1
|
Chen X, Chen F, Liang C, He G, Chen H, Wu Y, Chen Y, Shuai J, Yang Y, Dai C, Cao L, Wang X, Cai E, Wang J, Wu M, Zeng L, Zhu J, Hai D, Pan W, Pan S, Zhang C, Quan S, Su F. MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations. Front Microbiol 2023; 14:1308149. [PMID: 38149270 PMCID: PMC10750405 DOI: 10.3389/fmicb.2023.1308149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 11/14/2023] [Indexed: 12/28/2023] Open
Abstract
Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting grave clinical challenges due to its nonspecific symptoms and the urgent need for timely intervention. The severity and the rapid progression of TBM underscore the necessity of early and accurate diagnosis to prevent irreversible neurological deficits and reduce mortality rates. Traditional diagnostic methods, reliant primarily on clinical findings and cerebrospinal fluid analysis, often falter in delivering timely and conclusive results. Moreover, such methods struggle to distinguish TBM from other forms of neuroinfections, making it critical to seek advanced diagnostic solutions. Against this backdrop, magnetic resonance imaging (MRI) has emerged as an indispensable modality in diagnostics, owing to its unique advantages. This review provides an overview of the advancements in MRI technology, specifically emphasizing its crucial applications in the early detection and identification of complex pathological changes in TBM. The integration of artificial intelligence (AI) has further enhanced the transformative impact of MRI on TBM diagnostic imaging. When these cutting-edge technologies synergize with deep learning algorithms, they substantially improve diagnostic precision and efficiency. Currently, the field of TBM imaging diagnosis is undergoing a phase of technological amalgamation. The melding of MRI and AI technologies unquestionably signals new opportunities in this specialized area.
Collapse
Affiliation(s)
- Xingyu Chen
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Fanxuan Chen
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Chenglong Liang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Guoqiang He
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Hao Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yanchan Wu
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Yinda Chen
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Jincen Shuai
- Baskin Engineering, University of California, Santa Cruz, CA, United States
| | - Yilei Yang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | | | - Luhuan Cao
- Wenzhou Medical University, Wenzhou, China
| | - Xian Wang
- Wenzhou Medical University, Wenzhou, China
| | - Enna Cai
- Wenzhou Medical University, Wenzhou, China
| | | | | | - Li Zeng
- Wenzhou Medical University, Wenzhou, China
| | | | - Darong Hai
- Wenzhou Medical University, Wenzhou, China
| | - Wangzheng Pan
- Renji College of Wenzhou Medical University, Wenzhou, China
| | - Shuo Pan
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, China
| | - Feifei Su
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
| |
Collapse
|
2
|
Singh T, Saxena N, Khurana M, Singh D, Abdalla M, Alshazly H. Data Clustering Using Moth-Flame Optimization Algorithm. SENSORS 2021; 21:s21124086. [PMID: 34198501 PMCID: PMC8231885 DOI: 10.3390/s21124086] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 11/16/2022]
Abstract
A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.
Collapse
Affiliation(s)
- Tribhuvan Singh
- Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India;
| | - Nitin Saxena
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India; (N.S.); (M.K.)
| | - Manju Khurana
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India; (N.S.); (M.K.)
| | - Dilbag Singh
- School of Engineering and Applied Sciences, Bennett University, Greater Noida 201310, India;
| | - Mohamed Abdalla
- Department of Mathematics, Faculty of Science, King Khalid University, Abha 62529, Saudi Arabia;
- Department of Mathematics, Faculty of Science, South Valley University, Qena 83523, Egypt
| | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
- Correspondence:
| |
Collapse
|
6
|
An Efficient Optimization Method for Solving Unsupervised Data Classification Problems. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:802754. [PMID: 26336509 PMCID: PMC4532808 DOI: 10.1155/2015/802754] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 06/11/2015] [Accepted: 06/29/2015] [Indexed: 11/29/2022]
Abstract
Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.
Collapse
|