Artificial intelligence in lung cancer: current applications and perspectives

Japanese Journal of Radiology, Journal Year: 2022, Volume and Issue: unknown

Published: Nov. 9, 2022

Language: Английский

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

Nanoscale, Journal Year: 2024, Volume and Issue: 16(11), P. 5458 - 5486

Published: Jan. 1, 2024

AI enabled imaging technology advances the precision, early detection, and personalizes treatment through analysis interpretation of medical images.

Language: Английский

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Current applications and development of artificial intelligence for digital dental radiography

Dentomaxillofacial Radiology, Journal Year: 2021, Volume and Issue: 51(1)

Published: July 8, 2021

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in field of dental maxillofacial radiology. Dental radiography, which is commonly used daily practices, provides an incredibly rich resource for AI development attracted many researchers to develop its application various purposes. This study reviewed applicability radiography from current studies. Online searches on PubMed IEEE Xplore databases, up December 2020, subsequent manual were performed. Then, we categorized according similarity following purposes: diagnosis caries, periapical pathologies, periodontal bone loss; cyst tumor classification; cephalometric analysis; screening osteoporosis; tooth recognition forensic odontology; implant system recognition; image quality enhancement. Current methodology each aforementioned subsequently discussed. Although most studies demonstrated a great potential further still needed before implementation clinical routine due several challenges limitations, such as lack datasets size justification unstandardized reporting format. Considering limitations challenges, future should follow standardized formats order align designs enhance impact globally.

Language: Английский

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Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study

The Lancet Digital Health, Journal Year: 2021, Volume and Issue: 3(8), P. e486 - e495

Published: July 26, 2021

BackgroundMedical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS diseases using data derived from real world, and externally test model photographs collected prospectively settings in which would most likely be adopted.MethodsIn this national evidence study, we trained DLS, Comprehensive AI Retinal Expert (CARE) system, identify 14 common retinal abnormalities 207 228 colour 16 with different distributions. CARE was internally validated 21 867 tested 18 136 35 across China where might adopted, including eight tertiary hospitals, six community physical examination centres. The further compared that ophthalmologists datasets non-Chinese ethnicities previously unused camera types. This registered ClinicalTrials.gov, NCT04213430, is currently closed.FindingsThe area under receiver operating characteristic curve (AUC) internal validation set 0·955 (SD 0·046). AUC values external were 0·965 (0·035) 0·983 (0·031) 0·953 (0·042) similar ophthalmologists. Large variations sensitivity observed among regions varying experience. system retained strong identification when dataset (AUC 0·960, 95% CI 0·957–0·964 referable diabetic retinopathy).InterpretationOur showed satisfactory multiple photographs, so could allow implemented adopted care.FundingThis funded by National Key R&D Programme China, Science Technology Planning Projects Guangdong Province, Natural Foundation Fundamental Research Funds Central Universities.TranslationFor Chinese translation abstract see Supplementary Materials section.

Language: Английский

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COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach

International Journal of Interactive Multimedia and Artificial Intelligence, Journal Year: 2020, Volume and Issue: 6(2), P. 4 - 4

Published: Jan. 1, 2020

The Corona Virus Disease (COVID-19) is an infectious disease caused by a new virus that has not been detected in humans before.The causes respiratory illness like the flu with various symptoms such as cough or fever that, severe cases, may cause pneumonia.The COVID-19 spreads so quickly between people, affecting to 1,200,000 people worldwide at time of writing this paper (April 2020).Due number contagious and deaths are continually growing day day, aim study develop quick method detect chest X-ray images using deep learning techniques.For purpose, object detection architecture proposed, trained tested public available dataset composed 1500 non-infected patients infected main goal our classify patient status either negative positive case.In experiments SDD300 model we achieve 94.92% sensibility 92.00% specificity detection, demonstrating usefulness application models images.

