How to Improve the Digital Pathology Imaging Factor

 The introduction of whole-slide digital imaging technologies has sparked the development of novel interactive pathology education models, accelerated clinical processes, and allowed for the development of new medically actionable image analysis tools and investigative methodologies. This technology has supplied the clinical and scientific community with new solutions that will need to be integrated with established models for modern care, despite the fact that it is still evolving. Finally, we expect a new system of care to evolve, in which these breakthrough technologies are rationally and thoroughly validated and integrated into everyday practise. The high-tech world of digital pathology imaging is beset by a low-tech issue. And getting the more knowledge about of improving the factor of digital pathology join us at CME/CPD accredited 11th Emirates Pathology & Digital Pathology Conference on May 09-10, 2022, Online & enhance your knowledge on Pathology

The issue: Due to the fact that digital pathology is advancing rapidly around the world, with more clinicians examining tissue images on "smart" computers to diagnose patients, there are no trustworthy guidelines for the processing and digitalization of tissue slides.

This means that low-quality slides are mixed in with high-quality slides, possibly confusing or misleading a computer algorithm attempting to learn how to recognize a malignant cell, Case Western Reserve University researchers are working to change that

The two have a three-year, $1.2 million grant from the National Cancer Institute to fund their new quality-control tool, which was just published in the Journal of Clinical Oncology Clinical Informatics.

The new system uses a set of metrics and classifiers to help users identify corrupted photographs and keep those that would help technicians and physicians diagnose problems.

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Image analysis with computer systems

imaging physicists, to explore the potential of a newly established branch in the field of pathology, Pathologists have been collaborating with technology experts, such as data scientists, computational engineers,: machine vision, a component of computational image analysis, thanks to advances in scanning technology and the increasing availability of large datasets of digital images. The main goal of this field is to extract and/or produce quantitative data from digital image subject matter, either alone or in conjunction with other types of spatially or non-spatially based biological or omics data. Recognizing the data provided by computational image analysis has a high degree of dimensionality

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AI models, such as ML and DL, can be utilised for a variety of tasks, including illness diagnosis and automatic detection, segmentation, and quantification of histological parameters and structural alterations. These are referred to as 'low-level' activities. In contrast, in 'high-level tasks,' AI models are used to simultaneously interrogate and integrate multiple classes of primary data — for example, histopathological image data spatially coupled to transcriptomic data — allowing for the prediction of processes such as disease aggression (for example, the biological potential of a malignancy), patient outcome, organ engraftment survival, and therapeutic response. As a result, AI has the ability to raise digital images from their fundamental duty as a tool for visual assessment of illness condition to a far more complicated and comprehensive position as a tool for disease trajectory prediction. In the context of direct human cognitive and visual assessment, these new capabilities enable the extraction of meaningful information in a way that was previously not conceivable with conventional microscopy.

Pathology is more common in pathology than in other areas of medicine. Aside from research, the route to completely digitised clinical pathology departments with whole slide imaging (WSI), convolutional neural networks (CNNs), and cost-effective, high-performance processing and data storage is still a work in progress. The technology is still in its infancy, best practises for AI differ, and data difficulties can be a stumbling barrier.

The process of creating high-resolution digital images from tissue sections on glass slides is known as digital pathology (also known as telepathology or whole slide imaging). A pathologist examines these glass slides under a microscope as part of the diagnosis process. Pathologists can now operate remotely and interact with other experts when second opinions are needed, thanks to the advent of digital pathology, which stores digital pictures on secure servers and allows them to be viewed on computer monitors. There are numerous advantages to incorporating digital pathology into clinical practise. Despite the fact that this has long been recognised, its use as a diagnostic tool is still limited, and pathologists' estimates for its future use are cautious

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Reference pathology and digital pathology UCGconferences press releases and blogs

Medium: https://medium.com/@traviis.stork/digital-pathology-advantages-limitations-and-emerging-perspectives-57bce27ae3f2

Linked In: https://www.linkedin.com/pulse/digital-pathology-advantages-limitations-emerging-dr-travis-stork

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