CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases

Drug-eluting stents appear superior to bare metal stents for vein-graft PCI in vessels up to a stent diameter of 4 mm.

BACKGROUND: Research trials have shown improved short-term outcome with drug-eluting stents (DES) over bare metal stents (BMS) in saphenous vein graft (SVG) percutaneous coronary intervention (PCI), primarily by reducing target vessel revascularization (TVR) for in-stent restenosis. We compared the outcomes in patients undergoing SVG stent implantation treated with DES or BMS. In exploratory analyses we investigated the influence of stent generation and diameter. METHODS: Data were obtained from a prospective database of 657 patients who underwent PCI for SVG lesions between 2003 and 2011. A total of 344 patients had PCI with BMS and 313 with DES. Propensity scores were developed based on 15 observed baseline covariates in a logistic regression model with stent type as the dependent variable. The nearest-neighbour-matching algorithm with Greedy 5-1 Digit Matching was used to produce two patient cohorts of 313 patients each. We assessed major adverse cardiac events (MACE) out to a median of 3.3 years (interquartile range: 2.1-4.1). MACE was defined as all-cause mortality, myocardial infarction (MI), TVR and stroke. RESULTS: There was a significant difference in MACE between the two groups in favour of DES (17.9% DES vs. 31.2% BMS group; p = 0.0017) over the 5-year follow-up period. MACE was driven by increased TVR in the BMS group. There was no difference in death, MI or stroke. Adjusted Cox analysis confirmed a decreased risk of MACE for DES compared with BMS 0.75 (95% confidence interval (CI) 0.52-0.94), with no difference in the hazard of all-cause mortality (hazard ratio: 1.08; 95% CI: 0.77-1.68). However, when looking at stent diameters greater than 4 mm, no difference was seen in MACE rates between BMS and DES. CONCLUSIONS: Overall in our cohort of patients who had PCI for SVG disease, DES use resulted in lower MACE rates compared with BMS over a 5-year follow-up period; however, for stent diameters over 4 mm no difference in MACE rates was seen

Datafish Multiphase Data Mining Technique to Match Multiple Mutually Inclusive Independent Variables in Large PACS Databases

Retrospective data mining has tremendous potential in research but is time and labor intensive. Current data mining software contains many advanced search features but is limited in its ability to identify patients who meet multiple complex independent search criteria. Simple keyword and Boolean search techniques are ineffective when more complex searches are required, or when a search for multiple mutually inclusive variables becomes important. This is particularly true when trying to identify patients with a set of specific radiologic findings or proximity in time across multiple different imaging modalities. Another challenge that arises in retrospective data mining is that much variation still exists in how image findings are described in radiology reports. We present an algorithmic approach to solve this problem and describe a specific use case scenario in which we applied our technique to a real-world data set in order to identify patients who matched several independent variables in our institution\u27s picture archiving and communication systems (PACS) database

Sonographic Diagnosis of Velamentous and Marginal Placental Cord Insertion

Routine second trimester ultrasound (US) examinations include an assessment of the umbilical cord given its vital role as a vascular conduit between the maternal placenta and fetus during fetal development. Placental cord insertion abnormalities can be identified during prenatal US screening and are increasingly recognized as independent risk factors for various complications during pregnancy and delivery. The purpose of this pictorial review is to illustrate examples of velamentous and marginal placental cord insertion with an emphasis on how to differentiate their morphology using color Doppler US

Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions

In recent years, there has been enormous interest in applying artificial intelligence (AI) to radiology. Although some of this interest may have been driven by exaggerated expectations that the technology can outperform radiologists in some tasks, there is a growing body of evidence that illustrates its limitations in medical imaging. The true potential of the technique probably lies somewhere in the middle, and AI will ultimately play a key role in medical imaging in the future. The limitless power of computers makes AI an ideal candidate to provide the standardization, consistency, and dependability needed to support radiologists in their mission to provide excellent patient care. However, important roadblocks currently limit the expansion of this field in medical imaging. This article reviews some of the challenges and potential solutions to advance the field forward, with focus on the experience gained by hosting image-based competitions

Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia

This dataset is intended to be used for machine learning and is composed of annotations with bounding boxes for pulmonary opacity on chest radiographs which may represent pneumonia in the appropriate clinical setting

The RSNA Pediatric Bone Age Machine Learning Challenge.

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue