The fundamental problem with ultrasound-guided diagnosis is that the acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details. This limitation leads to challenges in ultrasound echocardiography, such as poor visualization of heart valves or foreshortening of ventricles. Clinicians must interpret these images with inherent uncertainty, a nuance absent in machine learning's one-hot labels. We propose Re-Training for Uncertainty (RT4U), a data-centric method to introduce uncertainty to weakly informative inputs in the training set. This simple approach can be incorporated to existing state-of-the-art aortic stenosis classification methods to further improve their accuracy. When combined with conformal prediction techniques, RT4U can yield adaptively sized prediction sets which are guaranteed to contain the ground truth class to a high accuracy. We validate the effectiveness of RT4U on three diverse datasets: a public (TMED-2) and a private AS dataset, along with a CIFAR-10-derived toy dataset. Results show improvement on all the datasets.

Cardiac auscultation, an integral tool in diagnosing cardiovascular diseases (CVDs), often relies on the subjective interpretation of clinicians, presenting a limitation in consistency and accuracy. Addressing this, we introduce the BUET Multi-disease Heart Sound (BMD-HS) dataset - a comprehensive and meticulously curated collection of heart sound recordings. This dataset, encompassing 864 recordings across five distinct classes of common heart sounds, represents a broad spectrum of valvular heart diseases, with a focus on diagnostically challenging cases. The standout feature of the BMD-HS dataset is its innovative multi-label annotation system, which captures a diverse range of diseases and unique disease states. This system significantly enhances the dataset's utility for developing advanced machine learning models in automated heart sound classification and diagnosis. By bridging the gap between traditional auscultation practices and contemporary data-driven diagnostic methods, the BMD-HS dataset is poised to revolutionize CVD diagnosis and management, providing an invaluable resource for the advancement of cardiac health research. The dataset is publicly available at this link: https://github.com/mHealthBuet/BMD-HS-Dataset.

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a new deep learning framework for automatic interpretation for structural heart diseases like AS. When deployed, SMMIL can combine information from two input modalities, spectral Dopplers and 2D cineloops, to produce a study-level AS diagnosis. During training, SMMIL can combine a smaller labeled set and an abundant unlabeled set of both modalities to improve its classifier. Experiments demonstrate that SMMIL outperforms recent alternatives at 3-level AS severity classification as well as several clinically relevant AS detection tasks.

MIT engineers are hoping to help doctors tailor treatments to patients' specific heart form and function, with a custom robotic heart. The team has developed a procedure to 3D print a soft and flexible replica of a patient's heart. No two hearts beat alike. The size and shape of the the heart can vary from one person to the next. These differences can be particularly pronounced for people living with heart disease, as their hearts and major vessels work harder to overcome any compromised function.

Barcelona, Spain – 28 Aug 2022: A novel artificial intelligence (AI) algorithm uses routine echocardiograms to identify aortic stenosis patients at high risk of death who could benefit from treatment. The late breaking research is presented in a Hot Line session today at ESC Congress 2022.1 Aortic stenosis is the most common primary valve lesion requiring surgery or transcatheter intervention in Europe and North America.2 Prevalence is rapidly increasing due to ageing populations. Guidelines strongly advise early intervention in all symptomatic patients with severe aortic stenosis due to the dismal prognosis. Approximately 50% of untreated patients with aortic stenosis die in the first two years after symptoms appear.3

We used 10433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training (8327 images from 4512 patients, mean age 65 [SD] 15 years), validation (1041 images from 563 patients, mean age 65 14 years), and test (1065 images from 563 patients, mean age 65 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an AUC, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% CI 0.77–0.88),

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos.

Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. Therefore, researchers of the Mayo Clinic, USA, developed an AI-ECG using a convolutional neural network to identify patients with moderate to severe AS. It was a retrospective study in which researchers identified 258 607 adults [mean age 63 16.3 years; women 122 790 (48%)] with echocardiography and an ECG performed within 180 days using the Mayo Clinic Unified Data Platform (UDP). The researchers tested the use of an AI-ECG to help identify patients with moderate to severe aortic stenosis (AS). Using echocardiography data, the researchers identified moderate to severe AS in 9723 (3.7%) patients. They performed Artificial intelligence training in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) in randomly selected subjects.

Screening for significant aortic stenosis was fast and effective through the assessment of phonocardiograms by a digital stethoscope and machine learning, according to results presented at the American Society of Echocardiography Scientific Sessions. "A machine-learning algorithm trained on heart sounds can rapidly and accurately detect a murmur in patients with clinically significant aortic stenosis," Steve Pham, MD, vice president of clinical and research affairs at Eko Devices, told Cardiology Today. "Front-line clinicians may be able to use Eko stethoscopes (Eko CORE) with this algorithm to refer patients for echocardiography to confirm aortic stenosis." Brent E. White, MD, of the Bluhm Cardiovascular Institute at Northwestern Memorial Hospital in Chicago, and colleagues analyzed 639 recordings from 161 patients who were undergoing transthoracic echocardiography. The 15-second phonocardiogram recordings were obtained from the digital stethoscope, which is wirelessly paired with a mobile app (Eko Mobile).

The goal is for the technology to quickly scan medical images and prioritize abnormal results, allowing doctors to spend their time on the more difficult cases. The machines would also provide a check on human error. Companies are jumping on board. IBM Watson Health, which acquired enterprise imaging software company Merge Healthcare in 2015, plans to put its Watson supercomputer to work analyzing medical images. One of its projects, presented at the Radiological Society of North America's annual conference, focuses on research around aortic stenosis, a heart condition that occurs when the aortic valve narrows.