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Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides. Mod Pathol 2022; 35:1791-1803. [PMID: 36198869 PMCID: PMC9532237 DOI: 10.1038/s41379-022-01161-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 12/24/2022]
Abstract
To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model (R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100-2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.
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Abstract
Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of WSI systems as class II medical devices. In response to new market forces and recent technology advances outside of pathology, a new field of computational pathology has emerged that applies artificial intelligence (AI) and machine learning algorithms to WSIs. Computational pathology has great potential for augmenting pathologists' accuracy and efficiency, but there are important concerns regarding trust of AI due to the opaque, black-box nature of most AI algorithms. In addition, there is a lack of consensus on how pathologists should incorporate computational pathology systems into their workflow. To address these concerns, building computational pathology systems with explainable AI (xAI) mechanisms is a powerful and transparent alternative to black-box AI models. xAI can reveal underlying causes for its decisions; this is intended to promote safety and reliability of AI for critical tasks such as pathology diagnosis. This article outlines xAI enabled applications in anatomic pathology workflow that improves efficiency and accuracy of the practice. In addition, we describe HistoMapr-Breast, an initial xAI enabled software application for breast core biopsies. HistoMapr-Breast automatically previews breast core WSIs and recognizes the regions of interest to rapidly present the key diagnostic areas in an interactive and explainable manner. We anticipate xAI will ultimately serve pathologists as an interactive computational guide for computer-assisted primary diagnosis.
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Tosun AB, Pullara F, Becich MJ, Taylor DL, Chennubhotla SC, Fine JL. HistoMapr™: An Explainable AI (xAI) Platform for Computational Pathology Solutions. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR DIGITAL PATHOLOGY 2020. [DOI: 10.1007/978-3-030-50402-1_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am J Surg Pathol 2019; 42:1636-1646. [PMID: 30312179 PMCID: PMC6257102 DOI: 10.1097/pas.0000000000001151] [Citation(s) in RCA: 253] [Impact Index Per Article: 50.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Supplemental Digital Content is available in the text. Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clinical care. To evaluate the potential impact of digital assistance on interpretation of digitized slides, we conducted a multireader multicase study utilizing our deep learning algorithm for the detection of breast cancer metastasis in lymph nodes. Six pathologists reviewed 70 digitized slides from lymph node sections in 2 reader modes, unassisted and assisted, with a wash-out period between sessions. In the assisted mode, the deep learning algorithm was used to identify and outline regions with high likelihood of containing tumor. Algorithm-assisted pathologists demonstrated higher accuracy than either the algorithm or the pathologist alone. In particular, algorithm assistance significantly increased the sensitivity of detection for micrometastases (91% vs. 83%, P=0.02). In addition, average review time per image was significantly shorter with assistance than without assistance for both micrometastases (61 vs. 116 s, P=0.002) and negative images (111 vs. 137 s, P=0.018). Lastly, pathologists were asked to provide a numeric score regarding the difficulty of each image classification. On the basis of this score, pathologists considered the image review of micrometastases to be significantly easier when interpreted with assistance (P=0.0005). Utilizing a proof of concept assistant tool, this study demonstrates the potential of a deep learning algorithm to improve pathologist accuracy and efficiency in a digital pathology workflow.
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Whole slide imaging equivalency and efficiency study: experience at a large academic center. Mod Pathol 2019; 32:916-928. [PMID: 30778169 DOI: 10.1038/s41379-019-0205-0] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Revised: 12/13/2018] [Accepted: 12/14/2018] [Indexed: 11/08/2022]
Abstract
Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day's routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the "MSK Slide Viewer". Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.
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Farahani N, Liu Z, Jutt D, Fine JL. Pathologists' Computer-Assisted Diagnosis: A Mock-up of a Prototype Information System to Facilitate Automation of Pathology Sign-out. Arch Pathol Lab Med 2017; 141:1413-1420. [DOI: 10.5858/arpa.2016-0214-oa] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Context.—
Pathologists' computer-assisted diagnosis (pCAD) is a proposed framework for alleviating challenges through the automation of their routine sign-out work. Currently, hypothetical pCAD is based on a triad of advanced image analysis, deep integration with heterogeneous information systems, and a concrete understanding of traditional pathology workflow. Prototyping is an established method for designing complex new computer systems such as pCAD.
Objective.—
To describe, in detail, a prototype of pCAD for the sign-out of a breast cancer specimen.
