An industry perspective: An update on the adoption of whole slide imaging

Montalto, Michael

doi:10.4103/2153-3539.180014

Cited by 23 publications

(15 citation statements)

References 16 publications

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“…Reimbursement of the costs of AI‐based diagnostic and prognostic/predictive assays is one of the major issues that affect the application of these assays in clinic [91]. In the USA, insurance companies standardize expenses on the basis of the current procedural terminology codes maintained by the American Medical Association and reported by medical professionals [92‐95]. At present, there are no dedicated procedure codes for the use of AI in digital pathology with diagnostic or prognostic intent.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

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

Jiang

Yue

Wang

et al. 2020

Cancer Communications

269

147

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

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Section: Challenges and Perspectivesmentioning

confidence: 99%

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

Jiang

Yue

Wang

et al. 2020

Cancer Communications

269

147

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“…One area of concern is pathologists' apparent lack of enthusiasm for performing their work functioning as telepathologists. [187][188][189][190][191][192][193][194][195] Where significant travel can be avoided, the technology sparks some interest. 10,11,196 However, this represents a very small fraction of what surgical pathologists do in their everyday practices.…”

Section: Concluding Commentsmentioning

confidence: 99%

The Empirical Foundations of Telepathology: Evidence of Feasibility and Intermediate Effects

Bashshur

Krupiński

Weinstein

et al. 2017

Telemedicine and e-Health

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The evidence regarding feasibility/acceptance of telepathology and related information technology applications has been well documented for several decades. The majority of evidentiary studies focused on intermediate outcomes, as indicated by comparability between telepathology and conventional light microscopy. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include use of telepathology and whole slide imaging for teaching, research, and outreach to resource-limited countries. Challenges still exist, however, in terms of use of telepathology as an effective diagnostic modality in clinical practice.

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“…Yet, despite evidence that image analysis improves IHC biomarker scoring accuracy and reproducibility in tumours81015, the adoption of computer-aided diagnosis by pathologists has remained limited in practice. This can be explained by limited evidence of added clinical value and by the surplus of time required to predefine tumour regions in the tissue sample16. Recently, deep learning techniques have dramatically improved the ability of computers to recognize objects in images17 raising the possibility for fully automated computer-aided diagnosis.…”

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confidence: 99%

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

Vandenberghe

Scott

Scorer

et al. 2017

Sci Rep

171

109

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Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour resection samples, automated scoring showed a concordance of 83% with a pathologist. The twelve discordant cases were then independently reviewed, leading to a modification of diagnosis from initial pathologist assessment for eight cases. Diagnostic discordance was found to be largely caused by perceptual differences in assessing HER2 expression due to high HER2 staining heterogeneity. This study provides evidence that deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying cases at high risk of misdiagnosis.

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An industry perspective: An update on the adoption of whole slide imaging

Cited by 23 publications

References 16 publications

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

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

The Empirical Foundations of Telepathology: Evidence of Feasibility and Intermediate Effects

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

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