Role of noninvasive ocular imaging as a biomarker in peripheral artery disease (PAD): A systematic review.

This study aimed to review the current literature exploring the utility of noninvasive ocular imaging for the diagnosis of peripheral artery disease (PAD). Our search was conducted in early April 2022 and included the databases Medline, Scopus, Embase, Cochrane, and others. Five articles were included in the final review. Of the five studies that used ocular imaging in PAD, two studies used retinal color fundus photography, one used optical coherence tomography (OCT), and two used optical coherence tomography angiography (OCTA) to assess the ocular changes in PAD. PAD was associated with both structural and functional changes in the retina. Structural alterations around the optic disc and temporal retinal vascular arcades were seen in color fundus photography of patients with PAD compared to healthy individuals. The presence of retinal hemorrhages, exudates, and microaneurysms in color fundus photography was associated with an increased future risk of PAD, especially the severe form of the disease. The retinal nerve fiber layer (RNFL) was significantly thinner in the nasal quadrant in patients with PAD compared to age-matched healthy individuals in OCT. Similarly, the choroidal thickness in the subfoveal region was significantly thinner in patients with PAD compared to controls. Patients with PAD also had a significant reduction in the retinal and choroidal circulation in OCTA compared to healthy controls. As PAD causes thinning and ischemic changes in retinal vessels, examination of the retinal vessels using retinal imaging techniques can provide useful information about early microvascular damage in PAD. Ocular imaging could potentially serve as a biomarker for PAD. PROSPERO ID: CRD42022310637.

A Novel Feature Selection Method for High-Dimensional Mixed Decision Tables.

Attribute reduction, also called feature selection, is one of the most important issues of rough set theory, which is regarded as a vital preprocessing step in pattern recognition, machine learning, and data mining. Nowadays, high-dimensional mixed and incomplete data sets are very common in real-world applications. Certainly, the selection of a promising feature subset from such data sets is a very interesting, but challenging problem. Almost all of the existing methods generated a cover on the space of objects to determine important features. However, some tolerance classes in the cover are useless for the computational process. Thus, this article introduces a new concept of stripped neighborhood covers to reduce unnecessary tolerance classes from the original cover. Based on the proposed stripped neighborhood cover, we define a new reduct in mixed and incomplete decision tables, and then design an efficient heuristic algorithm to find this reduct. For each loop in the main loop of the proposed algorithm, we use an error measure to select an optimal feature and put it into the selected feature subset. Besides, to deal more efficiently with high-dimensional data sets, we also determine redundant features after each loop and remove them from the candidate feature subset. For the purpose of verifying the performance of the proposed algorithm, we carry out experiments on data sets downloaded from public data sources to compare with existing state-of-the-art algorithms. Experimental results showed that our algorithm outperforms compared algorithms, especially in classification accuracy.