Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review

Patel, Ankush; Balis, Ulysses; Cheng, Jerome; Li, Zaibo; Lujan, Giovanni; McClintock, David S; Pantanowitz, Liron; Parwani, Anil V.

doi:10.4103/jpi.jpi_66_21

Cited by 59 publications

(69 citation statements)

References 74 publications

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“…In addition, the diagnosis can be made using a computer, and the process becomes straightforward as the intermediate process is omitted. With the commercialization of high-speed, high-capacity digital scanners, the world's leading university hospitals have actively introduced digital scanners to digitize pathological slides [6]. It is expected that digital scanners with more diverse functions will be developed in the future, and as a result, the price of scanners is expected to be levelled down due to competition.…”

Section: The Future We Dreammentioning

confidence: 99%

Can Artificial Intelligence Help?

2022

Journal of Experimental Pathology

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Section: The Future We Dreammentioning

confidence: 99%

Can Artificial Intelligence Help?

2022

Journal of Experimental Pathology

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“…Issues that had lingered throughout many departments were fervently exacerbated, e.g., specialist deficits and demands of shorter turnaround times (TAT) amidst increasing caseloads and complexity of pathology reports for aging patient demographics harboring higher disease incidence. New solutions were necessitated upon the exhumation of long withstanding problems [ 2 , 3 ]. Diagnostic surgical pathology remains the ‘gold standard’ for cancer diagnosis despite substantial inter-observer variability from human error, e.g., bias and fatigue, leading to misdiagnosis of challenging histological patterns and missed identification of small quantities of cancer within biopsy material.…”

Section: Introductionmentioning

confidence: 99%

“…Batch-scanning and continuous or random-access processing capabilities enabling the concurrent uploading of glass slides during the image capture and digitization processes of others have improved laboratory efficiency [ 4 , 5 ]. Many WSI devices can now handle an array of mediums cast on slides of varying dimensions, with single slide load capacity of some devices reaching up to 1000 [ 2 ]. WSI scanning cameras and image sensors deliver superior sensitivity, resolution, field-of-view (FOV), and frame rates for optimal capture and digitization of glass slide specimens [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Many WSI devices can now handle an array of mediums cast on slides of varying dimensions, with single slide load capacity of some devices reaching up to 1000 [ 2 ]. WSI scanning cameras and image sensors deliver superior sensitivity, resolution, field-of-view (FOV), and frame rates for optimal capture and digitization of glass slide specimens [ 2 ]. Newer scientific CMOS (sCMOS) sensors are featured in many current WSI scanning devices, often as adjunctive to multiple CCD and CMOS sensors for optimization of image quality.…”

Section: Introductionmentioning

confidence: 99%

“…Augmentation of DP tools with artificial intelligence (AI)-based algorithms reflect another. As the university’s primary diagnostic novelty recedes amongst a growing global normalcy of automated workflows, increased efforts for diagnostic quality, and the creation of integrated ecosystems supportive of computational pathology [ 2 , 11 , 12 , 13 ], further capitalization from digital integration is now within reach for digitized departments primed to actuate the clinical potential of predictive diagnostic AI-technology for WSI.…”

Section: Introductionmentioning

confidence: 99%

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Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Patel

Shaker

Mohanty³

et al. 2022

Diagnostics

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Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

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Development of a multi‐scanner facility for data acquisition for digital pathology artificial intelligence

Humphries,

Kaye,

Stankeviciute

et al. 2024

The Journal of Pathology

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Whole slide imaging (WSI) of pathology glass slides using high‐resolution scanners has enabled the large‐scale application of artificial intelligence (AI) in pathology, to support the detection and diagnosis of disease, potentially increasing efficiency and accuracy in tissue diagnosis. Despite the promise of AI, it has limitations. ‘Brittleness’ or sensitivity to variation in inputs necessitates that large amounts of data are used for training. AI is often trained on data from different scanners but not usually by replicating the same slide across scanners. The utilisation of multiple WSI instruments to produce digital replicas of the same slides will make more comprehensive datasets and may improve the robustness and generalisability of AI algorithms as well as reduce the overall data requirements of AI training. To this end, the National Pathology Imaging Cooperative (NPIC) has built the AI FORGE (Facilitating Opportunities for Robust Generalisable data Emulation), a unique multi‐scanner facility embedded in a clinical site in the NHS to (1) compare scanner performance, (2) replicate digital pathology image datasets across WSI systems, and (3) support the evaluation of clinical AI algorithms. The NPIC AI FORGE currently comprises 15 scanners from nine manufacturers. It can generate approximately 4,000 WSI images per day (approximately 7 TB of image data). This paper describes the process followed to plan and build such a facility. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review

Cited by 59 publications

References 74 publications

Can Artificial Intelligence Help?

Can Artificial Intelligence Help?

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Development of a multi‐scanner facility for data acquisition for digital pathology artificial intelligence

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