Population-Based Evaluation of Artificial Intelligence for Mammography Prior to Widespread Clinical Translation

在广泛临床转化之前对乳腺 X 线摄影人工智能进行基于人群的评估

• 批准号: 10651842
• 负责人: CHRISTOPH I LEE
• 金额: $ 61.29万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651842
• 关键词: 3-Dimensional; Academy; Address; Adopted; Algorithms; Artificial Intelligence; Biometry; Breast Cancer Detection; Breast Cancer Epidemiology; Breast Cancer Surveillance Consortium; Cancer Detection; Cancer Intervention and Surveillance Modeling Network; Characteristics; Clinical; Clinical effectiveness; Clinical/Radiologic; Data; Data Set; Detection; Digital Breast Tomosynthesis; Digital Mammography; Effectiveness; Ensure; Evaluation; Feedback; Funding; Future; Geography; Health Benefit; Health Services Research; Healthcare; Human; Image; Industry; Informatics; Infrastructure; Institution; Internal Medicine; International; Knowledge; Label; Link; Malignant Neoplasms; Mammographic screening; Mammography; Medicine; Modeling; Outcome; Patient-Focused Outcomes; Performance; Physicians; Policy Maker; Population; Privatization; Prospective Studies; Reader; Registries; Screening procedure; Target Populations; Technology; Technology Assessment; Testing; Time; Translations; Triage; United States; United States Food and Drug Administration; Update; Validation; Visual Perception; Woman; Women' s Group; algorithm training; artificial intelligence algorithm; breast imaging; cancer diagnosis; clinical practice; clinical translation; cohort; comparative; computer aided detection; cost; cost effectiveness; deep learning algorithm; deep neural network; detection platform; digital technology; follow-up; improved; improved outcome; industry partner; malignant breast neoplasm; models and simulation; mortality; multilevel analysis; neoplasm registry; novel; population based; prospective; radiologist; screening; tool; tumor

PROJECT SUMMARY Multiple artificial intelligence (AI) technologies are now commercially available for automated interpretation of screening mammography. These AI technologies hold promise for improving screening performance and outcomes for the 40 million U.S. women who undergo routine breast cancer screening each year. Federal regulatory approval of new AI technologies requires only a demonstration of non-inferior accuracy to existing computer-aided detection systems in small, retrospective reader studies, but their widespread clinical translation is contingent upon more robust population-based evaluation. Specifically, the impact of these AI technologies on actual patient outcomes needs to be assessed, including whether or not they lead to improved detection of clinically meaningful cancers in the general screening population. Robust external validation of AI algorithms for mammography screening has thus far been limited by use of single institution datasets not representative of the entire target population, use of AI algorithms that are not publicly available, comparison to radiologist performance in enriched case sets, limited follow-up time for cancer diagnoses influencing ground truth labels, and evaluation on 2D digital mammography rather than 3D digital breast tomosynthesis (DBT) exams. Our study objective is to conduct a comparative evaluation of five commercially available AI technologies for automated DBT screening interpretation that overcomes all of these limitations and then estimate the long-term benefits, harms, and costs of AI-driven DBT screening at the U.S. population level. Specifically, we will 1) use a centralized honest broker, model-to-data paradigm infrastructure to perform an independent, external validation of five leading commercial AI technologies for DBT screening using prospectively collected data obtained from eight diverse U.S. regional breast imaging registries; 2) stratify AI vs. radiologist performance on detailed woman-, exam-, radiologist-, and tumor-level characteristics to inform targeted algorithm training and refinement efforts to ensure generalizability of the AI algorithms; 3) explore targeted approaches for improving clinical workflow efficiency by using AI to safely triage exams highly likely to be negative; and 4) use a validated breast cancer microsimulation model to determine population-level, long- term health benefits, harms, and costs associated with AI technologies for DBT screening both as a standalone screening tool and as a second independent reader to radiologist interpretation. Our proposed study will represent the most objective and rigorous evaluation of deep learning algorithms for DBT screening interpretation in the U.S. to date. Our results will provide urgently needed evidence to inform key stakeholders including women, physicians, payers, industry partners, and policymakers regarding how to maximize the value of AI technologies for DBT screening prior to their widespread clinical translation.

项目总结