Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Cancers, Journal Year: 2022, Volume and Issue: 14(14), P. 3442 - 3442

Published: July 15, 2022

Breast cancer is one the main death causes for women worldwide, as 16% of diagnosed malignant lesions worldwide are its consequence. In this sense, it paramount importance to diagnose these in earliest stage possible, order have highest chances survival. While there several works that present selected topics area, none them a complete panorama, is, from image generation interpretation. This work presents comprehensive state-of-the-art review and processing techniques detect Cancer, where potential candidates presented discussed. Novel methodologies should consider adroit integration artificial intelligence-concepts categorical data generate modern alternatives can accuracy, precision reliability expected mitigate misclassifications.

Language: Английский

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Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

European Radiology, Journal Year: 2023, Volume and Issue: 34(3), P. 2096 - 2109

Published: Sept. 2, 2023

Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators highlight key considerations for developing implementing solutions imaging.

Language: Английский

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Molecular mechanisms augmenting resistance to current therapies in clinics among cervical cancer patients

Medical Oncology, Journal Year: 2023, Volume and Issue: 40(5)

Published: April 15, 2023

Cervical cancer (CC) is the fourth leading cause of death (~ 324,000 deaths annually) among women internationally, with 85% these reported in developing regions, particularly sub-Saharan Africa and Southeast Asia. Human papillomavirus (HPV) considered major driver CC, availability prophylactic vaccine, HPV-associated CC expected to be eliminated soon. However, female patients advanced-stage cervical demonstrated a high recurrence rate (50–70%) within two years completing radiochemotherapy. Currently, 90% failures chemotherapy are during invasion metastasis cancers related drug resistance. Although molecular target therapies have shown promising results lab, they had little success due tumor heterogeneity fueling resistance bypass targeted signaling pathway. The last decades seen emergence immunotherapy, especially immune checkpoint blockade (ICB) therapies, as an effective treatment against metastatic tumors. Unfortunately, only small subgroup (< 20%) benefited from this approach, reflecting disease manifestation primary or acquired over time. Thus, understanding mechanisms driving could significantly improve quality medical care for steer them accurate, individualized treatment. rise artificial intelligence machine learning has also been pivotal factor discovery. With advancement such technology, screening diagnosis become easier. This review will systematically discuss different tumor-intrinsic extrinsic cells adapt resist current treatments scheme novel strategies overcome

Language: Английский

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Microfluidic Technology, Artificial Intelligence, and Biosensors As Advanced Technologies in Cancer Screening: A Review Article

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

Published: May 29, 2023

Cancer screening techniques aim to detect premalignant lesions and enable early intervention delay the onset of cancer while keeping incidence constant. Technology advancements have led development powerful tools such as microfluidic technology, artificial intelligence, machine learning algorithms, electrochemical biosensors aid in detection. Non-invasive methods like virtual colonoscopy endoscopic ultrasonography also been developed provide comprehensive pictures organs early. This review article provides an overview recent advances biomarkers through a narrative literature search. Microfluidic devices easy handling sub-microliter volumes become promising tool for detection, drug screening, modeling angiogenesis metastasis research. Machine intelligence shown high accuracy oncology-related diagnostic imaging, reducing manual steps lesion detection providing standardized accurate results, with potential global standardization areas colon polyps, breast cancer, primary metastatic brain cancer. A biomarker-based diagnosis is effective therapy, integrated nanoparticles offer multiplexing amplification capabilities. Understanding these advanced technologies' basics, achievements, challenges crucial advancing their use oncology.

Language: Английский

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Advanced Medical Image Segmentation Enhancement: A Particle Swarm Optimization-Based Histogram Equalization Approach

Published: Jan. 2, 2024

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study on the efficacy of Particle Swarm Optimization (PSO) combined with Histogram Equalization (HE) preprocessing segmentation, focusing Lung CT-Scan Chest X-ray datasets. Best Cost values reveal PSO algorithm’s performance, HE demonstrating significant stabilization enhanced convergence, particularly complex images. Evaluation metrics, including Accuracy, Precision, Recall, F-Score, Specificity, Dice, Jaccard, show substantial improvements preprocessing, emphasizing its impact accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, K-means, confirm competitiveness PSO-HE approach, especially The also underscores positive influence clarity precision. These findings highlight promise approach advancing accuracy reliability paving way further method integration to enhance this critical healthcare application.

