LITERATURE UPDATE
Digital pathology and artificial intelligence: a selection of research at the cutting edge of practice
The adoption of digital pathology into routine histopathology practice is not without its problems in terms of implementation, changing work practices and staff acceptance. Here, Pathology in Practice Science Editor Brian Nation compiles a selection of research devoted to such issues, and also examples of the value this technology offers to patient care and outcome.
The digital revolution in pathology: Towards a smarter approach to research and treatment. Tucci F, Laurinavicius A, Kather JN, Eloy C. Tumori. 2024 Apr 12:3008916241231035. doi: 10.1177/03008916241231035. Online ahead of print.
Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. Here, the authors delve into the
current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. They discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.
Multiple instance learning for digital pathology: A review of the state-of-the- art, limitations & future potential Gadermayr M, Tschuchnig M. Comput Med Imaging Graph. 2024 Mar; 112:
102337. doi: 10.1016/
j.compmedimag.2024.102337.
Digital whole-slide images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of digital pathology. However, a limitation is given by the fact that typical deep learning algorithms require (manual) annotations in addition to the large amounts of image data, to enable effective training.
Multiple instance learning exhibits a powerful tool for training deep neural networks in a scenario without fully annotated data. These methods are particularly effective in the domain of digital pathology, due to the fact that labels for whole-slide images are often captured routinely, whereas labels for patches, regions, or pixels are not. This potential resulted in a considerable number of publications, with the vast majority published in the last four years. Besides the availability of digitised data and a high motivation from the medical perspective, the availability of powerful graphics processing units exhibits an accelerator in this field. In this paper, the authors provide an overview of widely and effectively used concepts of (deep) multiple instance learning approaches and recent advancements. They also critically discuss remaining challenges as well as future potential.
Complete digital pathology transition: A large multi-center experience Samueli B, Aizenberg N, Shaco-Levy R
WWW.PATHOLOGYINPRACTICE.COM MAY 2024
et al. Pathol Res Pract. 2024 Jan; 253: 155028. doi: 10.1016/
j.prp.2023.155028.
Transitioning from glass slide pathology to digital pathology for primary diagnostics requires an appropriate laboratory information system, an image management system, and slide scanners; it also reinforces the need for sophisticated pathology informatics including synoptic reporting. Previous reports have discussed the transition itself and relevant considerations for it, but not the selection criteria and considerations for the infrastructure. Here, the authors describe the
process used to evaluate slide scanners, image management systems, and synoptic reporting systems for a large multisite institution.
Six network hospitals evaluated six slide scanners, three image management systems, and three synoptic reporting systems. Scanners were evaluated based on the quality of image, speed, ease of operation, and special capabilities (including z-stacking, fluorescence and others). Image management and synoptic reporting systems were evaluated for their ease of use and capacity.
Among the scanners evaluated, the Leica GT450 produced the highest quality images, while the 3DHistech Pannoramic provided fluorescence and superior z-stacking. The newest generation of scanners, released relatively recently, performed better than slightly older scanners from major manufacturers. Although the Olympus VS200 was not fully vetted due to not meeting all inclusion criteria, it is discussed herein due to its exceptional versatility. For Image Management Software, the
51
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60
