Harnessing Artificial Intelligence to Enhance Global Breast Cancer Care: A Scoping Review of Applications, Outcomes, and Challenges

Cancers, Journal Year: 2025, Volume and Issue: 17(2), P. 197 - 197

Published: Jan. 9, 2025

In recent years, Artificial Intelligence (AI) has shown transformative potential in advancing breast cancer care globally. This scoping review seeks to provide a comprehensive overview of AI applications care, examining how they could reshape diagnosis, treatment, and management on worldwide scale discussing both the benefits challenges associated with their adoption. accordance PRISMA-ScR ensuing guidelines reviews, PubMed, Web Science, Cochrane Library, Embase were systematically searched from inception end May 2024. Keywords included "Artificial Intelligence" "Breast Cancer". Original studies based focus narrative synthesis was employed for data extraction interpretation, findings organized into coherent themes. Finally, 84 articles included. The majority conducted developed countries (n = 54). publications last 10 years 83). six main themes screening 32), image detection nodal status 7), AI-assisted histopathology 8), assessing post-neoadjuvant chemotherapy (NACT) response 23), margin assessment 5), as clinical decision support tool 9). been used tools augment treatment decisions multidisciplinary tumor board settings. Overall, demonstrated improved accuracy efficiency; however, most did not report patient-centric outcomes. show promise enhancing diagnostic planning. However, persistent adoption, such quality, algorithm transparency, resource disparities, must be addressed advance field.

Language: Английский

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Prediction and detection of terminal diseases using Internet of Medical Things: A review

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109835 - 109835

Published: Feb. 24, 2025

Language: Английский

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BCNet: A Novel Deep Learning Model for Enhanced Breast Cancer Classification Using Histopathological Images

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Breast cancer is the most commonly diagnosed among women and a leading cause of cancer-related deaths globally, necessitating accurate timely diagnosis for effective treatment. Histopathological examination breast tissue samples gold standard diagnosing cancer, but this process subjective, time-consuming, reliant on level pathologist’s expertise. This study introduces new deep learning model, Cancer Network (BCNet), specifically designed to detect classify cancer. BCNet, 22-layer convolutional neural network (CNN), aims enhance diagnostic accuracy by capturing high-level discriminative features tailored images. The BCNet model was evaluated against established CNN models, demonstrating superior performance, achieving an up 99.8% binary classification 99.6% multi-class at different magnifications. These results highlight BCNet’s robustness potential reduce errors assist pathologists. Future research should explore generalizability across larger datasets its integration into clinical workflows provide real-time, AI-assisted support.

Language: Английский

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Mammo-Bench: A Large-scale Benchmark Dataset of Mammography Images

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 2, 2025

Abstract Breast cancer remains a significant global health concern, and machine learning algorithms computer-aided detection systems have shown great promise in enhancing the accuracy efficiency of mammography image analysis. However, there is critical need for large, benchmark datasets training deep models breast detection. In this work we developed Mammo-Bench, large-scale dataset images, by collating data from seven well-curated resources, viz ., DDSM, INbreast, KAU-BCMD, CMMD, CDD-CESM, DMID, RSNA Screening Dataset. To ensure consistency across images diverse sources while preserving clinically relevant features, preprocessing pipeline that includes segmentation, pectoral muscle removal, intelligent cropping proposed. The consists 74,436 high-quality mammographic 26,500 patients 7 countries one largest open-source databases to best our knowledge. show efficacy on large dataset, performance ResNet101 architecture was evaluated Mammo-Bench results compared independently few member an external VinDr-Mammo. An 78.8% (with augmentation minority classes) 77.8% (without augmentation) achieved proposed other which varied 25 – 69%. Noticeably, improved prediction classes observed with dataset. These establish baseline demonstrate Mammo-Bench's utility as comprehensive resource developing evaluating analysis systems.

Language: Английский

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The Diagnostic Classification of the Pathological Image Using Computer Vision

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 96 - 96

Published: Feb. 8, 2025

Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), shown superior performance in tasks such as classification, segmentation, object detection pathology. has significantly improved accuracy disease diagnosis healthcare. By leveraging advanced algorithms machine techniques, computer systems can analyze medical images with high precision, often matching or even surpassing human expert performance. In pathology, deep models been trained on large datasets annotated pathology to perform cancer diagnosis, grading, prognostication. While approaches show great promise challenges remain, including issues related model interpretability, reliability, generalization across diverse patient populations imaging settings.

