1.

Record Nr.

UNINA990000579270403321

Autore

Sedov, Leonid Ivanovich

Titolo

A course in continuum mechanics / SEDOV L.

Pubbl/distr/stampa

s.l. : s.e., s.d.

Locazione

DINSC

Collocazione

SC.0,313

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9911031631003321

Autore

Nema Rajeev

Titolo

Advances in Cancer Detection, Prediction, and Prognosis Using Artificial Intelligence and Machine Learning / / edited by Rajeev Nema, Ashok Kumar, Dinesh Kumar Saini

Pubbl/distr/stampa

Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025

ISBN

981-9693-46-2

Edizione

[1st ed. 2025.]

Descrizione fisica

1 online resource (602 pages)

Collana

Biomedical and Life Sciences Series

Altri autori (Persone)

KumarAshok

SainiDinesh Kumar

Disciplina

576.5

616.994

Soggetti

Cancer - Genetic aspects

Cancer

Bioinformatics

Biomathematics

Machine learning

Oncology

Cancer Genetics and Genomics

Cancer Biology

Mathematical and Computational Biology

Machine Learning

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa



Livello bibliografico

Monografia

Nota di contenuto

-- Chapter 1: Cancer Diagnosis: An overview  -- Chapter 2: Computational Methods in Oncology  -- Chapter 3: Overview of Computational Approaches for Cancer Diagnosis  -- Chapter 4: Artificial intelligence and Machine Learning in improving diagnostic accuracy  -- Chapter 5: Advances in Image Processing and Pattern Recognition  -- Chapter 6: Genomics and Bioinformatics in the discovery and validation of diagnostic biomarkers  -- Chapter 7: Computer-based wearable devices for remote patient monitoring.  -- Chapter 8: Overview of computational approaches for Cancer Prognosis  -- Chapter 9: Predictive Modeling for Cancer Prognosis  -- Chapter 10:Genomics and Transcriptomics based predictive and prognostic biomarkers  -- Chapter 11:Artificial Intelligence in Personalized Medicine and Treatment Planning  -- Chapter 12:Integrative Multi-Omics Approaches  -- Chapter 13:Challenges and Limitations of Computational Methods in Oncology  -- Chapter 14:Challenges in Integration of computational approaches with Clinical Practice  -- Chapter 15:Emerging Technologies in Computational Oncology  -- Chapter 16:Statement On the Effectiveness of AI And ML In Cancer Care  -- Chapter 17:Future Directions in Computational Cancer Research  -- Chapter 18:Deep learning algorithms to analyze medical images for early detection of cancer  -- Chapter 19:Ethical considerations regarding patient privacy and data security  -- Chapter 20:Call for further research and implementation of deep learning technologies in oncology for enhanced healthcare delivery.

Sommario/riassunto

This book covers all aspects of computational biology in studying cancer diagnosis and prognosis, including newer applications involving infection and inflammation, as well as basic information on advanced simulation techniques. It describes the different tools, risk-based modeling techniques, early prediction algorithms and the biomarkers of different cancers that help in their early and better diagnosis in routine clinical practice involving multiple organs and systems. Early cancer diagnosis and artificial intelligence (AI) are rapidly evolving fields, with the UK's National Health Service aiming to improve early diagnosis rates to 75% by 2028. Screening can improve early cancer detection and mortality, but patient selection and risk stratification are key challenges. AI algorithms can facilitate cancer diagnosis by triggering investigation in screened individuals according to clinical parameters and automating clinical workflows where capacity is limited. Machine learning, which learns complex data patterns to make predictions has the potential to revolutionize early cancer diagnosis and support capacity concerns through automation. The chapters present the advances in diagnosing different types of cancer including bladder cancer, breast cancer, colorectal cancer, kidney (renal cell) cancer, lung cancer, lymphoma, pancreatic cancer, prostate cancer, skin cancer, uterine and metastatic cancers. The chapters also cover recurrent cancer, advanced cancer treatment, and the management of cancer in adolescents and young adults. The pan-cancer analyses presented in the book cover all aspects of early diagnosis, supplemented by numerous illustrations and figures to offer a fresh perspective and lucid understanding of computer-based approaches in cancer management. This book simplifies computational methods in medical diagnosis and highlights the benefits of early detection compared to other methods. It is targeted at biomedical scientists and clinical practitioners who conduct artificial intelligence-based research.