Artificial intelligence and machine learning in cancer imaging

Communications Medicine, Год журнала: 2022, Номер 2(1)

Опубликована: Окт. 27, 2022

An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case met, as well undertake robust testing prior its adoption into healthcare systems. This review highlights key developments in field. We discuss challenges opportunities AI ML imaging; considerations algorithms can be widely used disseminated; ecosystem needed promote growth

Язык: Английский

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Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures

Diagnostics, Год журнала: 2023, Номер 13(3), С. 546 - 546

Опубликована: Фев. 2, 2023

Lung cancer presents one of the leading causes mortalities for people around world. image analysis and segmentation are primary steps used early diagnosis cancer. Handcrafted medical imaging a very time-consuming task radiation oncologists. To address this problem, we propose in work to develop full entire system lung CT scan imaging. The proposed is composed two main parts: first part developed on top UNETR network, second classification classify output part, either benign or malignant, self-supervised network. powerful tool diagnosing combatting using 3D-input data. Extensive experiments have been performed contribute better results. Training testing Decathlon dataset. Experimental results conducted new state-of-the-art performances: accuracy 97.83%, 98.77% as accuracy. use

Язык: Английский

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Revolutionizing radiation therapy: the role of AI in clinical practice

Journal of Radiation Research, Год журнала: 2023, Номер 65(1), С. 1 - 9

Опубликована: Окт. 19, 2023

This review provides an overview of the application artificial intelligence (AI) in radiation therapy (RT) from a oncologist's perspective. Over years, advances diagnostic imaging have significantly improved efficiency and effectiveness radiotherapy. The introduction AI has further optimized segmentation tumors organs at risk, thereby saving considerable time for oncologists. also been utilized treatment planning optimization, reducing several days to minutes or even seconds. Knowledge-based deep learning techniques employed produce plans comparable those generated by humans. Additionally, potential applications quality control assurance plans, optimization image-guided RT monitoring mobile during treatment. Prognostic evaluation prediction using increasingly explored, with radiomics being prominent area research. future oncology offers establish standardization minimizing inter-observer differences improving dose adequacy evaluation. through may global implications, providing world-standard resource-limited settings. However, there are challenges accumulating big data, including patient background information correlating disease outcomes. Although remain, ongoing research integration technology hold promise advancements oncology.

Язык: Английский

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Deep learning for lungs cancer detection: a review

Artificial Intelligence Review, Год журнала: 2024, Номер 57(8)

Опубликована: Июль 8, 2024

Abstract Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating diagnosis classification diseases. In this study, we use variety medical imaging modalities, including X-rays, WSI, CT scans, MRI, thoroughly investigate techniques field classification. This study conducts comprehensive Systematic Literature Review (SLR) using for research, providing overview methodology, cutting-edge developments, quality assessments, customized approaches. It presents data from reputable journals concentrates years 2015–2024. solve difficulty manually identifying selecting abstract features images. includes wide range methods classifying but focuses most popular method, Network (CNN). CNN can achieve maximum accuracy because its multi-layer structure, automatic weights, capacity communicate local weights. Various algorithms shown with performance measures like precision, accuracy, specificity, sensitivity, AUC; consistently shows greatest accuracy. The findings highlight important contributions DCNN improving detection classification, making them an invaluable resource researchers looking gain greater knowledge learning’s function applications.

Язык: Английский

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Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions

Advances in Radiation Oncology, Год журнала: 2024, Номер 9(5), С. 101470 - 101470

