DeepCertainty: Deep Learning for Contextual Diagnostic Uncertainty Measurement in Radiology Reports

DeepCertainty：放射学报告中上下文诊断不确定性测量的深度学习

基本信息

批准号：
10593770
负责人：
Feifan Liu
金额：
$ 18.84万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10593770
关键词：
Accident and Emergency department Address Adopted Adoption Agreement American College of Radiology Angiography Awareness Calibration Cardiovascular system Caring Categories Cessation of life Clinical Communication Complex Computing Methodologies Data Data Set Decentralization Diagnosis Diagnostic Diagnostic Errors Diagnostic Imaging Diagnostic tests Disease Effectiveness Emergency Department Physician Emergency Department patient Emergency Situation Emergency department visit Ensure Evaluation Exclusion Feedback Foundations Funding Grain Guidelines Healthcare Hospitalization Image Image Analysis Individual Inpatients Institution Intensive Care Units Knowledge Language Learning Lung Magnetic Resonance Imaging Manuals Measurement Measures Medical Modeling Morbidity - disease rate Natural Language Processing Outpatients Patient Care Patients Perception Persons Physicians Process Pulmonary Embolism Quality of Care Radiology Specialty Reporting Sampling Semantics Signs and Symptoms Specific qualifier value Standardization Stress Structure System Technology Testing Time Uncertainty United States Variant Workplace X-Ray Computed Tomography accurate diagnosis accurate diagnostics adverse outcome clinical decision-making computerized cost outcomes deep learning design future implementation imaging modality impression improved innovation lexical mortality neural prevent radiologist rapid diagnosis statistics unnecessary treatment user-friendly

项目摘要

PE becomes the third leading cause of cardiovascular-related death, and more than 500,000 cases of PE occur in the United States (US) every year, resulting in approximately 200,000 deaths and hospitalization of over 250,000 patients. Rapid and accurate diagnosis of PE are of paramount importance to ensure the highest quality of care. Every year 1-2% of the 120 million emergency department (ED) patients in the US undergo computed tomographic pulmonary angiography (CTPA) for PE. The referring physicians rely heavily on CTPA reports diagnosing or excluding PEs. Clarity of the radiology report is one of the most critical qualities, and the American College of Radiology has emphasized a need for precision communication in radiological reports. Yet communicating uncertainty effectively in radiology reports is challenging. Referring physicians may interpret radiologists’ textual expressions that convey diagnostic confidence differently than intended. The gap between radiologists’ intended message and the referring physicians’ interpretation can not be completely resolved through structured reporting or standardized lexicon. Unnecessary hedging language in CTPA reports may further worsen the reporting ambiguity and may lead to inappropriate treatment of patients. Therefore, we aim to develop a deep learning-based approach for context-aware (un)certainty assessment (DeepCertainty), which is end-to-end trainable, calibratable, generalizable, scalable, and explainable. It would allow for fine- grained uncertainty measurement and standardization, facilitate consistent and accurate diagnostic certainty communication in CTPA reports and thus improve PE care. This study will build the foundation for future implementation and integration of DeepCertainty into clinical workflows to prompt real-time low-certainty alerts for improving PE diagnostic reporting quality and clarity, which will inform better treatment decisions for ED patients with suspected PE.

PE成为心血管相关死亡的第三个主要原因，超过500,000例PE 每年在美国（美国）发生，导致大约200,000人死亡和住院超过25万名患者。快速准确的PE诊断对于确保最高的PE至关重要护理质量。在美国1.2亿急诊科（ED）患者中，每年都有1-2％ PE的计算机断层扫描肺血管造影（CTPA）。参考医师在很大程度上依赖CTPA 报告诊断或排除PES。放射学报告的清晰度是最关键的品质之一，也是美国放射学院强调了在放射学报告中需要精确交流。然而，在放射学报告中有效地沟通不确定性是挑战。参考医生可能解释放射科医生的文本表达方式，这些文字表达方式与预期的方式不同。差距放射性人的预期信息与转诊医师的解释之间不可能完全通过结构化报告或标准化词典解决。 CTPA报告中的不必要的对冲语言报告的报告歧义可能更糟，并可能导致患者的不适当治疗。因此，我们旨在为上下文感知（UN）确定性评估（深度确定性）开发一种深度学习的方法，它可以很好地磨碎的不确定性测量和标准化，促进一致，准确的诊断确定性 CTPA报告中的沟通，从而改善PE护理。这项研究将为未来奠定基础将深层确定性的实施和集成到临床工作流程中，以提示实时低确定性警报为了改善PE诊断报告质量和清晰度，这将为ED提供更好的治疗决策怀疑PE的患者。