Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer

被引:13
作者
Chugh, Vibhas [1 ]
Basu, Adreeja [2 ]
Kaushik, Ajeet [3 ]
Bhansali, Shekhar [4 ]
Basu, Aviru Kumar [1 ]
机构
[1] Inst Nano Sci & Technol, Quantum Mat & Devices Unit, Mohali 140306, Punjab, India
[2] St Johns Univ, Biol Sci, New York, NY 10301 USA
[3] Florida Polytech Univ, Dept Environm Engn, NanoBioTech Lab, Lakeland, FL 33805 USA
[4] Florida Int Univ, Elect & Comp Engn, Miami, FL 33199 USA
关键词
Compendex;
D O I
10.1039/d3nr05648a
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article. AI enabled imaging technology advances the precision, early detection, and personalizes treatment through analysis and interpretation of medical images.
引用
收藏
页码:5458 / 5486
页数:29
相关论文
共 109 条
[91]  
Suresha R., 2022, INT C EDGE COMPUT AP, P1565
[92]   Support vector machine for diagnosis cancer disease: A comparative study [J].
Sweilam, Nasser H. ;
Tharwat, A. A. ;
Moniem, N. K. Abdel .
EGYPTIAN INFORMATICS JOURNAL, 2010, 11 (02) :81-92
[93]   Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks [J].
Togacar, Mesut ;
Ergen, Burhan ;
Comert, Zafer .
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2020, 40 (01) :23-39
[94]   Deep learning in cancer diagnosis, prognosis and treatment selection [J].
Tran, Khoa A. ;
Kondrashova, Olga ;
Bradley, Andrew ;
Williams, Elizabeth D. ;
Pearson, John, V ;
Waddell, Nicola .
GENOME MEDICINE, 2021, 13 (01)
[95]   Tuneable surface shear forces to physically displace nonspecific molecules in protein biomarker detection [J].
Vaidyanathan, Ramanathan ;
Rauf, Sakandar ;
Shiddiky, Muhammad J. A. ;
Trau, Matt .
BIOSENSORS & BIOELECTRONICS, 2014, 61 :184-191
[96]   MXene-based cytosensor for the detection of HER2-positive cancer cells using CoFe2O4@Ag magnetic nanohybrids conjugated to the HB5 aptamer [J].
Vajhadin, Fereshteh ;
Mazloum-Ardakani, Mohammad ;
Shahidi, Maryamsadat ;
Moshtaghioun, Seyed Mohammad ;
Haghiralsadat, Fateme ;
Ebadi, Azar ;
Amini, Abbas .
BIOSENSORS & BIOELECTRONICS, 2022, 195
[97]   Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis [J].
Vashisth, Shubham ;
Dhall, Ishika ;
Aggarwal, Garima .
JOURNAL OF INTELLIGENT SYSTEMS, 2021, 30 (01) :998-1013
[98]   2D Materials towards sensing technology: From fundamentals to applications [J].
Vazquez Sulleiro, Manuel ;
Dominguez-Alfaro, Antonio ;
Alegret, Nuria ;
Silvestri, Alessandro ;
Jennifer Gomez, I .
SENSING AND BIO-SENSING RESEARCH, 2022, 38
[99]   Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA [J].
Wan, Nathan ;
Weinberg, David ;
Liu, Tzu-Yu ;
Niehaus, Katherine ;
Ariazi, Eric A. ;
Delubac, Daniel ;
Kannan, Ajay ;
White, Brandon ;
Bailey, Mitch ;
Bertin, Marvin ;
Boley, Nathan ;
Bowen, Derek ;
Cregg, James ;
Drake, Adam M. ;
Ennis, Riley ;
Fransen, Signe ;
Gafni, Erik ;
Hansen, Loren ;
Liu, Yaping ;
Otte, Gabriel L. ;
Pecson, Jennifer ;
Rice, Brandon ;
Sanderson, Gabriel E. ;
Sharma, Aarushi ;
St John, John ;
Tang, Catherina ;
Tzou, Abraham ;
Young, Leilani ;
Putcha, Girish ;
Haque, Imran S. .
BMC CANCER, 2019, 19 (01)
[100]   Competitive electrochemical aptasensor based on a cDNA-ferrocene/MXene probe for detection of breast cancer marker Mucin1 [J].
Wang, Haiyan ;
Sun, Jingjing ;
Lu, Lin ;
Yang, Xiao ;
Xia, Jianfei ;
Zhang, Feifei ;
Wang, Zonghua .
ANALYTICA CHIMICA ACTA, 2020, 1094 :18-25