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 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 Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Dandan Sun ◽  
Yuchen Du ◽  
Qiuying Chen ◽  
Luyao Ye ◽  
Huai Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Torsion Angle ◽  
Choroidal Thickness ◽  
Imaging Features ◽  
Lasso Regression ◽  
Test Set ◽  
Swept Source ◽  
Negatively Associated ◽  
Fundus Photographs

Purpose: To construct quantifiable models of imaging features by machine learning describing early changes of optic disc and peripapillary region, and to explore their performance as early indicators for choroidal thickness (ChT) in young myopic patients.Methods: Eight hundred and ninety six subjects were enrolled. Imaging features were extracted from fundus photographs. Macular ChT (mChT) and peripapillary ChT (pChT) were measured on swept-source optical coherence tomography scans. All participants were divided randomly into training (70%) and test (30%) sets. Imaging features correlated with ChT were selected by LASSO regression and combined into new indicators of optic disc (IODs) for mChT (IOD_mChT) and for pChT (IOD_pChT) by multivariate regression models in the training set. The performance of IODs was evaluated in the test set.Results: A significant correlation between IOD_mChT and mChT (r = 0.650, R2 = 0.423, P < 0.001) was found in the test set. IOD_mChT was negatively associated with axial length (AL) (r = −0.562, P < 0.001) and peripapillary atrophy (PPA) area (r = −0.738, P < 0.001) and positively associated with ovality index (r = 0.503, P < 0.001) and torsion angle (r = 0.242, P < 0.001) in the test set. Every 1 × 10 μm decrease in IOD_mChT was associated with an 8.87 μm decrease in mChT. A significant correlation between IOD_pChT and pChT (r = 0.576, R2 = 0.331, P < 0.001) was found in the test set. IOD_pChT was negatively associated with AL (r = −0.478, P < 0.001) and PPA area (r = −0.651, P < 0.001) and positively associated with ovality index (r = 0.285, P < 0.001) and torsion angle (r = 0.180, P < 0.001) in the test set. Every 1 × 10 μm decrease in IOD_pChT was associated with a 9.64 μm decrease in pChT.Conclusions: The study introduced a machine learning approach to acquire imaging information of early changes of optic disc and peripapillary region and constructed quantitative models significantly correlated with choroidal thickness. The objective models from fundus photographs represented a new approach that offset limitations of human annotation and could be applied in other areas of fundus diseases.

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

scholarly journals Decreased choroidal and scleral thicknesses in highly myopic eyes with posterior staphyloma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Un Chul Park ◽  
Eun Kyoung Lee ◽  
Bo Hee Kim ◽  
Baek-Lok Oh
Keyword(s):  
Choroidal Thickness ◽  
Structural Changes ◽  
Meta Analysis ◽  
Cross Sectional Study ◽  
Pathologic Myopia ◽  
Sectional Study ◽  
Cross Sectional ◽  
Swept Source ◽  
Posterior Staphyloma ◽  
Myopic Maculopathy

AbstractIn this cross-sectional study, we investigated choroidal thickness (CT) and scleral thickness (ST) in highly myopic eyes and their associations with ocular factors. Patients underwent widefield swept-source optical coherence tomography (OCT) to measure the CT and ST at the subfovea and 3000 μm superior, inferior, temporal, and nasal to the fovea and macular curvature. A total of 237 eyes (154 patients) were included. At all five measurement points, thinner CTs and STs were associated with longer axial lengths (r = − 0.548 to − 0.357, all P < 0.001) and greater macular curvatures (r = − 0.542 to − 0.305, all P < 0.001). The CT and ST were significantly thinner in eyes with posterior staphyloma than in those without at all measurement points (all P ≤ 0.006) but did not differ between eyes with the wide macular and narrow macular type of staphyloma. Eyes with myopic maculopathy of category ≥ 3 according to the International Meta-Analysis for Pathologic Myopia classification had significantly thinner CTs and STs than those with category ≤ 2 (all P ≤ 0.005). In highly myopic eyes, a decrease in the CT and ST was more pronounced in eyes with more structural changes, such as longer axial length, steeper macular curvature, and the presence of posterior staphyloma.

scholarly journals Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Guie Li ◽  
Zhongliang Cai ◽  
Yun Qian ◽  
Fei Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Low Income ◽  
Urban Poverty ◽  
Image Features ◽  
Housing Affordability ◽  
Learning Approaches ◽  
Housing Inequality ◽  
High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

scholarly journals Chest computed tomography imaging features in patients with coronavirus disease 2019 (COVID-19)

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110106
Author(s):  
Hoda Salah Darwish ◽  
Mohamed Yasser Habash ◽  
Waleed Yasser Habash
Keyword(s):  
Computed Tomography ◽  
Intermediate Phase ◽  
Early Stage ◽  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Intermediate Stage ◽  
Mediastinal Lymphadenopathy ◽  
Imaging Features ◽  
Stage Disease ◽  
Bilateral Lesions

Objective To analyze computed tomography (CT) features of symptomatic patients with coronavirus disease 2019 (COVID-19). Methods Ninety-five symptomatic patients with COVID-19 confirmed by reverse-transcription polymerase chain reaction from 1 May to 14 July 2020 were retrospectively enrolled. Follow-up CT findings and their distributions were analyzed and compared from symptom onset to late-stage disease. Results Among all patients, 15.8% had unilateral lung disease and 84.2% had bilateral disease with slight right lower lobe predilection (47.4%). Regarding lesion density, 49.4% of patients had pure ground glass opacity (GGO) and 50.5% had GGO with consolidation. Typical early-stage patterns were bilateral lesions in 73.6% of patients, diffuse lesions (41.0%), and GGO (65.2%). Pleural effusion occurred in 13.6% and mediastinal lymphadenopathy in 11.5%. During intermediate-stage disease, 47.4% of patients showed GGO as the disease progressed; however, consolidation was the predominant finding (52.6%). Conclusion COVID-19 pneumonia manifested on lung CT scans with bilateral, peripheral, and right lower lobe predominance and was characterized by diffuse bilateral GGO progressing to or coexisting with consolidation within 1 to 3 weeks. The most frequent CT lesion in the early, intermediate, and late phases was GGO. Consolidation appeared in the intermediate phase and gradually increased, ending with reticular and lung fibrosis-like patterns.

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