scholarly journals Correction to: Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuchen Du ◽  
Qiuying Chen ◽  
Ying Fan ◽  
Jianfeng Zhu ◽  
Jiangnan He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Automatic Identification ◽  
Imaging Features ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Disc Region ◽  
Myopic Maculopathy

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuchen Du ◽  
Qiuying Chen ◽  
Ying Fan ◽  
Jianfeng Zhu ◽  
Jiangnan He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Early Stage ◽  
Image Features ◽  
Pathologic Myopia ◽  
High Risk Population ◽  
Imaging Features ◽  
Chorioretinal Atrophy ◽  
Disc Region ◽  
Myopic Maculopathy

Abstract Background Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. Methods A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features’ performance of classifying severe MM. Results Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). Conclusions Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features.

scholarly journals Automated detection of imaging features of disproportionately enlarged subarachnoid space hydrocephalus using machine learning methods

2019 ◽  
Vol 21 ◽  
pp. 101605 ◽  
Author(s):  
Nathaniel B. Gunter ◽  
Christopher G. Schwarz ◽  
Jonathan Graff-Radford ◽  
Jeffrey L. Gunter ◽  
David T. Jones ◽  
...  
Keyword(s):  
Machine Learning ◽  
Subarachnoid Space ◽  
Automated Detection ◽  
Imaging Features ◽  
Learning Methods ◽  
Machine Learning Methods

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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