Artificial intelligence: A critical review of applications for lung nodule and lung cancer

Diagnostic and Interventional Imaging, Journal Year: 2022, Volume and Issue: 104(1), P. 11 - 17

Published: Dec. 10, 2022

Language: Английский

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Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Cancers, Journal Year: 2023, Volume and Issue: 15(17), P. 4344 - 4344

Published: Aug. 30, 2023

Lung cancer has one of the worst morbidity and fatality rates any malignant tumour. Most lung cancers are discovered in middle late stages disease, when treatment choices limited, patients’ survival rate is low. The aim screening identification malignancies early stage more options for effective treatments available, to improve outcomes. desire efficacy efficiency clinical care continues drive multiple innovations into practice better patient management, this context, artificial intelligence (AI) plays a key role. AI may have role each process workflow. First, acquisition low-dose computed tomography programs, AI-based reconstruction allows further dose reduction, while still maintaining an optimal image quality. can help personalization programs through risk stratification based on collection analysis huge amount imaging data. A computer-aided detection (CAD) system provides automatic potential nodules with high sensitivity, working as concurrent or second reader reducing time needed interpretation. Once nodule been detected, it should be characterized benign malignant. Two approaches available perform task: first represented by segmentation consequent assessment lesion size, volume, densitometric features; consists first, followed radiomic features extraction characterize whole abnormalities providing so-called “virtual biopsy”. This narrative review aims provide overview all possible applications screening.

Language: Английский

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Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports

Japanese Journal of Radiology, Journal Year: 2023, Volume and Issue: 42(2), P. 190 - 200

Published: Sept. 15, 2023

Abstract Purpose In this preliminary study, we aimed to evaluate the potential of generative pre-trained transformer (GPT) series for generating radiology reports from concise imaging findings and compare its performance with radiologist-generated reports. Methods This retrospective study involved 28 patients who underwent computed tomography (CT) scans had a diagnosed disease typical findings. Radiology were generated using GPT-2, GPT-3.5, GPT-4 based on patient’s age, gender, site, We calculated top-1, top-5 accuracy, mean average precision (MAP) differential diagnoses GPT-4, radiologists. Two board-certified radiologists evaluated grammar readability, image findings, impression, diagnosis, overall quality all 4-point scale. Results Top-1 Top-5 accuracies different highest radiologists, followed by in that order (Top-1: 1.00, 0.54, 0.21, respectively; Top-5: 0.96, 0.89, respectively). There no significant differences qualitative scores about between GPT-3.5 or ( p > 0.05). However, GPT impression diagnosis significantly lower than those < Conclusions Our suggests have possibility generate high readability reasonable very short keywords; however, concerns persist regarding accuracy impressions diagnoses, thereby requiring verification

Language: Английский

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Digital Pathology: Transforming Diagnosis in the Digital Age

Cureus, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 3, 2023

In the context of rapid technological advancements, narrative review titled "Digital Pathology: Transforming Diagnosis in Digital Age" explores significant impact digital pathology reshaping diagnostic approaches. This delves into various effects field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing ongoing transformation taking place. The investigation process digitizing traditional glass slides, which aims to improve accessibility facilitate sharing. Additionally, it addresses complexities associated with data security standardization challenges. Incorporating AI enhances pathologists' capabilities accelerates analytical procedures. Furthermore, highlights growing importance collaborative networks facilitating global knowledge It also emphasizes this technology on medical education patient care. provide an overview pathology's transformative innovative potential, highlighting its disruptive nature practices.

Language: Английский

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Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review

BMC Medical Imaging, Journal Year: 2023, Volume and Issue: 23(1)

