Evaluating the potential of retinal photography in chronic kidney disease detection: a review.

Background: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness.

Retinal changes and cardiac biomarker assessment in relation to chronic kidney disease: a single centre study.

Background: The prevalence of chronic kidney disease (CKD) is rising in Malaysia. Early detection is necessary to prevent disease progression, especially in terms of cardiovascular (CV) risk, the main cause of death in end-stage renal disease (ESRD). Retinal changes have proven to be a good predictor of CKD whereas cardiac biomarkers are useful in cardiovascular risk stratification.

Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring.

Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu's thresholding with the Chan-Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner.

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis.

Background And Aims: A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology.

Towards a Connected Mobile Cataract Screening System: A Future Approach.

Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities.

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology.

Stem cells are primitive and precursor cells with the potential to reproduce into diverse mature and functional cell types in the body throughout the developmental stages of life. Their remarkable potential has led to numerous medical discoveries and breakthroughs in science. As a result, stem cell-based therapy has emerged as a new subspecialty in medicine.

Automated pterygium detection method of anterior segment photographed images.

Background And Bjective: Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding.

Design and development of a content-based medical image retrieval system for spine vertebrae irregularity.

Background: Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.

Methods: In this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image.