Screening for diabetic retinopathy—is the use of artificial intelligence and cost-effective fundus imaging the answer?

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

Download Full-text

A Single-center, Retrospective Study to Evaluate the Clinical Performance of Artificial Intelligence Medical Assisted Diagnostic Software (VeriSee DR) for Screening of Diabetic Retinopathy in Patients With Diabetes Mellitus

Case Medical Research ◽

10.31525/ct1-nct04160988 ◽

2019 ◽

Author(s):

Keyword(s):

Artificial Intelligence ◽

Diabetes Mellitus ◽

Diabetic Retinopathy ◽

Retrospective Study ◽

Clinical Performance ◽

Single Center ◽

Patients With Diabetes ◽

Diagnostic Software

Download Full-text

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190219102427 ◽

2020 ◽

Vol 16 (4) ◽

pp. 397-426

Author(s):

Muhammad Nadeem Ashraf ◽

Muhammad Hussain ◽

Zulfiqar Habib

Keyword(s):

Diabetic Retinopathy ◽

Blood Vessels ◽

Early Stage ◽

Spherical Shape ◽

Cost Effective ◽

Lesion Detection ◽

Diabetic Patients ◽

Cad System ◽

Non Invasive ◽

Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

Download Full-text

Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Energies ◽

10.3390/en14082338 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2338

Author(s):

Sofia Agostinelli ◽

Fabrizio Cumo ◽

Giambattista Guidi ◽

Claudio Tomazzoli

Keyword(s):

Artificial Intelligence ◽

Renewable Energy ◽

Energy Management ◽

Three Dimensional ◽

Cost Effective ◽

Automation System ◽

Climate Conditions ◽

Digital Twin ◽

Building Energy Management ◽

Computing Paradigm

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

Download Full-text

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

Nutrients ◽

10.3390/nu13051635 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1635

Author(s):

Sweeny Chauhan ◽

Alish Kerr ◽

Brian Keogh ◽

Stephanie Nolan ◽

Rory Casey ◽

...

Keyword(s):

Artificial Intelligence ◽

Pisum Sativum ◽

Cost Effective ◽

Glycated Haemoglobin ◽

Glucose Regulation ◽

Functional Ingredient ◽

Effective Manner ◽

Human Skeletal Muscle Cells ◽

Increased Risk

The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation.

Download Full-text

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519415500852 ◽

2015 ◽

Vol 15 (05) ◽

pp. 1550085 ◽

Cited By ~ 2

Author(s):

MADHURI TASGAONKAR ◽

MADHURI KHAMBETE

Keyword(s):

Diabetic Retinopathy ◽

Early Detection ◽

Ground Truth ◽

Vital Role ◽

Detection Accuracy ◽

Fuzzy C Means ◽

Fundus Imaging ◽

Imaging Detection ◽

Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

Download Full-text