Improving cardiovascular image-based phenotyping using emerging methods in artificial intelligence

使用人工智能新兴方法改善基于心血管图像的表型分析

• 批准号: 10379426
• 负责人: Rima Arnaout
• 金额: $ 80.89万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10379426
• 关键词: Abdomen; Address; Adult; Age; Aging; Apical; Artificial Intelligence; Biometry; Birth; Cardiac; Cardiovascular Diseases; Cardiovascular system; Clinical; Clinical Research; Collection; Communities; Complex; Congenital Abnormality; Data; Data Science; Data Set; Detection; Diagnosis; Diagnostic; Early Diagnosis; Early treatment; Echocardiography; Environment; Evaluation; Face; Fetal Heart; Goals; Heart; Heart Abnormalities; Human; Image; Image Analysis; Informatics; Label; Lead; Lesion; Life; Literature; Machine Learning; Measurement; Medical Imaging; Methods; Modeling; Morbidity - disease rate; Outcome; Patients; Pattern; Pattern Recognition; Performance; Phenotype; Physicians; Population; Pregnant Women; Provider; Psyche structure; Quality Control; Rare Diseases; Reproducibility; Research; Secure; Structure; Supervision; Surveys; Techniques; Testing; Time; Trachea; Training; Translating; Ultrasonography; Variant; Work; base; cardiovascular imaging; clinical center; clinical decision-making; clinical imaging; clinically relevant; comorbidity; computerized data processing; congenital heart disorder; cost; data curation; data harmonization; deep learning; deep learning algorithm; deep learning model; design; detection test; diagnostic accuracy; disease diagnosis; fetal; fetal diagnosis; heart imaging; image guided; image processing; imaging study; improved; insight; learning network; machine learning algorithm; machine learning method; model design; mortality; multidisciplinary; multimodality; neural network; neural network architecture; novel; patient population; precision medicine; prenatal; prevent; programs; repaired; screening; standard of care; statistics; theories; tool; ultrasound

Summary / Abstract Objective — The goal of this proposal is to develop and optimize novel deep learning (DL) assisted approaches to improve diagnosis and clinical decision-making for congenital heart disease (CHD). This will be achieved by using DL, machine learning (ML), and related methods to extract diagnosis, biometric characterizations, and other information from fetal ultrasound imaging. Notably, this work includes a clinical translational evaluation of these methods in a population-wide imaging collection spanning two decades, tens of thousands of patients, and several clinical centers. Background — Despite clear and numerous benefits to prenatal detection of CHD and an ability for fetal ultrasound to detect over 90% of CHD lesions in theory, in practice the fetal CHD detection rate is closer to 50%. Prior literature suggests a key cause of this startling diagnosis gap is suboptimal acquisition and interpretation of fetal heart images. DL is a novel data science technique that is proving excellent at pattern recognition in images. DL models are a function of the design and tuning of a neural network architecture, and the curation and processing of the image data used to train the network. Preliminary Studies — We have assembled a multidisciplinary team of experts in echocardiography and CHD (Drs. Grady, Levine, and Arnaout), DL and data science (Drs. Keiser, Butte and Arnaout), and statistics and clinical research (Drs. Arnaout and Grady) and secured access to tens of thousands of multicenter (UCSF and six other centers), multimodal fetal imaging studies. We have created a scalable image processing pipeline to transform clinical studies into image data ready for computing. We have designed and trained DL models to find key cardiac views in fetal ultrasound, calculate standard and advanced fetal cardiac biometrics from those views, and distinguish between normal hearts and certain CHD lesions. Hypothesis — While DL is powerful, much work is still needed to adapt it for clinical imaging and to translate it toward clinically relevant performance in patient populations. We hypothesize that an integrated ensemble DL/ML approach can lead to vast improvements in fetal CHD diagnosis. Aims — To this end, the main Aims of this proposal are (1) to develop and optimize neural network architectures and efficient data inputs to relieve key performance bottlenecks for DL in fetal CHD; and (2) to deploy DL models population-wide to evaluate their ability to improve diagnosis, biometric characterization, and precision phenotyping over the current standard of care. Our methods include DL/ML algorithms and retrospective imaging analysis. Environment and Impact — This work will be supported in an outstanding environment for research at the crossroads of data science, cardiovascular and fetal imaging, and translational informatics. The work proposed will provide valuable tools and insight into designing and evaluating both the data and the algorithms for DL on imaging for clinically relevant goals, and will lay important groundwork for DL-assisted phenotyping for both clinical use and precision medicine research.

摘要/摘要