Design.—
Deidentified glass slides and data from breast cancer specimens were used. Slides were digitized into whole-slide images with an Aperio ScanScope XT, and screen captures were created by using vendor-provided software. The advanced workflow prototype was constructed by using PowerPoint software.
Results.—
We modeled an interactive, computer-assisted workflow: pCAD previews whole-slide images in the context of integrated, disparate data and predefined diagnostic tasks and subtasks. Relevant regions of interest (ROIs) would be automatically identified and triaged by the computer. A pathologist's sign-out work would consist of an interactive review of important ROIs, driven by required diagnostic tasks. The interactive session would generate a pathology report automatically.
Conclusions.—
Using animations and real ROIs, the pCAD prototype demonstrates the hypothetical sign-out in a stepwise fashion, illustrating various interactions and explaining how steps can be automated. The file is publicly available and should be widely compatible. This mock-up is intended to spur discussion and to help usher in the next era of digitization for pathologists by providing desperately needed and long-awaited automation.
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Nguyen L, Tosun AB, Fine JL, Lee AV, Taylor DL, Chennubhotla SC. Spatial Statistics for Segmenting Histological Structures in H&E Stained Tissue Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1522-1532. [PMID: 28328502 PMCID: PMC5498226 DOI: 10.1109/tmi.2017.2681519] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. This paper focuses on segmenting histological structures in hematoxylin- and eosin-stained images of breast tissues, e.g., invasive carcinoma, carcinoma in situ, atypical and normal ducts, adipose tissue, and lymphocytes. We propose two graph-theoretic segmentation methods based on local spatial color and nuclei neighborhood statistics. For benchmarking, we curated a data set of 232 high-power field breast tissue images together with expertly annotated ground truth. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.) over the remaining connective tissue and non-tissue areas in ground truth annotations, we propose a new region-based score for evaluating segmentation algorithms. We demonstrate the improvement of our proposed methods over the state-of-the-art algorithms in both region- and boundary-based performance measures.
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Griffin J, Treanor D. Digital pathology in clinical use: where are we now and what is holding us back? Histopathology 2016; 70:134-145. [DOI: 10.1111/his.12993] [Citation(s) in RCA: 149] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Jon Griffin
- Sheffield NHS Foundation Trust; Sheffield UK
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Cervin I, Molin J, Lundström C. Improving the creation and reporting of structured findings during digital pathology review. J Pathol Inform 2016; 7:32. [PMID: 27563491 PMCID: PMC4977970 DOI: 10.4103/2153-3539.186917] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 04/22/2016] [Indexed: 12/02/2022] Open
Abstract
Background: Today, pathology reporting consists of many separate tasks, carried out by multiple people. Common tasks include dictation during case review, transcription, verification of the transcription, report distribution, and report the key findings to follow-up registries. Introduction of digital workstations makes it possible to remove some of these tasks and simplify others. This study describes the work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Methods: We explored the possibility to have a digital tool that simplifies image review by assisting note-taking, and with minimal extra effort, populates a structured report. Thus, our prototype sees reporting as an activity interleaved with image review rather than a separate final step. We created an interface to collect, sort, and display findings for the most common reporting needs, such as tumor size, grading, and scoring. Results: The interface was designed to reduce the need to retain partial findings in the head or on paper, while at the same time be structured enough to support automatic extraction of key findings for follow-up registry reporting. The final prototype was evaluated with two pathologists, diagnosing complicated partial mastectomy cases. The pathologists experienced that the prototype aided them during the review and that it created a better overall workflow. Conclusions: These results show that it is feasible to simplify the reporting tasks in a way that is not distracting, while at the same time being able to automatically extract the key findings. This simplification is possible due to the realization that the structured format needed for automatic extraction of data can be used to offload the pathologists’ working memory during the diagnostic review.
