Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning

Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and an external validation set show that our approach yields higher accuracy while reducing model size.Comment: multiple-instance learning; self-supervised learning; semi-supervised learning; medical imagin

Short-Term Effects of Ketamine and Isoflurane on Left Ventricular Ejection Fraction in an Experimental Swine Model

Background. General anesthesia is an essential element of experimental medical procedures. Ketamine and isoflurane are agents commonly used to induce and maintain anesthesia in animals. The cardiovascular effects of these anesthetic agents are diverse, and the response of global myocardial function is unknown. Methods. In a series of 15 swine, echocardiography measurements of left ventricular ejection fraction (LVEF) were obtained before the animals received anesthesia (baseline), after an intramuscular injection of ketamine (postketamine) and after inhaled isoflurane (postisoflurane). Results. The mean LVEF of an unanesthetized swine was 47 ± 3%. There was a significant decrease in the mean LVEF after administration of ketamine to 41 + 6.5% (P = 0.003). The addition of inhaled isoflurane did not result in further decrease in mean LVEF (mean LVEF 38 ± 7.2%, P = 0.22). Eight of the swine had an increase in their LVEF with sympathetic stimulation. Conclusions. In our experimental model the administration of ketamine was associated with decreased LV function. The decrease may be largely secondary to a blunting of sympathetic tone. The addition of isoflurane to ketamine did not significantly change LV function. A significant number of animals had returned to preanesthesia LV function with sympathetic stimulation

Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis

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.Comment: Echocardiography; Multimodal; Semi-supervised Learning; Multiple-Instance Learnin

Transesophageal echocardiography in cryptogenic stroke and patent foramen ovale: analysis of putative high-risk features from the risk of paradoxical embolism database

BACKGROUND Patent foramen ovale (PFO) is associated with cryptogenic stroke (CS), although the pathogenicity of a discovered PFO in the setting of CS is typically unclear. Transesophageal echocardiography features such as PFO size, associated hypermobile septum, and presence of a right-to-left shunt at rest have all been proposed as markers of risk. The association of these transesophageal echocardiography features with other markers of pathogenicity has not been examined. METHODS AND RESULTS We used a recently derived score based on clinical and neuroimaging features to stratify patients with PFO and CS by the probability that their stroke is PFO-attributable. We examined whether high-risk transesophageal echocardiography features are seen more frequently in patients more likely to have had a PFO-attributable stroke (n=637) compared with those less likely to have a PFO-attributable stroke (n=657). Large physiologic shunt size was not more frequently seen among those with probable PFO-attributable strokes (odds ratio [OR], 0.92; P=0.53). The presence of neither a hypermobile septum nor a right-to-left shunt at rest was detected more often in those with a probable PFO-attributable stroke (OR, 0.80; P=0.45; OR, 1.15; P=0.11, respectively). CONCLUSIONS We found no evidence that the proposed transesophageal echocardiography risk markers of large PFO size, hypermobile septum, and presence of right-to-left shunt at rest are associated with clinical features suggesting that a CS is PFO-attributable. Additional tools to describe PFOs may be useful in helping to determine whether an observed PFO is incidental or pathogenically related to CS

Inhibitors of Helicobacter pylori Protease HtrA Found by ‘Virtual Ligand’ Screening Combat Bacterial Invasion of Epithelia

Background: The human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention. Methodology/Principal Findings: We present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector (‘virtual ligand’) for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium. Conclusions/Significance: This study demonstrates that receptor-based virtual screening with a permissive (‘fuzzy’) pharmacophore model can help identify small bioactive agents for combating bacterial infection

What is Quality End-of-Life Care for Patients With Heart Failure? A Qualitative Study With Physicians

Background Advanced heart failure (AHF) carries a morbidity and mortality that are similar or worse than many advanced cancers. Despite this, there are no accepted quality metrics for end-of-life (EOL) care for patients with AHF. Methods and Results As a first step toward identifying quality measures, we performed a qualitative study with 23 physicians who care for patients with AHF. Individual, in-depth, semistructured interviews explored physicians\u27 perceptions of characteristics of high-quality EOL care and the barriers encountered. Interviews were analyzed using software-assisted line-by-line coding in order to identify emergent themes. Although some elements and barriers of high-quality EOL care for AHF were similar to those described for other diseases, we identified several unique features. We found a competing desire to avoid overly aggressive care at EOL alongside a need to ensure that life-prolonging interventions were exhausted. We also identified several barriers related to identifying EOL including greater prognostic uncertainty, inadequate recognition of AHF as a terminal disease and dependence of symptom control on disease-modifying therapies. Conclusions Our findings support quality metrics that prioritize receipt of goal-concordant care over utilization measures as well as a need for more inclusive payment models that appropriately reflect the dual nature of many AHF therapies