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An Image Examination System for Retinal Optic Disc mining and Analysis with Social Group Optimization Algorithm. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022. [DOI: 10.4018/ijsir.300370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This work aims to develop a hybrid image examination system to extract and evaluate the Optic Disc (OD) from the Age-related Macular Degeneration (AMD) and Non-AMD class Digital Fundus Retinal Image (DFRI). This work implements an image pre-processing through Shannon’s Entropy and Social Group Optimization (SE+SGO) based thresholding and image post-processing with Level Set Segmentation (LSS). A relative study among the extracted OD and the ground-truth is then executed to compute the vital Picture Similarity Parameters (PSP). This study also presents a detailed pixel level data analysis practice on the extracted OD. Finally, the performance of the LSS is then validated against the existing segmentation techniques, such as Chan-Vese, Active-Contour and k-means clustering. The proposed work is executed on the iChallenge-AMD-2018 DFRI (400 images) and the results confirm that, proposed hybrid tool helps to achieve better values of Jaccard (86.82%), Dice (91.78%), Accuracy (98.94%), Precision (92.86%), Sensitivity (94.06%), and Specificity (99.46%).
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Wang N, Huang Y, Liu H, Fei X, Wei L, Zhao X, Chen H. Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records. Biomed Eng Online 2019; 18:98. [PMID: 31601207 PMCID: PMC6788002 DOI: 10.1186/s12938-019-0718-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 10/01/2019] [Indexed: 12/24/2022] Open
Abstract
Background Conventional risk prediction techniques may not be the most suitable approach for personalized prediction for individual patients. Therefore, individualized predictive modeling based on similar patients has emerged. This study aimed to propose a comprehensive measurement of patient similarity using real-world electronic medical records data, and evaluate the effectiveness of the individualized prediction of a patient’s diabetes status based on the patient similarity. Results When using no more than 30% of the whole training sample, the personalized predictive models outperformed corresponding traditional models built on randomly selected training samples of the same size as the personalized models (P < 0.001 for all). With only the top 1000 (10%), 700 (7%) and 1400 (14%) similar samples, personalized random forest, k-nearest neighbor and logistic regression models reached the globally optimal performance with the area under the receiver-operating characteristic (ROC) curve of 0.90, 0.82 and 0.89, respectively. Conclusions The proposed patient similarity measurement was effective when developing personalized predictive models. The successful application of patient similarity in predicting a patient’s diabetes status provided useful references for diagnostic decision-making support by investigating the evidence on similar patients.
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Affiliation(s)
- Ni Wang
- School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China
| | - Yanqun Huang
- School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China
| | - Honglei Liu
- School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China
| | - Xiaolu Fei
- Information Center, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Lan Wei
- Information Center, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Xiangkun Zhao
- School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China
| | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China. .,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing, 100069, China.
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