Published: Sept. 15, 2023

Abstract Background Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung applications. Recently, many researchers have developed vision AI for diagnosis prognosis. Objective This scoping review aims to identify recent developments on imaging It provides key insights into how transformers complemented performance deep learning cancer. Furthermore, also identifies datasets that contributed field. Methods In this review, we searched Pubmed, Scopus, IEEEXplore, Google Scholar online databases. The search terms included intervention (vision transformers) task (i.e., cancer, adenocarcinoma, etc.). Two reviewers independently screened title abstract select relevant studies performed data extraction. A third reviewer was consulted validate inclusion exclusion. Finally, narrative approach used synthesize data. Results Of 314 retrieved studies, 34 published from 2020 2022. most commonly addressed in these classification types, such as squamous cell carcinoma versus identifying benign malignant pulmonary nodules. Other applications survival prediction patients segmentation lungs. lacked clear strategies clinical transformation. SWIN transformer a popular choice researchers; however, other architectures were reported where combined with convolutional neural networks or UNet model. Researchers publicly available database consortium genome atlas. One study cluster 48 GPUs, while one, two, four GPUs. Conclusion can be concluded models increasingly popularity developing However, their computational complexity relevance important factors considered future research work. valuable healthcare advance state-of-the-art We provide an interactive dashboard lung-cancer.onrender.com/ .

Language: Английский

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Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future

Diagnostic and Interventional Imaging, Journal Year: 2024, Volume and Issue: 105(11), P. 453 - 459

Published: June 24, 2024

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements diagnostic accuracy, efficiency, and patient outcomes. However, increasing adoption AI systems also raises concerns about their environmental impact, particularly context climate change. This review explores intersection change healthcare, examining challenges posed by energy consumption carbon footprint systems, as well potential solutions to mitigate impact. highlights energy-intensive nature model training deployment, contribution data centers greenhouse gas emissions, generation electronic waste. To address these challenges, development energy-efficient models, green computing practices, integration renewable sources are discussed solutions. emphasizes role optimizing workflows, reducing resource waste, facilitating sustainable practices such telemedicine. Furthermore, importance policy governance frameworks, global initiatives, collaborative efforts promoting is explored. concludes outlining best for including eco-design, lifecycle assessment, responsible management, continuous monitoring improvement. As industry continues embrace technologies, prioritizing sustainability responsibility crucial ensure that benefits realized while actively contributing preservation our planet.

Language: Английский

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Evolution of artificial intelligence as a modern technology in advanced cancer therapy

Journal of Drug Delivery Science and Technology, Journal Year: 2024, Volume and Issue: 98, P. 105892 - 105892

Published: June 15, 2024

Language: Английский

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Deep learning paradigms in lung cancer diagnosis: A methodological review, open challenges, and future directions

Physica Medica, Journal Year: 2025, Volume and Issue: 131, P. 104914 - 104914

Published: Feb. 11, 2025

Language: Английский

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A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer

Cancers, Journal Year: 2025, Volume and Issue: 17(5), P. 882 - 882

Published: March 4, 2025

According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It particularly high in list of leading causes death not only developed countries, but also worldwide; furthermore, it holds place terms cancer-related mortality. Nevertheless, many breakthroughs have been made last two decades regarding its management, with one most prominent being implementation artificial intelligence (AI) various aspects disease management. We included 473 papers this thorough review, which published during 5-10 years, order describe these breakthroughs. In screening programs, AI capable detecting suspicious nodules different imaging modalities-such as chest X-rays, computed tomography (CT), and positron emission (PET) scans-but discriminating between benign malignant well, success rates comparable or even better than those experienced radiologists. Furthermore, seems be able recognize biomarkers that appear patients who may develop cancer, years before event. Moreover, can assist pathologists cytologists recognizing type tumor, well specific histologic genetic markers play key role treating disease. Finally, treatment field, guide development personalized options for patients, possibly improving their prognosis.

Language: Английский

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Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Magnetic Resonance in Medical Sciences, Journal Year: 2023, Volume and Issue: 22(4), P. 401 - 414

Published: Jan. 1, 2023

Due primarily to the excellent soft tissue contrast depictions provided by MRI, widespread application of head and neck MRI in clinical practice serves assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition been extensively investigated research for their applicability across a range categories within medical imaging, including MRI. Analytical approaches AI shown potential addressing limitations associated with In this review, we focus on technical advancements deep-learning-based methodologies utility field encompassing aspects such as image acquisition reconstruction, lesion segmentation, disease classification diagnosis, prognostic prediction patients presenting We then discuss current offer insights regarding future challenges field.

Language: Английский

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