Precision medicine in digital pathology via image analysis and machine learning

2021 ◽  
pp. 149-173
Author(s):  
Peter D. Caie ◽  
Neofytos Dimitriou ◽  
Ognjen Arandjelović
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Precision Medicine ◽  
Digital Pathology

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review

2021 ◽  
pp. 030098582110404
Author(s):  
Aleksandra Zuraw ◽  
Famke Aeffner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Work Environment ◽  
Digital Pathology ◽  
Whole Slide Imaging ◽  
Peer Reviews ◽  
Veterinary Pathology ◽  
Diagnostic Applications ◽  
Emerging Topics

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence–based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist’s assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology

2021 ◽  
pp. 019262332199375
Author(s):  
Famke Aeffner ◽  
Tobias Sing ◽  
Oliver C. Turner
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Digital Pathology ◽  
Complex Data ◽  
Special Issue ◽  
Novel Technologies ◽  
Adequate Test ◽  
Key Topics ◽  
Research Drug ◽  
Whole Slide Images

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

scholarly journals Impact of blurs on machine-learning aided digital pathology image analysis

2020 ◽  
Vol 1 (1) ◽  
pp. 31-38
Author(s):  
Maki Ogura ◽  
Tomoharu Kiyuna ◽  
Hiroshi Yoshida
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Digital Pathology ◽  
Pathology Image

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology

2020 ◽  
pp. 019262332098031
Author(s):  
Aleksandra Zuraw ◽  
Michael Staup ◽  
Robert Klopfleisch ◽  
Famke Aeffner ◽  
Danielle Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Image Analysis ◽  
Control System ◽  
Digital Pathology ◽  
Computational Method ◽  
Quality Control System ◽  
Data Sets ◽  
End Points ◽  
Key Steps

Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed quality control system for the entire digital pathology workflow. Such system requires clear procedural controls, appropriate user training, and involvement of specialists to oversee key steps of the workflow. The toxicologic pathologist is responsible for reporting data obtained by digital image analysis and therefore needs to ensure that it is correct. To accomplish that, they must understand the main parameters of the quality control system and should play an integral part in its conception and implementation. This manuscript describes the most common digital tissue image analysis end points and potential sources of analysis errors. In addition, it outlines recommended approaches for ensuring quality and correctness of results for both classical and machine-learning based image analysis solutions, as adapted from a recently proposed Food and Drug Administration regulatory framework for modifications to artificial intelligence/machine learning-based software as a medical device. These approaches are beneficial for any type of toxicopathologic study which uses the described end points and can be adjusted based on the intended use of the image analysis solution.

Precision medicine-based machine learning analyses to explore optimal exercise therapies for individuals with knee osteoarthritis

2021 ◽  
Vol 29 ◽  
pp. S397-S398
Author(s):  
S. Kim ◽  
M.R. Kosorok ◽  
L. Arbeeva ◽  
T. Schwartz ◽  
Y.M. Golightly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knee Osteoarthritis ◽  
Precision Medicine

scholarly journals Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽  
2021 ◽  
Author(s):  
Stefanie Warnat-Herresthal ◽  
◽  
Hartmut Schultze ◽  
Krishnaprasad Lingadahalli Shastry ◽  
Sathyanarayanan Manamohan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Distributed Data ◽  
X Ray ◽  
Uniform Distributions ◽  
Privacy Laws ◽  
Data Owner ◽  
Machine Learning Approach ◽  
Chest X Ray ◽  
Privacy Legislation

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

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