Emerging role of deep learning‐based artificial intelligence in tumor pathology

Cancer Communications, Journal Year: 2020, Volume and Issue: 40(4), P. 154 - 166

Published: April 1, 2020

Abstract The development of digital pathology and progression state‐of‐the‐art algorithms for computer vision have led to increasing interest in the use artificial intelligence (AI), especially deep learning (DL)‐based AI, tumor pathology. DL‐based been developed conduct all kinds work involved pathology, including diagnosis, subtyping, grading, staging, prognostic prediction, as well identification pathological features, biomarkers genetic changes. applications AI not only contribute improve diagnostic accuracy objectivity but also reduce workload pathologists subsequently enable them spend additional time on high‐level decision‐making tasks. In addition, is useful meet requirements precision oncology. However, there are still some challenges relating implementation issues algorithm validation interpretability, computing systems, unbelieving attitude pathologists, clinicians patients, regulators reimbursements. Herein, we present an overview how AI‐based approaches could be integrated into workflow discuss perspectives

Language: Английский

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Digital Pathology: Advantages, Limitations and Emerging Perspectives

Journal of Clinical Medicine, Journal Year: 2020, Volume and Issue: 9(11), P. 3697 - 3697

Published: Nov. 18, 2020

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved way this development, but implementation large scale challenging technical, logistical, and financial levels. Comparative studies have published reassuring data safety feasibility, experiences highlight need training knowledge pitfalls. Up to half pathologists are reluctant sign out reports only digital slides concerned about reporting without tool that represented their profession since its beginning. Guidelines by international organizations aim safeguard histology in realm, from acquisition over setup work-stations long-term archiving, must be considered starting point only. Cost-efficiency analyses occupational health issues addressed comprehensively. Image analysis blended into traditional work-flow, approval artificial intelligence diagnostics starts challenge human evaluation as gold standard. Here we discuss past implementations, future possibilities through addition intelligence, technical challenges, possible changes pathologist’s profession.

Language: Английский

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Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Cancer Communications, Journal Year: 2021, Volume and Issue: 41(11), P. 1100 - 1115

Published: Oct. 6, 2021

Abstract Over the past decade, artificial intelligence (AI) has contributed substantially to resolution of various medical problems, including cancer. Deep learning (DL), a subfield AI, is characterized by its ability perform automated feature extraction and great power in assimilation evaluation large amounts complicated data. On basis quantity data novel computational technologies, especially DL, been applied aspects oncology research potential enhance cancer diagnosis treatment. These applications range from early detection, diagnosis, classification grading, molecular characterization tumors, prediction patient outcomes treatment responses, personalized treatment, automatic radiotherapy workflows, anti‐cancer drug discovery, clinical trials. In this review, we introduced general principle summarized major areas application for discussed future directions remaining challenges. As adoption AI use increasing, anticipate arrival AI‐powered care.

Language: Английский

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Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review

European Journal of Cancer, Journal Year: 2021, Volume and Issue: 155, P. 200 - 215

Published: Aug. 11, 2021

Language: Английский

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Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer

Modern Pathology, Journal Year: 2021, Volume and Issue: 35(1), P. 44 - 51

Published: Sept. 7, 2021

Language: Английский

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Artificial intelligence in diagnostic pathology

Diagnostic Pathology, Journal Year: 2023, Volume and Issue: 18(1)

Published: Oct. 3, 2023

Abstract Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic has gone through a staggering transformation wherein new such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided techniques are used assisting, augmenting empowering the computational histopathology AI-enabled diagnostics. This paving way advancement precision medicine cancer. Automated whole slide imaging (WSI) scanners now rendering quality, high-resolution images entire glass slides combining these with innovative making it possible to integrate into all aspects reporting including anatomical, clinical, molecular pathology. recent approvals WSI primary diagnosis by FDA well approval prostate AI algorithm paved starting incorporate this exciting technology use can provide unique platform innovations advances anatomical clinical workflows. In review, we describe milestones landmark trials emphasis on future directions.

Language: Английский

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Computational Pathology: A Survey Review and The Way Forward

Journal of Pathology Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 100357 - 100357

Published: Jan. 1, 2024

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath develop infrastructure workflows digital diagnostics as assistive CAD system clinical pathology, facilitating transformational changes in the diagnosis treatment cancer are mainly address by tools. With evergrowing deep learning computer vision algorithms, ease data flow from currently witnessing a paradigm shift. Despite sheer volume engineering scientific works being introduced image analysis, there still considerable gap adopting integrating these algorithms practice. This raises significant question regarding direction trends undertaken CPath. In this article we provide comprehensive review more than 800 papers challenges faced problem design all-the-way application implementation viewpoints. We have catalogued each paper into model-card examining key layout current landscape hope helps community locate relevant facilitate understanding field's future directions. nutshell, oversee cycle stages which required be cohesively linked together associated with such multidisciplinary science. overview different perspectives data-centric, model-centric, application-centric problems. finally sketch remaining directions technical integration For updated information on survey accessing original cards repository, please refer GitHub. Updated version draft can also found arXiv.

Language: Английский

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Rapid identification of Astragalus membranaceus processing with rice water based on intelligent color recognition and multi-source information fusion technology

Chinese Herbal Medicines, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Convergence of evolving artificial intelligence and machine learning techniques in precision oncology

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 31, 2025

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field precision oncology, promising to improve diagnostic approaches therapeutic strategies for patients cancer. By analyzing multi-dimensional, multiomic, spatial pathology, radiomic data, these enable a deeper understanding intricate molecular pathways, aiding in identification critical nodes within tumor's biology optimize treatment selection. applications AI/ML oncology are extensive include generation synthetic e.g., digital twins, order provide necessary information design or expedite conduct clinical trials. Currently, many operational technical challenges exist related data technology, engineering, storage; algorithm development structures; quality quantity pipeline; sharing generalizability; incorporation into current workflow reimbursement models.

Language: Английский

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Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review

BMC Medical Research Methodology, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 21, 2025

Language: Английский

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