A Revolution in Healthcare: AI-powered Cancer Imaging

被引:0
|
作者
Kumar, Gulshan [1 ]
Verma, Swati [2 ,3 ]
Malviya, Rishabha [1 ]
Paliwal, Sarvesh [3 ]
Narayan, Chaitanay Vinayak [2 ]
机构
[1] Galgotias Univ, Sch Med & Allied Sci, Dept Pharm, Greater Noida, India
[2] ITS Coll Pharm, Dept Pharm, Muradnagar 201206, Ghaziabad, India
[3] Banasthali Vidyapith, Dept Pharm, Banasthali 304022, Rajasthan, India
关键词
Deep learning; AI neural networks; radiomics; computer-aided diagnosis; data mining; pattern recognition; support vector machines; ARTIFICIAL-INTELLIGENCE AI; NODULE DETECTION; NEURAL-NETWORKS; LUNG-CANCER; DIAGNOSIS; INFORMATION; TECHNOLOGY; PROGNOSIS; THERAPY; SYSTEM;
D O I
10.2174/0115733947304178240804182938
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Diagnosing and successfully treating cancer remains to be a formidable challenge. Cancer diagnosis by conventional methods is laborious and highly subjective, reliant on the knowledge and experience of radiologists and pathologists. With the combination of AI and ML technologies, cancer imaging has seen a paradigm change. Medical imaging like CT, MRI, and PET scans may be analyzed using AIML algorithms and deep neural networks for characteristics and patterns that might indicate malignancy. More precise diagnosis and tailored treatment programs are possible with their aid in tumor segmentation and categorization. A type of artificial intelligence that has shown promise in cancer detection is radiomics. One more key approach utilized in AI-powered cancer detection is texture analysis. This technique entails analyzing the spatial organization of pixel intensities in a picture. The genetic elements that contribute to the genesis and progression of cancer are becoming better understood with the development of artificial intelligence systems that can analyze genomic data in addition to medical imaging. This review article delves into the revolutionary effects of AI and ML on cancer imaging, showcasing significant progress, obstacles, and potential solutions. Early detection, diagnosis, and personalized treatment methods are being transformed by these technologies, which are making use of the massive quantities of medical data that are accessible. The result is an improvement in patient outcomes.
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页数:15
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