Language: Английский

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Prospects for the Application of Artificial Intelligence in Mammography

Journal of radiology and nuclear medicine, Journal Year: 2025, Volume and Issue: 105(5), P. 282 - 286

Published: Feb. 21, 2025

Today in the world there is a growing interest interpretation of radiologic, particular mammographic, data using artificial intelligence (AI). In presented review scientific literature, based on most significant studies recent years an attempt was made to determine place AI radiologic diagnosis breast cancer. It shown that future, can become integral part cancer mammographic screening, although at moment ethical and legal issues its use have not been fully resolved.

Language: Английский

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Transformative insights in breast cancer: review of atomic force microscopy applications

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 28, 2025

Breast cancer remains one of the foremost global health concerns, highlighting urgent need for innovative diagnostic and therapeutic strategies. Traditional imaging techniques, such as mammography ultrasound, play essential roles in clinical practice; however, they often fall short detecting early-stage tumors providing comprehensive insights into mechanical properties cells. In this context, Atomic Force Microscopy (AFM) has emerged a transformative tool breast research, owing to its high-resolution capabilities nanomechanical characterization. This review explores recent advancements AFM technology applied emphasizing key findings that include differentiation various stages tumor progression through imaging, precise characterization properties, capability single-cell analysis. These not only enhance our understanding heterogeneity but also reveal potential biomarkers early detection targets. Furthermore, critically examines several challenges limitations associated with application research. Issues complexities sample preparation, accessibility, cost are discussed. Despite these challenges, transform biology is significant. Looking ahead, continued promise deepen guide strategies aimed at improving patient outcomes.

Language: Английский

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A Low-Cost Optomechatronic Diffuse Optical Mammography System for 3D Image Reconstruction: Proof of Concept

Diagnostics, Journal Year: 2025, Volume and Issue: 15(5), P. 584 - 584

Published: Feb. 27, 2025

Background: The development and initial testing of an optomechatronic system for the reconstruction three-dimensional (3D) images to identify abnormalities in breast tissue assist diagnosis cancer is presented. Methods: This combines 3D technology with diffuse optical mammography (DOM) offer a detecting tool that complements assists medical diagnosis. DOM analyzes properties light, density composition variations. Integrating enables detailed visualization precise tumor localization sizing, offering more information than traditional methods. technological combination accurate, earlier diagnoses helps plan effective treatments by understanding patient's anatomy location. Results: Using Chinese ink, it was possible simulated 10, 15, 20 mm diameter phantoms from cosmetic surgery. Conclusions: Data can be processed using algorithms generate images, providing non-invasive safe approach anomalies. Currently, pilot phase phantoms, enabling evaluation its accuracy functionality before application clinical studies.

Language: Английский

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Improving radiomics-based models for esophagogastric variceal bleeding risk prediction in cirrhotic patients

World Journal of Gastroenterology, Journal Year: 2025, Volume and Issue: 31(11)

Published: March 12, 2025

A recent study by Peng et al developed a predictive model for first-instance secondary esophageal variceal bleeding in cirrhotic patients integrating clinical and multi-organ radiomic features. The combined radiomic-clinical demonstrated strong capabilities, achieving an area under the curve of 0.951 training cohort 0.930 validation cohort. results highlight potential noninvasive prediction models assessing risk, aiding timely decision-making. Additionally, manual delineation regions interest raises risk observer bias despite efforts to minimize it. adjusted covariates, while some confounders, such as socioeconomic status, alcohol use, liver function scores, were not included. imbalance sizes between groups may reduce statistical power validation. Expanding incorporating multi-center external would improve generalizability. Future studies should focus on long-term patient outcomes, exploring additional imaging modalities, automated segmentation techniques refine model.

Language: Английский

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Advanced deep learning and large language models: Comprehensive insights for cancer detection

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105495 - 105495

Published: March 1, 2025

Language: Английский

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