Language: Английский

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Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity

Cancers, Journal Year: 2024, Volume and Issue: 16(8), P. 1454 - 1454

Published: April 10, 2024

Background: Renal cancers are among the top ten causes of cancer-specific mortality, which ccRCC subtype is responsible for most cases. The grading important in determining tumour aggressiveness and clinical management. Objectives: objectives this research were to predict WHO/ISUP grade pre-operatively characterise heterogeneity sub-regions using radiomics ML models, including comparison with pre-operative biopsy-determined a sub-group. Methods: Data obtained from multiple institutions across two countries, 391 patients pathologically proven ccRCC. For analysis, data separated into four cohorts. Cohorts 1 2 included respective cohort 3 was combined both 2, 4 subset 1, biopsy subsequent histology resection (partial or total nephrectomy) available. 3D image segmentation carried out derive voxel interest (VOI) mask. Radiomics features then extracted contrast-enhanced images, normalised. Pearson correlation coefficient XGBoost model used reduce dimensionality features. Thereafter, 11 algorithms implemented purpose predicting characterising tumours. Results: 50% core 25% periphery exhibited best performance, an average AUC 77.9% 78.6%, respectively. presented highest performance cohorts 3, values 87.6% 76.9%, With periphery, showed 95.0% 80.0% prediction when internal external validation, respectively, while had 31.0% classification final as reference standard. CatBoost classifier each 80.0%, 86.5%, 77.0% 90.3% Conclusions: signatures have potential superior compared biopsy. Moreover, contain useful information that should be analysed independently grade. Therefore, it possible distinguish improve patient care

Language: Английский

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Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors

Journal of Clinical Medicine, Journal Year: 2023, Volume and Issue: 12(8), P. 2818 - 2818

Published: April 11, 2023

Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting advances. Malignant tumors are critical focus of research and improvement clinical diagnosis treatment. Mediastinal malignancy an important tumor that attracts increasing attention today due to difficulties Combined with artificial challenges from drug discovery survival constantly being overcome. This article reviews progress use AI diagnosis, treatment, prognostic prospects mediastinal malignant based on current literature findings.

Language: Английский

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AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability

Cancers, Journal Year: 2023, Volume and Issue: 15(13), P. 3369 - 3369

Published: June 27, 2023

Glioma is the most common type of tumor in humans originating brain. According to World Health Organization, gliomas can be graded on a four-stage scale, ranging from benign malignant. The grading these tumors image information far trivial task for radiologists and one which they could assisted by machine-learning-based decision support. However, machine learning analytical pipeline also fraught with perils stemming different sources, such as inadvertent data leakage, adequacy 2D sampling, or classifier assessment biases. In this paper, we analyze glioma database sourced multiple datasets using simple classifier, aiming obtain reliable and, way, provide few guidelines ensure reliability. Our results reveal that focusing region interest augmentation techniques significantly enhanced accuracy confidence classifications. Evaluation an independent test set resulted AUC-ROC 0.932 discrimination low-grade high-grade gliomas, 0.893 classification grades 2, 3, 4. study highlights importance providing, beyond generic performance, measures how trustworthy model's output is, thus assessing certainty robustness.

Language: Английский

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Application of Artificial Intelligence in Prediction of Ki-67 Index in Meningiomas: A Systematic Review and Meta-Analysis

World Neurosurgery, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

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Applications of AI in Cancer Detection — A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Various Types of Cancer

Published: Jan. 3, 2025

The mixing of superior deep learning strategies has profoundly impacted the sector sickness identification, promising sizable advancements in diagnostic accuracy and performance. This paper explores utilization multi-scale convolutional layers, interest mechanisms, switch learning, generative adversarial networks (GANs), self-supervised healthcare domain. These techniques collectively beautify capability neural (CNNs) to discover diagnose diseases from medical pix with extraordinary precision. Multi-scale layers allow models capture features at numerous scales, improving sensitivity specificity disease detection, mainly situations like most cancers. Attention mechanisms similarly refine this process by allowing focus on applicable components an picture, mirroring meticulous examination professionals. Transfer leveraging training fashions, extensively reduces reliance tremendous, categorized datasets, thereby expediting development enhancing version accuracy. approach shown outstanding success throughout distinctive imaging modalities, X-rays CT scans, adaptability robustness models. GANs contribute via producing artificial records augment schooling addressing challenge limited data availability model performance, specifically uncommon scenarios. Self-supervised which trains fashions unlabeled proxy duties, demonstrated comparable performance absolutely supervised while requiring fewer samples, therefore lowering need for luxurious time-eating annotation. Innovations those areas have not only improved technical identification but also opened new avenues his or her application. Future research should explore multimodal mixes various assets, including genomic information digital health data, imparting a more complete perspective. implementation federated guarantees privacy decentralized assets. Explainable AI (XAI) enhance interpretability, fostering extra consider popularity amongst Moreover, integration wearable devices continuous fitness tracking improvement real-time adaptive hold tremendous promise revolutionizing patient care control. comprehensive method methodologies disorder underscores transformative potential healthcare. With aid modern-day demanding exploring progressive answers, we can pave way greater accuracy, efficiency, personalized systems, end results advancing current exercise.

Language: Английский

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