AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease using Novel Non-invasive Biomarkers.

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography.

Smartphone-Based Point-of-Care Urinalysis Assessment.

A dipstick urinalysis test is performed by immersing a reagent strip in the urine specimen and then comparing the resulting reagent pad colors with a reference key. The color assessment of the reagent strip can be performed manually or by using a urine analyzer. However, the manual procedure is prone to subjective inaccuracies in varying ambient illumination and urine analyzer equipment is expensive.

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.

In Alzheimer's disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population.

An incremental learning approach to automatically recognize pulmonary diseases from the multi-vendor chest radiographs.

The human respiratory network is a vital system that provides oxygen supply and nourishment to the whole body. Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized deep learning systems (in both transfer learning and fine-tuning modes) to diagnose pulmonary disorders using chest X-rays (CXRs).

RIDB: A Dataset of fundus images for retina based person identification.

The paper describes a dataset, entitled Retina Identification Database (RIDB). The stated dataset contains Retinal fundus images acquired using Fundus imaging camera TOPCON-TRC 50 EX. The abovementioned dataset holds a significant position in retinal recognition and identification.

An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan.

Public health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to construct an effective representation model that is able to reveal the relationship patterns and trends. The purpose of this work is to combine disease incidence spatio-temporal data with other features of interest in a mutlivariate spatio-temporal model for investigating characteristic disease and feature patterns over identified hotspots.

A Parallel Architecture for the Partitioning Around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare.

Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time.

Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula.

Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula.

A Methodological Review of 3D Reconstruction Techniques in Tomographic Imaging.

Computer Vision has provided immense support to medical diagnostics over the past two decades. Analogous to Non Destructive Testing of mechanical parts, advances in medical imaging has enabled surgeons to determine root cause of an illness by consulting medical images particularly 3-D imaging. 3-D modeling in medical imaging has been pursued using surface rendering, volume rendering and regularization based methods.

Decision support system for detection of hypertensive retinopathy using arteriovenous ratio.

Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR).

Predicting Progression From Mild Cognitive Impairment to Alzheimer's Disease Using Autoregressive Modelling of Longitudinal and Multimodal Biomarkers.

Mild cognitive impairment is a preclinical stage of Alzheimer's disease (AD). For effective treatment of AD, it is important to identify mild cognitive impairment (MCI) patients who are at a high risk of developing AD over the course of time. In this study, autoregressive modelling of multiple heterogeneous predictors of Alzheimer's disease is performed to capture their evolution over time.

A Nonparametric Approach for Mild Cognitive Impairment to AD Conversion Prediction: Results on Longitudinal Data.

The goal of this study is to introduce a nonparametric technique for predicting conversion from Mild Cognitive impairment (MCI)-to-Alzheimer's disease (AD). Progression of a slowly progressing disease such as AD benefits from the use of longitudinal data; however, research till now is limited due to the insufficient patient data and short follow-up time. A small dataset size invalidates the estimation of underlying disease progression model; hence, a supervised nonparametric method is proposed.

Designing area optimized application-specific network-on-chip architectures while providing hard QoS guarantees.

With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows.

Covert network analysis for key player detection and event prediction using a hybrid classifier.

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework.

Automated detection of exudates and macula for grading of diabetic macular edema.

Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula.

Detection and classification of retinal lesions for grading of diabetic retinopathy.

Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates.

Automated detection and grading of diabetic maculopathy in digital retinal images.

Diabetic maculopathy is one of the retinal abnormalities in which a diabetic patient suffers from severe vision loss due to the affected macula. It affects the central vision of the person and causes blindness in severe cases. In this article, we propose an automated medical system for the grading of diabetic maculopathy that will assist the ophthalmologists in early detection of the disease.

Effect of atrioventricular conduction on heart rate variability.

This paper discusses the effect of atrioventricular conduction time (AVCT) on the short-term Heart Rate Variability (HRV) by computing HRV parameters using intervals between the onsets of successive P waves (PP time series) for three groups: normal, arrhythmia and sudden cardiac death (SCD) patients. A very precise wavelet transform based ECG delineator was developed to detect PP, PR and RR time series. Mean PR variation in arrhythmia and SCD group was found to be significantly high as compared to the normal group.

Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early.

Efficient algorithm development of CIS speech processing strategy for cochlear implants.

Continuous Interleaved Sampling (CIS) is one of the most useful and famous speech processing strategies used in Cochlear Implant speech processors. However, algorithm realization in hardware is a laborious task due to high computation cost of the algorithm. Real-time issues and low-power design demands an optimized realization of algorithm.

Laboratory prototype of cochlear implant: design and techniques.

This paper presents design overview of a low cost prototype of Cochlear Implant developed from commercial off-the-shelf components. Design scope includes speech processing module implemented on a commercial digital signal processor, transcutaneous data and power transceiver developed from a single pair of inductive coils and finally a stimulator circuitry for cochlear stimulation. Different speech processing strategies such as CIS, SMSP and F0/F1 have been implemented and tested using a novel, indigenously developed speech processing research module which evaluates the performance of speech processing strategies in software, hardware and practical scenarios.

Hardware accelerator for prediction of exons.

Gene annotation is by nature a computationally intensive problem, as it needs to process huge data size of DNA sequences. This forces the need to look for alternate ways of implementing algorithms to predict exons. The paper presents an accelerator for indexing DNA sequences.