Applications of Artificial Intelligence and Machine learning in smart cities

Computer Communications, Journal Year: 2020, Volume and Issue: 154, P. 313 - 323

Published: March 1, 2020

Language: Английский

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Radiomics with artificial intelligence: a practical guide for beginners

Diagnostic and Interventional Radiology, Journal Year: 2019, Volume and Issue: 25(6), P. 485 - 495

Published: Sept. 4, 2019

Radiomics is a relatively new word for the field of radiology, meaning extraction high number quantitative features from medical images. Artificial intelligence (AI) broadly set advanced computational algorithms that basically learn patterns in data provided to make predictions on unseen sets. can be coupled with AI because its better capability handling massive amount compared traditional statistical methods. Together, primary purpose these fields extract and analyze as much meaningful hidden possible used decision support. Nowadays, both radiomics have been getting attention their remarkable success various radiological tasks, which has met anxiety by most radiologists due fear replacement intelligent machines. Considering ever-developing advances power availability large sets, marriage humans machines future clinical practice seems inevitable. Therefore, regardless feelings, should familiar concepts. Our goal this paper was three-fold: first, familiarize AI; second, encourage get involved fields; and, third, provide recommendations good design assessment works.

Language: Английский

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A survey on deep learning in medicine: Why, how and when?

Information Fusion, Journal Year: 2020, Volume and Issue: 66, P. 111 - 137

Published: Sept. 15, 2020

Language: Английский

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Overview of radiomics in breast cancer diagnosis and prognostication

The Breast, Journal Year: 2019, Volume and Issue: 49, P. 74 - 80

Published: Nov. 6, 2019

Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity positive predictive power screening diagnostic approaches, respectively; b) invasiveness with discomfort for women undergoing tests; c) long turnaround time recall tests. In the setting, is suboptimal, when a suspicious lesion detected recommended, value modest. Recent technological advances in medical imaging, especially field artificial intelligence applied to image analysis, hold promise addressing challenges detection, assessment treatment response, monitoring disease progression. Radiomics include feature extraction from images; these features are related tumor size, shape, intensity, texture, collectively providing comprehensive characterization, so-called radiomics signature tumor. based hypothesis that extracted quantitative data derives mechanisms occurring at genetic molecular levels. article we focus role potential diagnosis prognostication.

Language: Английский

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Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Journal Year: 2021, Volume and Issue: 6(9), P. 856 - 864

Published: Feb. 9, 2021

Language: Английский

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Recent Updates of Transarterial Chemoembolilzation in Hepatocellular Carcinoma

International Journal of Molecular Sciences, Journal Year: 2020, Volume and Issue: 21(21), P. 8165 - 8165

Published: Oct. 31, 2020

Transarterial chemoembolization (TACE) is a standard treatment for intermediate-stage hepatocellular carcinoma (HCC). In this review, we summarize recent updates on the use of TACE HCC. can be performed using two techniques; conventional (cTACE) and drug-eluting beads (DEB-TACE). The anti-tumor effect has been reported to similar; however, DEB-TACE carries higher risk hepatic artery biliary injuries relatively lower post-procedural pain than cTACE. used early stage HCC if other curative treatments are not feasible or as neoadjuvant before liver transplantation. also considered selected patients with limited portal vein thrombosis preserved function. When deciding repeat TACE, ART (Assessment Retreatment TACE) score ABCR (AFP, BCLC, Child-Pugh, Response) guide decision process, refractoriness needs considered. Studies combination therapy modalities, such local ablation, radiation therapy, systemic have actively conducted still ongoing. Recently, new prognostic models, including analysis neutrophil-lymphocyte ratio, radiomics, deep learning, developed help predict survival after TACE.

Language: Английский

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Artificial intelligence for clinical oncology

Cancer Cell, Journal Year: 2021, Volume and Issue: 39(7), P. 916 - 927

Published: April 29, 2021

Language: Английский

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Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Cancer Cell International, Journal Year: 2021, Volume and Issue: 21(1)

Published: May 21, 2021

Abstract Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth AI in last decade evidenced be potential platform for optimal decision-making by super-intelligence, where mind limited process huge data a narrow time range. Cancer multifaced disorder with thousands genetic epigenetic variations. AI-based hold great promise pave way identify these mutations aberrant protein interactions at very early stage. Modern biomedical research also focused bring technology clinics safely ethically. assistance pathologists physicians could leap forward towards prediction disease risk, diagnosis, prognosis, treatments. Clinical applications Machine Learning (ML) cancer diagnosis treatment are future medical guidance faster mapping new every individual. By using base system approach, researchers can collaborate real-time share knowledge digitally potentially heal millions. In this review, we present game-changing clinics, connecting biology Intelligence explain how help oncologist precise treatment.

Language: Английский

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Radiomic Features at Contrast-enhanced CT Predict Recurrence in Early Stage Hepatocellular Carcinoma: A Multi-Institutional Study

Radiology, Journal Year: 2020, Volume and Issue: 294(3), P. 568 - 579

Published: Jan. 14, 2020

Background Early stage hepatocellular carcinoma (HCC) is the ideal candidate for resection in patients with preserved liver function; however, cancer will recur half of these and no reliable prognostic tool has been established. Purpose To investigate effectiveness radiomic features predicting tumor recurrence after early HCC. Materials Methods In total, 295 (median age, 58 years; interquartile range, 50–65 221 men) who underwent contrast material–enhanced CT curative HCC that met Milan criteria between February 2009 December 2016 were retrospectively recruited from three independent institutions. Follow-up consisted serum α-fetoprotein level, function tests, dynamic imaging examinations every 3 months during first 2 years then 6 thereafter. development cohort 177 institution 1, recurrence-related computationally extracted its periphery a radiomics signature was built least absolute shrinkage selection operator regression. Two models, one integrating preoperative pre- postoperative variables, created by using multivariable Cox regression analysis. An external 118 institutions used to validate proposed models. Results The model integrated level number; incorporated microvascular invasion satellite nodules into above-mentioned predictors. both study cohorts, two radiomics-based models provided better predictive performance (concordance index ≥0.77, P < .05 all), lower prediction error (integrated Brier score ≤0.14), larger net benefits, as determined means decision curve analysis, than rival without widely adopted staging systems. gave risk strata high, intermediate, or low distinct profiles recurrent number. Conclusion postresection helped predict carcinoma. © RSNA, 2020 Online supplemental material available this article.

Language: Английский

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Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence

Technology in Society, Journal Year: 2019, Volume and Issue: 60, P. 101198 - 101198

Published: Oct. 23, 2019

Language: Английский

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