Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability

doi:10.1016/j.chemosphere.2023.138458

科研成果详情

题名	Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability
作者	Huang, Zhongguan 1; Chen, Shuainan2; Ali, H. Elhosiny 3; Elkamchouchi, Dalia H.4; Hu, Jun2; Ali, Elimam 5; Zhang, Jie 2; Huang, Yideng2
发表日期	2023-08
发表期刊	Chemosphere 影响因子和分区
语种	英语
原始文献类型	Journal Article
关键词	Biological nanomaterials CNN (Convolutional neural networks) Chemical components ELM (Extreme learning machines) Ear Environmental sustainability Infection treatment
其他关键词	WATER-RESOURCES APPLICATIONS ; NEURAL-NETWORK MODELS ; SHEAR-STRENGTH ; ANTIBACTERIAL ACTIVITY ; SILVER NANOPARTICLES ; INPUT DETERMINATION ; MENIERES-DISEASE ; DRUG-DELIVERY ; PREDICTION ; MEMBRANE
摘要	Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.
资助项目	Abdulrahman University[PNURSP2023R238];Key Program of Zhejiang Provincial Natural Science Foundation of China[LZ22H130001];Prince Satam bin Abdulaziz University[PSAU/2023/R/1444];National Natural Science Foundation of China[82171144,82171146];Deanship of Scientific Research at King Khalid University[R.G.P.2/483/44];
出版者	PERGAMON-ELSEVIER SCIENCE LTD
ISSN	0045-6535
EISSN	1879-1298
卷号	331
DOI	10.1016/j.chemosphere.2023.138458
页数	12
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
WOS记录号	WOS:000989242400001
收录类别	PUBMED ; SCIE ; EI ; SCOPUS
在线发表日期	2023-05
EI入藏号	20231814039735
EI主题词	Nanoparticles
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 723.4 Artificial Intelligence ; 761 Nanotechnology ; 933 Solid State Physics ; 941.1 Acoustical Instruments
URL	查看原文
PubMed ID	36966931
SCOPUSEID	2-s2.0-85153943614
通讯作者地址	[Zhang, Jie]Department of Otolaryngology,The First Affiliated Hospital of Wenzhou Medical University,Zhejiang,Wenzhou,325000,China ; [Huang, Yideng]Department of Otolaryngology,The First Affiliated Hospital of Wenzhou Medical University,Zhejiang,Wenzhou,325000,China
Scopus学科分类	Environmental Engineering;Environmental Chemistry;Chemistry (all);Pollution;Public Health, Environmental and Occupational Health;Health, Toxicology and Mutagenesis
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/174148
专题	附属第一医院其他_附属平阳医院（平阳县人民医院）
通讯作者	Zhang, Jie; Huang, Yideng
作者单位	1.Department of Otolaryngology,Pingyang Hospital Affiliated to Wenzhou Medical University,Zhejiang,Pingyang,325400,China; 2.Department of Otolaryngology,The First Affiliated Hospital of Wenzhou Medical University,Zhejiang,Wenzhou,325000,China; 3.Department of Physics,Faculty of Science,King Khalid University,Abha,P.O. Box 9004,Saudi Arabia; 4.Department of Information Technology,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O. Box 84428,Riyadh,11671,Saudi Arabia; 5.Department of Civil Engineering,College of Engineering in Al-Kharj,Prince Sattam Bin Abdulaziz University,Al-Kharj,11942,Saudi Arabia
第一作者单位	附属平阳医院（平阳县人民医院）
通讯作者单位	附属第一医院; 第一临床医学院（信息与工程学院）、附属第一医院
第一作者的第一单位	附属平阳医院（平阳县人民医院）
推荐引用方式 GB/T 7714	Huang, Zhongguan,Chen, Shuainan,Ali, H. Elhosiny,et al. Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability[J]. Chemosphere,2023,331.
APA	Huang, Zhongguan., Chen, Shuainan., Ali, H. Elhosiny., Elkamchouchi, Dalia H.., Hu, Jun., ... & Huang, Yideng. (2023). Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability. Chemosphere, 331.
MLA	Huang, Zhongguan,et al."Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability".Chemosphere 331(2023).