读者相比，提交仲裁的病例数量从 13%（35199/280594）减少到 2%（5056/280594）。
模拟表明，所提出的工作流程保留了人类双重阅读的筛查性能，同时大大减少了工作量。进一步的研究应研究对第二人类读者的影响，因为他们只评估 AI 预测和第一人类读者意见不一致的病例。
