An independent healthcare provider

Professor Mark Redmond is one of the leading cardiothoracic surgeons in the country. He completed his training at Johns Hopkins Hospital in Baltimore, Maryland, and has held a number of prominent positions, including Director of Paediatric Heart and Lung Transplant, Co-Director of the Albert Broccoli Centre for Aortic Diseases, and Director of The Cardiac Research Laboratories, at Johns Hopkins. He returned to Dublin in 2000 where he founded the Beacon Hospital and Medical Group in Stillorgan. Professor Redmond continues to lead as a consultant cardiothoracic surgeon and is active in developing and promoting alternative models to public healthcare.</p

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a model. In this study, we demonstrate the utilization of a Long Short-Term Memory (LSTM-CCA) model for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data in order to gain insights into how P-RF data are utilized. The P-RF data are constructed from the in-phase and quadrature component (I/Q) data processed via histograms, and are combined with enhanced EO data via dense optical flow (DOF). The preprocessed data are then used as training data with an LSTM-CCA model in order to achieve object detection and tracking. In order to determine the impact of the different data inputs, a greedy algorithm (explainX.ai) is implemented to determine the weight and impact of the canonical variates provided to the fusion model on a scenario-by-scenario basis. This research introduces an explainable LSTM-CCA framework for P-RF and EO sensor fusion, providing novel insights into the sensor fusion process that can assist in the detection and differentiation of targets and help decision-makers to determine the weights for each input

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a model. In this study, we demonstrate the utilization of a Long Short-Term Memory (LSTM-CCA) model for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data in order to gain insights into how P-RF data are utilized. The P-RF data are constructed from the in-phase and quadrature component (I/Q) data processed via histograms, and are combined with enhanced EO data via dense optical flow (DOF). The preprocessed data are then used as training data with an LSTM-CCA model in order to achieve object detection and tracking. In order to determine the impact of the different data inputs, a greedy algorithm (explainX.ai) is implemented to determine the weight and impact of the canonical variates provided to the fusion model on a scenario-by-scenario basis. This research introduces an explainable LSTM-CCA framework for P-RF and EO sensor fusion, providing novel insights into the sensor fusion process that can assist in the detection and differentiation of targets and help decision-makers to determine the weights for each input

Human occupancy detection via passive cognitive radio

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects—not only in residential rooms—but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects

A case of teratoid Wilms’ tumour

Wilms’ tumour is the most common renal malignancy in children and the fourth most common childhood cancer. In the United States, Wilms’ tumour accounts for 6.6% of childhood malignancies, with 500 new cases presenting each year. This condition arises from pluripotent embryonal cells in the developing kidney. Pluripotency is apparent in the pathognomic triphasic histologic appearance, consisting of three components: epithelial, blastemal and stromal cells. The teratoid histological variant of Wilms’ tumour is rare, with only 16 reported cases. It is identified histologically by heterologous differentiation in the presence of other mature tissue types within a Wilms’ tumour. These mature tissues may include muscle, squamous, bone, cartilage, glial, adipose and glandular tissues. Fernandes et al. refined the definition of teratoid Wilms’ tumour by restricting it to tumours with heterologous differentiation accounting for greater than 50% of their volume.</p

Combined pleuroscopy and endobronchial ultrasound for diagnosis and staging of suspected lung cancer

The standard approach to staging of lung cancer in patients with pleural effusion (clinical M1a) is thoracentesis followed by pleural biopsies if the cytologic analysis is negative. If pleural biopsy findings are negative, endobronchial ultrasound-guided transbronchial needle aspiration is used to complete the staging process and, in some cases, obtain diagnosis. In this case series we report 7 patients in which a combined procedure was performed for staging of known or suspected lung cancer. We found that the combined approach was both feasible and safe in this case series. Keywords: Pleuroscopy, Endobronchial ultrasoun

Secondary spontaneous pneumothorax in patients with sarcoma treated with Pazopanib, a case control study

Abstract Background The tyrosine kinase inhibitor pazopanib is used for treatment of sarcoma. Recent studies have suggested that the use of pazopanib may lead to the development of pneumothorax, an unexpected adverse effect in patients with sarcoma metastatic to the chest. Methods We conducted a retrospective case control study of patients with sarcoma with metastases to the chest with pneumothorax (cases) and without pneumothorax (controls). The control population was selected from tumor registry in a 1:4 (cases to controls) ratio. The primary outcome of interest was the association between pazopanib and pneumothorax risk in patients with sarcoma metastatic to the chest. Secondary objective was to evaluate risk factors for pneumothorax. Results We identified 41 cases and 164 controls. Using purposeful selection method the odds of developing pneumothorax while being on pazopanib was not significant in univariate (p = .06) and multivariable analysis (p = .342). On univariate analysis risk factors of pneumothorax in patients with sarcoma were age, male sex, African American race, the presence of cavitary lung nodules/masses, and the presence of pleural-based nodules/masses. On multivariate analysis, only the presence of cavitary lung nodules/masses (P < .001) and the presence of pleural-based nodules/masses (P < .001) remained as risk factors for developing pneumothorax. Conclusion Pazopanib does not increase the risk of pneumothorax in patients with sarcoma and evidence of metastatic disease to the chest. Presence of cavitary lung nodules/masses and the presence of pleural-based nodules/masses were found to be risk factors for pneumothorax