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Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X-ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF.

The study's goal was to demonstrate that the AI interpretation of chest X-rays can assist the clinician in diagnosing HF.

Patients older than 45 years were included in the study. The study analyzed 10 100 deidentified outpatient chest X-rays by AI algorithm. The AI-generated report was later verified by an independent radiologist. Patients with CTR &gt; 0.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF.

Out of 10 100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X-rays. One hundred and six out of 183 underwent diagnostic tests. Eighty-two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction.

HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X-ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction.

large_The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).webp

medium_The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).webp

small_The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).webp

thumbnail_The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).webp

The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).webp

The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF)

A rapid and accurate diagnosis of heart failure(HF) is of utmost importance to decrease the mortality and to retrench the medical expenditure. Our project aims to identify potential HF patients by detecting cardiomegaly and pleural effusion in their chest X-rays by the help of Artificial Intelligence(AI) scanning&nbsp;

We scanned the anonymous chest X-rays of the patients who were at least 45 years old and have applied to any department of the hospital (except cardiology, cardiovascular surgery and emergency department) by the help of AI. AI detected patients who have both cardiomegaly and pleural effusion in their X-Rays and they have been invited to our cardiology clinic for further, definitive HF diagnostic tests.&nbsp;

5623 subjects were scanned and 119 of them(2.1%) had cardiomegaly and pleural effusion together. We reached 57 of 119 patients. We diagnosed HF in 49 of 57 patients (86%) according to the 2021 ESC HF guidelines. The mean values for left ventricular EF was 42±13 %, NT-proBNP&nbsp;levels were median 4218&nbsp;pg/ml (Q1: 1947pg/ml-Q3:10674pg/ml) and the mean age of HF patients was 70±10 years.&nbsp;&nbsp;

ART-IN-HF project is the first initiative that had used chest X-ray scanning for the early diagnosis of HF by detecting both cardiomegaly and pleural effusion in chest X-rays facilitated by AI. Most of the patients who had both cardiomegaly and pleural effusion were diagnosed with HF. AI might be useful for detecting HF using chest X-ray scanning in undiagnosed HF patients.&nbsp;

large_The Early diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF) The First Outcomes.webp

medium_The Early diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF) The First Outcomes.webp

small_The Early diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF) The First Outcomes.webp

thumbnail_The Early diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF) The First Outcomes.webp

The Early diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF) The First Outcomes.webp

The Early diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF): The First Outcomes

Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response.

The Swedish Heart Failure Registry is a nationwide registry collecting detailed demographic, clinical, laboratory, and medication data and linked to databases with outcome information. We applied random forest modeling to identify predictors of 1‐year survival. Cluster analysis was performed and validated using serial bootstrapping. Association between clusters and survival was assessed with Cox proportional hazards modeling and interaction testing was performed to assess for heterogeneity in response to HF pharmacotherapy across propensity‐matched clusters. Our study included 44 886 HF patients enrolled in the Swedish Heart Failure Registry between 2000 and 2012. Random forest modeling demonstrated excellent calibration and discrimination for survival (C‐statistic=0.83) whereas left ventricular ejection fraction did not (C‐statistic=0.52): there were no meaningful differences per strata of left ventricular ejection fraction (1‐year survival: 80%, 81%, 83%, and 84%). Cluster analysis using the 8 highest predictive variables identified 4 clinically relevant subgroups of HF with marked differences in 1‐year survival. There were significant interactions between propensity‐matched clusters (across age, sex, and left ventricular ejection fraction and the following medications: diuretics, angiotensin‐converting enzyme inhibitors, β‐blockers, and nitrates, P &lt; 0.001, all).

Machine learning algorithms accurately predicted outcomes in a large data set of HF patients. Cluster analysis identified 4 distinct phenotypes that differed significantly in outcomes and in response to therapeutics. Use of these novel analytic approaches has the potential to enhance effectiveness of current therapies and transform future HF clinical trials.

large_Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.webp

medium_Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.webp

small_Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.webp

thumbnail_Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.webp

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.webp

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

The number of heart transplants performed is limited by organ availability and is managed by the United Network for Organ Sharing (UNOS). Efforts are underway to make organ disbursement more equitable as demand increases.

Significant variation exists in contemporary patterns of care, wait times, and outcomes among patients undergoing heart transplantation across UNOS regions.

We identified adult patients undergoing first, single‐organ heart transplantation between January 2006 and December 2014 in the UNOS dataset and compared sociodemographic and clinical profiles, wait times, use of mechanical circulatory support (MCS), status at time of transplantation, and 1‐year survival across UNOS regions.

We analyzed 17 096 patients undergoing heart transplantation. There were no differences in age, sex, renal function, and peripheral vascular resistance across regions; however, there was 3‐fold variation in median wait time (range, 48–166 days) across UNOS regions. Proportion of patients undergoing transplantation with status 1A ranged from 36% to 79% across regions (P &lt; 0.01), and percentage of patients hospitalized at time of transplantation varied from 41% to 98%. There was also marked variation in MCS and inotrope utilization (28%–57% and 25%–58%, respectively; P &lt; 0.001). Durable ventricular assist device implantation varied from 20% to 44% (P &lt; 0.001), and intra‐aortic balloon pump utilization ranged from 4% to 18%.

Marked differences exist in patterns of care across UNOS regions that generally trend with differences in waitlist time. Novel policy initiatives are required to address disparities in access to allografts and ensure equitable and efficient allocation of organs.

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thumbnail_Variation in practice patterns and outcomes across United Network for Organ Sharing allocation regions.webp

Variation in practice patterns and outcomes across United Network for Organ Sharing allocation regions.webp

Variation in practice patterns and outcomes across United Network for Organ Sharing allocation regions

In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR images in End Diastole and End Systole phase. We show that both our segmentation models achieve near state-of-the-art performance scores in terms of distance metrics and have convincing accuracy in terms of clinical parameters. A comparative analysis is provided by introducing a novel dice loss function and its combination with cross entropy loss. By exploring different network structures and comprehensive experiments, we discuss several key insights to obtain optimal model performance, which also is central to the theme of this challenge.

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thumbnail_2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation.webp

2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation.webp

2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation

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