FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform.

The emergence of federated learning (FL) technique in fog-enabled healthcare system has leveraged enhanced privacy towards safeguarding sensitive patient information over heterogeneous computing platforms. In this paper, we introduce the FedHealthFog framework, which was meticulously developed to overcome the difficulties of distributed learning in resource-constrained IoT-enabled healthcare systems, particularly those sensitive to delays and energy efficiency. Conventional federated learning approaches face challenges stemming from substantial compute requirements and significant communication costs. This is primarily due to their reliance on a singular server for the aggregation of global data, which results in inefficient training models. We present a transformational approach to address these problems by elevating strategically placed fog nodes to the position of local aggregators within the federated learning architecture. A sophisticated greedy heuristic technique is used to optimize the choice of a fog node as the global aggregator in each communication cycle between edge devices and the cloud. The FedHealthFog system notably accounts for drop in communication latency of 87.01%, 26.90%, and 71.74%, and energy consumption of 57.98%, 34.36%, and 35.37% respectively, for three benchmark algorithms analyzed in this study. The effectiveness of FedHealthFog is strongly supported by outcomes of our experiments compared to cutting-edge alternatives while simultaneously reducing number of global aggregation cycles. These findings highlight FedHealthFog's potential to transform federated learning in resource-constrained IoT environments for delay-sensitive applications.

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction.

The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.

Improved right ventricular strain estimation in rats using anisotropic diffusion filtering.

Calculating cardiac strains through speckle tracking echocardiography (STE) has shown promise as prognostic markers linked to functional indices and disease outcomes. However, the presence of acoustic shadowing often challenges the accuracy of STE in small animals such as rodents. The shadowing arises due to the complex anatomy of rodents, with operator dexterity playing a significant role in image quality. The effects of the semi-transparent shadows are further exacerbated in right ventricular (RV) imaging due to the thinness and rapid motion of the RV free wall (RVFW). The movement of the RVFW across the shadows distorts speckle tracking and produces unnatural and non-physical strains. The objective of this study was to minimize the effects of shadowing on STE by distinguishing "out-of-shadow" motion and identifying speckles in and out of shadow. Parasternal 2D echocardiography was performed, and short-axis B-mode (SA) images of the RVFW were acquired for a rodent model of pulmonary hypertension (n = 1). Following image acquisition, a denoising algorithm using edge-enhancing anisotropic diffusion (EED) was implemented, and the ensuing effects on strain analysis were visualized using a custom STE pipeline. Speckles in the shadowed regions were identified through a correlation between the filtered image and the original acquisition. Thus, pixel movement across the boundary was identified by enhancing the distinction between the shadows and the cardiac wall, and non-physical strains were suppressed. The strains obtained through STE showed expected patterns with enhanced circumferential contractions in the central region of the RVFW in contrast to smaller and nearly uniform strains derived from the unprocessed images.

Volumetric versus distortional deformation in rat lungs.

There are several lung diseases that lead to alterations in regional lung mechanics, including acute respiratory distress syndrome. Such alterations can lead to localized underventilation of the affected areas resulting in the overdistension of the surrounding healthy regions. They can also lead to the surrounding alveoli expanding unevenly or distorting. Therefore, the quantification of the regional deformation in the lungs offers insights into identifying the regions at risk of lung injury. Although few recent studies have developed image processing techniques to quantify the regional volumetric deformation in the lung from dynamic imaging, the presence and extent of distortional deformation in the lung, and its correlation with volumetric deformation, remain poorly understood. In this study, we present a method that uses the four-dimensional displacement field obtained from image registration to quantify both regional volumetric and distortional deformation in the lung. We used dynamic computed tomography scans in a healthy rat over the course of one respiratory cycle in free breathing. Non-rigid image registration was performed to quantify voxel displacement during respiration. The deformation gradient was calculated using the displacement field and its determinant was used to quantify regional volumetric deformation. Regional distortion was calculated as the ratio of maximum to minimum principal stretches using the isochoric part of the Cauchy green tensor. We found an inverse correlation between volumetric strains and distortion indicating that poorly expanding alveoli tend to experience larger distortion. The combination of regional volumetric strains and distortion may serve as high-fidelity biomarkers to identify the regions at risk of most adverse lung injuries.

On the possibility of estimating myocardial fiber architecture from cardiac strains.

The myocardium is composed of a complex network of contractile myofibers that are organized in such a way as to produce efficient contraction and relaxation of the heart. The myofiber architecture in the myocardium is a key determinant of cardiac motion and the global or organ-level function of the heart. Reports of architectural remodeling in cardiac diseases, such as pulmonary hypertension and myocardial infarction, potentially contributing to cardiac dysfunction call for the inclusion of an architectural marker for an improved assessment of cardiac function. However, the in-vivo quantification of three-dimensional myo-architecture has proven challenging. In this work, we examine the sensitivity of cardiac strains to varying myofiber orientation using a multiscale finite-element model of the LV. Additionally, we present an inverse modeling approach to predict the myocardium fiber structure from cardiac strains. Our results indicate a strong correlation between fiber orientation and LV kinematics, corroborating that the fiber structure is a principal determinant of LV contractile behavior. Our inverse model was capable of accurately predicting the myocardial fiber range and regional fiber angles from strain measures. A concrete understanding of the link between LV myofiber structure and motion, and the development of non-invasive and feasible means of characterizing the myocardium architecture is expected to lead to advanced LV functional metrics and improved prognostic assessment of structural heart disease.

Unilateral lung hypoplasia: a rare cause of unilateral opaque hemithorax in chest X-ray in a young boy.

Congenital abnormalities of lung are very rare entity, and very often under or misdiagnosed by physicians. The present case, a 12-year boy, who was initially diagnosed as unilateral massive pleural effusion with collapse of lung, and after thorough investigation, including CT scan of thorax, fiber-optic bronchoscopy, and echocardiography, a final diagnosis of unilateral lung hypoplasia was made. So if a teenager present with a unilateral opaque hemithorax in chest X-ray, this entity may be a differential diagnosis.