Language: Английский

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Screening for Lung Cancer in Individuals Who Never Smoked: An International Association for the Study of Lung Cancer Early Detection and Screening Committee Report

Journal of Thoracic Oncology, Journal Year: 2021, Volume and Issue: 17(1), P. 56 - 66

Published: Aug. 27, 2021

Language: Английский

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The Global Emergency of Novel Coronavirus (SARS-CoV-2): An Update of the Current Status and Forecasting

International Journal of Environmental Research and Public Health, Journal Year: 2020, Volume and Issue: 17(16), P. 5648 - 5648

Published: Aug. 5, 2020

Over the past two decades, there have been major outbreaks where crossover of animal Betacoronaviruses to humans has resulted in severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East (MERS-CoV). In December 2019, a global public health concern started with emergence new strain (SARS-CoV-2 or 2019 novel coronavirus, 2019-nCoV) which rapidly spread all over world from its origin Wuhan, China. SARS-CoV-2 belongs Betacoronavirus genus, includes human SARS-CoV, MERS other coronaviruses (HCoVs), HCoV-OC43 HCoV-HKU1. The fatality rate is lower than previous epidemics, but it faster spreading large number infected people viral pneumonia illness, showed be highly contagious. Based on current published evidence, herein we summarize origin, genetics, epidemiology, clinical manifestations, preventions, diagnosis up date treatments infections comparison those caused by SARS-CoV MERS-CoV. Moreover, possible impact weather conditions transmission also discussed. Therefore, aim present review reconsider pandemics provide reference for future studies as well therapeutic approaches.

Language: Английский

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Imaging of coronavirus disease 2019: A Chinese expert consensus statement

European Journal of Radiology, Journal Year: 2020, Volume and Issue: 127, P. 109008 - 109008

Published: April 17, 2020

Language: Английский

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Professionals’ responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study

BMC Health Services Research, Journal Year: 2021, Volume and Issue: 21(1)

Published: Aug. 14, 2021

Abstract Background Artificial Intelligence (AI) innovations in radiology offer a potential solution to the increasing demand for imaging tests and ongoing workforce crisis. Crucial their adoption is involvement of different professional groups, namely radiologists radiographers, who work interdependently but whose perceptions responses towards AI may differ. We aim explore knowledge, awareness attitudes amongst groups radiology, analyse implications future these technologies into practice. Methods conducted 18 semi-structured interviews with 12 6 radiographers from four breast units National Health Services (NHS) organisations one focus group 8 fifth NHS unit, between 2018 2020. Results found that vary respect knowledge around AI. Through networks, conference attendance, contacts industry developers, receive more information acquire applications Radiographers instead rely on localized personal networks information. Our results also show although both believe shortages, they differ significantly regarding impact it will have roles. Radiologists has take repetitive tasks allow them interesting challenging work. They are less concerned technology might constrain role autonomy. showed greater concern about could roles skills development. were confident ability respond positively risks opportunities posed by technology. Conclusions In summary, our findings suggest linked existing roles, mediated differences attributable inter-professional status identity. These question broad-brush assertions deskilling which neglect need healthcare be integrated processes subject high levels

Language: Английский

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Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology”

Diagnostics, Journal Year: 2023, Volume and Issue: 13(14), P. 2333 - 2333

Published: July 10, 2023

Diffuse lung disorders (DLDs) and interstitial diseases (ILDs) are pathological conditions affecting the parenchyma network. There approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing monitoring ILDs, as they can provide non-invasive, rapid, repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of parenchyma, resembling low-magnification anatomical preparation from histological perspective. The intrinsic contrast provided by air enables identification even subtlest morphological changes tissue. By interpreting findings observed on HRCT, radiologists make differential diagnosis pattern collaboration with clinical functional data. use quantitative software artificial intelligence (AI) further enhances analysis providing objective comprehensive evaluation. integration “meta-data” such demographics, laboratory, genomic, metabolomic, proteomic data through AI could lead to more instrumental profiling beyond human eye’s capabilities.

Language: Английский

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