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.

Public Health Relevance

Medical imaging is critical to almost every type of diagnostic and management decision, but human interpretation of medical images can lack accuracy and reproducibility. By developing machine learning methods for analyzing medical images, the work in our proposal can improve diagnostic accuracy in medical imaging, for both clinical and research uses.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Clinical Translational Imaging Science Study Section (CTIS)
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Li, Huiqing
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University of California San Francisco
Internal Medicine/Medicine
Schools of Medicine
San Francisco
United States
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