Automatic segmentation of 2D echocardiography ultrasound images by means of generative adversarial network.

Automated cardiac segmentation from two-dimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation. To accomplish this, we integrate the backbone PatchGAN model for the discriminator and the UNET for the generator, for building the Pix2Pix GAN. The resulting model produces precisely segmented images, thanks to UNET's capability for precise segmentation and PatchGAN's capability for fine-grained discrimination. For the experimental validation, we use the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, which consists of echocardiographic images from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at the end-diastolic (ED) and end-systolic (ES) phases. Similarly to state-of-the-art studies on the same dataset, we followed the same train-test splits. Our results demonstrate that the proposed GAN-based technique improves segmentation performance for clinical and geometrical parameters compared to the state-of-the-art methods. More precisely, throughout the ED and ES phases, the mean Dice values for the left ventricular endocardium reached 0.961 and 0.930 for 2CH, and 0.959 and 0.950 for 4CH, respectively. Furthermore, the average ejection fraction correlation and Mean Absolute Error obtained were 0.95 and 3.2ml for 2CH, and 0.98 and 2.1ml for 4CH, outperforming the state-of-the-art results.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level.

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Human-to-AI Interrater Agreement for Lung Ultrasound Scoring in COVID-19 Patients.

OBJECTIVES: Lung ultrasound (LUS) has sparked significant interest during COVID-19. LUS is based on the detection and analysis of imaging patterns. Vertical artifacts and consolidations are some of the recognized patterns in COVID-19. However, the interrater reliability (IRR) of these findings has not been yet thoroughly investigated. The goal of this study is to assess IRR in LUS COVID-19 data and determine how many LUS videos and operators are required to obtain a reliable result. METHODS: A total of 1035 LUS videos from 59 COVID-19 patients were included. Videos were randomly selected from a dataset of 1807 videos and scored by six human operators (HOs). The videos were also analyzed by artificial intelligence (AI) algorithms. Fleiss' kappa coefficient results are presented, evaluated at both the video and prognostic levels. RESULTS: Findings show a stable agreement when evaluating a minimum of 500 videos. The statistical analysis illustrates that, at a video level, a Fleiss' kappa coefficient of 0.464 (95% confidence interval [CI] = 0.455-0.473) and 0.404 (95% CI = 0.396-0.412) is obtained for pairs of HOs and for AI versus HOs, respectively. At prognostic level, a Fleiss' kappa coefficient of 0.505 (95% CI = 0.448-0.562) and 0.506 (95% CI = 0.458-0.555) is obtained for pairs of HOs and for AI versus HOs, respectively. CONCLUSIONS: To examine IRR and obtain a reliable evaluation, a minimum of 500 videos are recommended. Moreover, the employed AI algorithms achieve results that are comparable with HOs. This research further provides a methodology that can be useful to benchmark future LUS studies.

Congenital constriction ring of limbs in subjects with history of maternal substance use.

Congenital Constriction Ring (CCR) is a rare malformation which manifests itself in the form of ring-like constrictive bands. Due to its heterogeneous nature, its etiology remains unclear. Here, we present a series of seven independent individuals afflicted with CCR, which primarily involved the digits. The phenotypic manifestations included terminal phalangeal reduction, anonychia, digit hypoplasia, and acrosyndactyly. Mesoaxial digits in hands and preaxial digits in feet were most frequently affected. Camptodactyly and clubfoot were witnessed in four and one subject, respectively. Curiously, mothers of six of these subjects revealed that they consumed copious amounts of Multani mitti(Fuller's clay) and/or Naswar(nonsmoke-tobacco), during their respective pregnancies. Maternal substance use during pregnancy is not an unusual practice, however, its relationship with CCR as pregnancy outcome remains unexplored. Case-control studies are warranted to elucidate the relationship between the exposure to these substances and the etiology of CCR and/or other limb defects in the offspring.

Chemical exposure in occupational settings and related health risks: a neglected area of research in Pakistan.

In Pakistan a huge number of workers is routinely exposed to various types of chemical contaminants but there is a dearth of information as to the impact of these agents, due to a lack of a routine surveillance system and proper reporting. Prolonged and sometimes acute occupational exposures to varied organic chemicals may result in numerous health related problems. Studies from all over the world have shown adverse health outcomes of chemicals that are commonly used in various occupations. Such chemical exposures are not just confined to the workplace, but the residents surrounding industrial sites also face significant health risks due to indirect chemical exposure. Occupational exposure is a multidimensional risk factor that varies from one occupation to another, and is associated with health decline in workers. Common determinants of workplace hazards include improper, or lack of use of self-protective equipment, active and passive exposure to cigarette smoke as well as the socio-demographic and economic background of workers. There may be more than one cause of occupational stress and psychophysical disturbance among workers such as workload, lower salaries, and lack of social and medical facilities; indeed, their general health is poor. Therefore, in Pakistan, it is particularly important to focus on these issues and set rules and regulations to create occupational hazard awareness among workers, which will promote health safety at work places. If priorities are given to the correct use of self-protective equipment, adopting proper hygiene at the workplace and to avoid smoking, occupational exposures and consequent health risks may be minimized significantly.