Assessment of left ventricular ejection fraction in artificial intelligence based on left ventricular opacification.

Background: Left ventricular opacification (LVO) improves the accuracy of left ventricular ejection fraction (LVEF) by enhancing the visualization of the endocardium. Manual delineation of the endocardium by sonographers has observer variability. Artificial intelligence (AI) has the potential to improve the reproducibility of LVO to assess LVEF. Objectives: The aim was to develop an AI model and evaluate the feasibility and reproducibility of LVO in the assessment of LVEF. Methods: This retrospective study included 1305 echocardiography of 797 patients who had LVO at the Department of Ultrasound Medicine, Union Hospital, Huazhong University of Science and Technology from 2013 to 2021. The AI model was developed by 5-fold cross validation. The validation datasets included 50 patients prospectively collected in our center and 42 patients retrospectively collected in the external institution. To evaluate the differences between LV function determined by AI and sonographers, the median absolute error (MAE), spearman correlation coefficient, and intraclass correlation coefficient (ICC) were calculated. Results: In LVO, the MAE of LVEF between AI and manual measurements was 2.6% in the development cohort, 2.5% in the internal validation cohort, and 2.7% in the external validation cohort. Compared with two-dimensional echocardiography (2DE), the left ventricular (LV) volumes and LVEF of LVO measured by AI correlated significantly with manual measurements. AI model provided excellent reliability for the LV parameters of LVO (ICC > 0.95). Conclusions: AI-assisted LVO enables more accurate identification of the LV endocardium and reduces observer variability, providing a more reliable way for assessing LV function.

Accumulation and Quantitative Assessment of Terrigenous Organic Matter in Upper Oligocene to Lower Miocene Marine Source Rocks of the Qiongdongnan Basin.

The intense collision between marine and terrestrial agents results in the dual-source (marine and terrigenous) characteristics of marine source rocks. Our research quantitatively assessed terrestrial organic matter and revealed the crucial role of terrestrial organic materials in the organic matter enrichment of lower Miocene to upper Oligocene marine source rocks in the Qiongdognnan Basin. The quantitative assessment was achieved using partial least-squares analysis with eight biomarker parameters associated with n-alkanes, isoprenoids, bicadinanes, taraxerane, tricyclic terpanes, and gammacerane. Differential unloading of terrestrial organic materials based on sedimentary facies of the delta-marginal sea system were observed through oleanane and bicadinane contents. It should be noted that the diagnostic ratio of oleanane was excluded from the quantitative analysis due to the dual influence from differential unloading and contact with seawater of the terrestrial organic materials. Calculation results show that the terrestrial organic matter was highest in the delta front at 70%, followed by prodelta at 59% and inner shallow marine at 57%. From the late Oligocene to the early Miocene, the proportion of terrestrial organic matter in marine source rocks continuously increased, with the highest average value observed in the second member of the Sanya Formation at 69% and the lowest occurring in the third member of the Lingshui Formation at 46%. Increasing terrestrial organic material inputs and preservation driven by the East Asian summer monsoon provided first-order control of the accumulation of organic carbon in the Qiongdongnan Basin during late Oligocene to early Miocene, rather than the bioproductivity of marine algae. The redox conditions of the water column determine the enrichment extent of organic matter.

Human reliability assessment of intelligent coal mine hoist system based on Bayesian network.

The human reliability of intelligent coal mine hoist operation system is affected by many factors, in order to reduce the occurrence of human error in the hoist system and improve the reliability of the system. The characteristics of phased-mission task operation of hoists is combined, the phase dependence of human cognitive errors is considered and, a new human reliability evaluation method is proposed with the help of Bayesian network (BN) model in this paper. Firstly, the phase dependence of human cognitive errors was analyzed based on the cognitive behavior model. Then the human error analysis in the hoist system was carried out, and several main performance shaping factors are selected. Secondly, BN was used to build the human reliability model of the hoist system at each stage. Finally, it is found that the phase dependence of cognitive errors has a negative impact on the human reliability of the hoist system through the case analysis. At the same time, several main performance shaping factors (PSFs)were quantitatively analyzed by using the reverse reasoning ability of BN, which proves the effectiveness of the proposed method, and provides a scientific and reasonable theoretical basis for the development of effective human error prevention measures for the operation of intelligent coal mine hoists.

Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment.

The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer's experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.

Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information.

The outbreak of COVID-19 around the world has caused great pressure to the health care system, and many efforts have been devoted to artificial intelligence (AI)-based analysis of CT and chest X-ray images to help alleviate the shortage of radiologists and improve the diagnosis efficiency. However, only a few works focus on AI-based lung ultrasound (LUS) analysis in spite of its significant role in COVID-19. In this work, we aim to propose a novel method for severity assessment of COVID-19 patients from LUS and clinical information. Great challenges exist regarding the heterogeneous data, multi-modality information, and highly nonlinear mapping. To overcome these challenges, we first propose a dual-level supervised multiple instance learning module (DSA-MIL) to effectively combine the zone-level representations into patient-level representations. Then a novel modality alignment contrastive learning module (MA-CLR) is presented to combine representations of the two modalities, LUS and clinical information, by matching the two spaces while keeping the discriminative features. To train the nonlinear mapping, a staged representation transfer (SRT) strategy is introduced to maximumly leverage the semantic and discriminative information from the training data. We trained the model with LUS data of 233 patients, and validated it with 80 patients. Our method can effectively combine the two modalities and achieve accuracy of 75.0% for 4-level patient severity assessment, and 87.5% for the binary severe/non-severe identification. Besides, our method also provides interpretation of the severity assessment by grading each of the lung zone (with accuracy of 85.28%) and identifying the pathological patterns of each lung zone. Our method has a great potential in real clinical practice for COVID-19 patients, especially for pregnant women and children, in aspects of progress monitoring, prognosis stratification, and patient management.

Rapid and Large-Scale Quality Assessment of Two-Dimensional MoS2 Using Sulfur Particles with Optical Visualization.

The efficient nondestructive assessment of quality and homogeneity for two-dimensional (2D) MoS2 is critically important to advance their practical applications. Here, we presented a rapid and large-area assessment method for visually evaluating the quality and uniformity of chemical vapor deposition (CVD)-grown MoS2 monolayers simply with conventional optical microscopes. This was achieved through one-pot adsorbing abundant sulfur particles selectively onto as-grown poorer-quality MoS2 monolayers in a CVD system without any additional treatment. We further revealed that this favorable adsorption of sulfur particles on MoS2 originated from their intrinsic higher-density sulfur vacancies. Based on unadsorbed MoS2 monolayers, superior performance field effect transistors with a mobility of ∼49 cm2 V-1 s-1 were constructed. Importantly, the assessment approach was noninvasive due to the all-vapor-phase and moderate adsorption-desorption process. Our work offers a new route for the performance and yield optimization of devices by quality assessment of 2D semiconductors prior to device fabrication.

Anatomical Feature-Based Lung Ultrasound Image Quality Assessment Using Deep Convolutional Neural Network.

Lung ultrasound (LUS) has been used for point-of-care diagnosis of respiratory diseases including COVID-19, with advantages such as low cost, safety, absence of radiation, and portability. The scanning procedure and assessment of LUS are highly operator-dependent, and the appearance of LUS images varies with the probe's position, orientation, and contact force. Karamalis et al. introduced the concept of ultrasound confidence maps based on random walks to assess the ultrasound image quality algorithmically by estimating the per-pixel confidence in the image data. However, these confidence maps do not consider the clinical context of an image, such as anatomical feature visibility and diagnosability. This work proposes a deep convolutional network that detects important anatomical features in an LUS image to quantify its clinical context. This work introduces an Anatomical Feature-based Confidence (AFC) Map, quantifying an LUS image's clinical context based on the visible anatomical features. We developed two U-net models, each segmenting one of the two classes crucial for analyzing an LUS image, namely 1) Bright Features: Pleural and Rib Lines and 2) Dark Features: Rib Shadows. Each model takes the LUS image as input and outputs the segmented regions with confidence values for the corresponding class. The evaluation dataset consists of ultrasound images extracted from videos of two sub-regions of the chest above the anterior axial line from three human subjects. The feature segmentation models achieved an average Dice score of 0.72 on the model's output for the testing data. The average of non-zero confidence values in all the pixels was calculated and compared against the image quality scores. The confidence values were different between different image quality scores. The results demonstrated the relevance of using an AFC Map to quantify the clinical context of an LUS image.