Опубликована: Фев. 8, 2024

PurposeManual contour work for radiation treatment planning takes significant time to ensure volumes are accurately delineated. The use of artificial intelligence with deep learning based autosegmentation (DLAS) models has made itself known in recent years alleviate this workload. It is used organs at risk (OAR) contouring consistency performance and saving. purpose study was evaluate the current published data DLAS clinical target volume (CTV) contours, identify areas improvement, discuss future directions.MethodologyA literature review performed by utilizing key words "Deep Learning" AND ("Segmentation" OR "Delineation") "Clinical Target Volume" an indexed search into PubMed. A total 154 articles on criteria were reviewed. considered model used, disease site, targets contoured, guidelines utilized, overall performance.ResultsOf 53 investigating CTV, only 6 before 2020. Publications have increased years, 46 between 2020-2023. cervix (n=19) prostate (n=12) studied most frequently. Most studies (n=43) involved a single institution. Median sample size 130 patients (range: 5-1,052). common metrics utilized measure Dice similarity coefficient (DSC) followed Hausdorff distance. Dosimetric seldom reported (n=11). There also variability specific (RTOG, ESTRO, others). had good CTV multiple sites, showing DSC values >0.7. delineated faster compared manual contouring. However, some contours still required least minor edits, require improvement.ConclusionsDLAS demonstrates capability completing plans efficiency accuracy. developed validated institutions using developing institutions. about years. Future need include larger datasets different patient demographics, stages, validation multi-institutional settings, inclusion dosimetric performance. Manual directions. Of improvement.

Язык: Английский

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Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives

Strahlentherapie und Onkologie, Год журнала: 2024, Номер unknown

Опубликована: Авг. 6, 2024

Abstract The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. medical field radiation oncology is also subject to this development, AI all steps the patient journey. In review article, we summarize contemporary techniques and explore clinical applications AI-based automated segmentation models in radiotherapy planning, focusing on delineation organs at risk (OARs), gross tumor volume (GTV), target (CTV). Emphasizing need for precise individualized plans, various commercial freeware state-of-the-art approaches. Through own findings based literature, demonstrate improved efficiency consistency as well time savings different scenarios. Despite challenges implementation such domain shifts, potential benefits personalized treatment planning are substantial. integration mathematical growth detection further enhances possibilities refining volumes. As advancements continue, prospect one-stop-shop represents an exciting frontier radiotherapy, potentially enabling fast enhanced precision individualization.

Язык: Английский

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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

Applied Sciences, Год журнала: 2022, Номер 12(7), С. 3223 - 3223

Опубликована: Март 22, 2022

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered radiotherapy (RT). We conducted a PubMed Scopus search to identify AI application field RT limited last four years. total, 1824 original papers were identified, 921 analyzed by considering phase workflow according approaches. permits processing large quantities information, data, images stored oncology information systems, process that is not manageable individuals or groups. allows iterative tasks datasets (e.g., delineating normal tissues optimal planning solutions) might support entire community working various sectors RT, summarized this overview. AI-based are now on roadmap workflow, mainly segmentation, generation synthetic images, outcome prediction. Several concerns raised, including need harmonization while overcoming ethical, legal, skill barriers.

Язык: Английский

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Lung-RetinaNet: Lung Cancer Detection Using a RetinaNet With Multi-Scale Feature Fusion and Context Module

IEEE Access, Год журнала: 2023, Номер 11, С. 53850 - 53861

Опубликована: Янв. 1, 2023

Язык: Английский

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Automatic Segmentation with Deep Learning in Radiotherapy

Cancers, Год журнала: 2023, Номер 15(17), С. 4389 - 4389

Опубликована: Сен. 1, 2023

This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), methods. We collect key statistics about the to uncover commonalities, trends, methods, identify areas where more research might be needed. Moreover, we analyzed corpus by posing explicit questions aimed at providing high-quality actionable insights, including: “What should researchers think when starting study?”, “How can practices medical improved?”, is missing from corpus?”, more. allowed us provide practical guidelines on how conduct good study today’s competitive environment will useful for future within field, regardless specific radiotherapeutic subfield. To aid our analysis, used large language model ChatGPT condense information.

Язык: Английский

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A deep learning‐based framework (Co‐ReTr) for auto‐segmentation of non‐small cell‐lung cancer in computed tomography images

Journal of Applied Clinical Medical Physics, Год журнала: 2024, Номер 25(3)

Опубликована: Фев. 19, 2024

Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical image segmentation applications. However, CNNs limitations learning long-range spatial dependencies due to locality layers. Transformers were introduced address this challenge. In transformers with self-attention mechanism, even first layer information processing makes connections between distant locations. Our paper presents a novel framework that bridges these two unique techniques, and transformers, segment gross tumor volume (GTV) accurately efficiently computed tomography (CT) images non-small cell-lung cancer (NSCLC) patients.

Язык: Английский

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