RETRACTED ARTICLE: Google’s new AI technology detects cardiac issues using retinal scan

Borra, Surya Prasada Rao; Sumathy, B.; Ismail, B. Mohammed; Kumar, Sheo; Phasinam, Khongdet; Ramesh, Ram

doi:10.1007/s13204-021-02208-5

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study, Borra et al (2022) investigated the validity, reliability, and acceptability of this unique approach compared with prevalent peripheral risk factors of cardiovascular disease to evaluate whether ECG‐gating retinal pictures refine retinal artery caliber measures. Their pre‐processing techniques were RGB channel separation, normalization, and contrast limited adaptive histogram equalization (CLAHE).…”

Section: Identification Of Risk Factors Of Systemic Diseasesmentioning

confidence: 99%

Use of artificial intelligence algorithms to predict systemic diseases from retinal images

Khan

Surya

Roy

et al. 2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

The rise of non‐invasive, rapid, and widely accessible quantitative high‐resolution imaging methods, such as modern retinal photography and optical coherence tomography (OCT), has significantly impacted ophthalmology. These techniques offer remarkable accuracy and resolution in assessing ocular diseases and are increasingly recognized for their potential in identifying ocular biomarkers of systemic diseases. The application of artificial intelligence (AI) has been demonstrated to have promising results in identifying age, gender, systolic blood pressure, smoking status, and assessing cardiovascular disorders from the fundus and OCT images. Although our understanding of eye–body relationships has advanced from decades of conventional statistical modeling in large population‐based studies incorporating ophthalmic assessments, the application of AI to this field is still in its early stages. In this review article, we concentrate on the areas where AI‐based investigations could expand on existing conventional analyses to produce fresh findings using retinal biomarkers of systemic diseases. Five databases—Medline, Scopus, PubMed, Google Scholar, and Web of Science were searched using terms related to ocular imaging, systemic diseases, and artificial intelligence characteristics. Our review found that AI has been employed in a wide range of clinical tests and research applications, primarily for disease prediction, finding biomarkers and risk factor identification. We envisage artificial intelligence‐based models to have significant clinical and research impacts in the future through screening for high‐risk individuals, particularly in less developed areas, and identifying new retinal biomarkers, even though technical and socioeconomic challenges remain. Further research is needed to validate these models in real‐world setting.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Prediction

show abstract

Section: Identification Of Risk Factors Of Systemic Diseasesmentioning

confidence: 99%

Use of artificial intelligence algorithms to predict systemic diseases from retinal images

Khan

Surya

Roy

et al. 2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Tumors that are detected early are less likely to transmit to certain other portions of the body, but late detection makes treatments challenging and, in certain cases, catastrophic, along with affecting people's social lives. Different Machine Learning methods [19], Computer vision [17], neural network organization [21], and classification [22] [23] approaches have been used to help recognize, segment, and define skin cancerous growths. The different kinds of cancerous lesions described in this study are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Sripada

2022

ijhs

View full text Add to dashboard Cite

Melanoma is a malignant skin disease that kills roughly one million people per year, as per WHO records. The proposed work's major goal is to create and develop an enhanced deep neural network model that can evaluate, recognize, and predict cutaneous skin disease in its early stages with more accuracy, lowering the chance of death. The dataset, which was gathered from dermatologists and the public domain, contains roughly 3606 images of various types of skin lesions; some of the images were distorted. Gaussian and bilateral filters were used to decrease the noise in the images. After cleaning the data, the proposed six Convolutional layered Deep-CNN networks deployed to identify and predict skin cancer. The proposed CNN network was able to recognize skin diseases such Malignant Melanoma, Squamous cellcarcinoma, and Basal cellcarcinoma with an optimal error rate and good accuracy of 98.07 percent.

show abstract

Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic Review

Shwetha,

Ramana

2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

RETRACTED ARTICLE: Google’s new AI technology detects cardiac issues using retinal scan

Cited by 5 publications

References 19 publications

Use of artificial intelligence algorithms to predict systemic diseases from retinal images

Use of artificial intelligence algorithms to predict systemic diseases from retinal images

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic Review

Contact Info

Product

Resources

About