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Affiliation(s)
- Ida Cervin
- Sectra AB, Chalmers University of Technology, Sectra AB, Gothenburg, Sweden
| | - Jesper Molin
- Center for Medical Image Science and Visualization, Chalmers University of Technology, Sectra AB, Gothenburg, Sweden
| | - Claes Lundström
- Center for Medical Image Science and Visualization, Linköping University, Sectra AB, Linköping, Sweden
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Rosenberg AZ, Palmer M, Merlino L, Troost JP, Gasim A, Bagnasco S, Avila-Casado C, Johnstone D, Hodgin JB, Conway C, Gillespie BW, Nast CC, Barisoni L, Hewitt SM. The Application of Digital Pathology to Improve Accuracy in Glomerular Enumeration in Renal Biopsies. PLoS One 2016; 11:e0156441. [PMID: 27310011 PMCID: PMC4911144 DOI: 10.1371/journal.pone.0156441] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Accepted: 05/14/2016] [Indexed: 12/02/2022] Open
Abstract
Background In renal biopsy reporting, quantitative measurements, such as glomerular number and percentage of globally sclerotic glomeruli, is central to diagnostic accuracy and prognosis. The aim of this study is to determine the number of glomeruli and percent globally sclerotic in renal biopsies by means of registration of serial tissue sections and manual enumeration, compared to the numbers in pathology reports from routine light microscopic assessment. Design We reviewed 277 biopsies from the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository, enumerating 9,379 glomeruli by means of whole slide imaging. Glomerular number and the percentage of globally sclerotic glomeruli are values routinely recorded in the official renal biopsy pathology report from the 25 participating centers. Two general trends in reporting were noted: total number per biopsy or average number per level/section. Both of these approaches were assessed for their accuracy in comparison to the analogous numbers of annotated glomeruli on WSI. Results The number of glomeruli annotated was consistently higher than those reported (p<0.001); this difference was proportional to the number of glomeruli. In contrast, percent globally sclerotic were similar when calculated on total glomeruli, but greater in FSGS when calculated on average number of glomeruli (p<0.01). The difference in percent globally sclerotic between annotated and those recorded in pathology reports was significant when global sclerosis is greater than 40%. Conclusions Although glass slides were not available for direct comparison to whole slide image annotation, this study indicates that routine manual light microscopy assessment of number of glomeruli is inaccurate, and the magnitude of this error is proportional to the total number of glomeruli.
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MESH Headings
- Biopsy
- Glomerulonephritis, IGA/diagnostic imaging
- Glomerulonephritis, IGA/pathology
- Glomerulonephritis, IGA/surgery
- Glomerulonephritis, Membranous/diagnostic imaging
- Glomerulonephritis, Membranous/pathology
- Glomerulonephritis, Membranous/surgery
- Glomerulosclerosis, Focal Segmental/diagnostic imaging
- Glomerulosclerosis, Focal Segmental/pathology
- Glomerulosclerosis, Focal Segmental/surgery
- Humans
- Kidney Glomerulus/diagnostic imaging
- Kidney Glomerulus/pathology
- Kidney Glomerulus/surgery
- Microscopy/methods
- Nephrotic Syndrome/diagnostic imaging
- Nephrotic Syndrome/pathology
- Nephrotic Syndrome/surgery
- Signal Processing, Computer-Assisted
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Affiliation(s)
- Avi Z. Rosenberg
- Department of Pathology, Children’s National Medical Center, Washington, DC, United States of America
- National Institute of Digestive Diseases and Kidney, National Institutes of Health, Bethesda, MD, United States of America
| | - Matthew Palmer
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Lino Merlino
- Department of Pathology, University of Miami, Miami, FL, United States of America
| | - Jonathan P. Troost
- Department of Pediatrics, Division of Pediatric Nephrology, University of Michigan, Ann Arbor, MI, United States of America
| | - Adil Gasim
- Department of Pathology, University of North Carolina, Chapel Hill, NC, United States of America
| | - Serena Bagnasco
- Department of Pathology, The Johns Hopkins School of Medicine, Baltimore, MD, United States of America
| | | | - Duncan Johnstone
- Department of Medicine, Section of Nephrology, Hypertension and Kidney Transplantation Temple University, Philadelphia, PA, United States of America
| | - Jeffrey B. Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States of America
| | - Catherine Conway
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America
| | - Brenda W. Gillespie
- Biostatistics Department, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - Cynthia C. Nast
- Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Laura Barisoni
- Department of Pathology, University of Miami, Miami, FL, United States of America
| | - Stephen M. Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America
- * E-mail:
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Guo H, Birsa J, Farahani N, Hartman DJ, Piccoli A, O'Leary M, McHugh J, Nyman M, Stratman C, Kvarnstrom V, Yousem S, Pantanowitz L. Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out. J Pathol Inform 2016; 7:23. [PMID: 27217973 PMCID: PMC4872480 DOI: 10.4103/2153-3539.181767] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Accepted: 04/06/2016] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND The adoption of digital pathology offers benefits over labor-intensive, time-consuming, and error-prone manual processes. However, because most workflow and laboratory transactions are centered around the anatomical pathology laboratory information system (APLIS), adoption of digital pathology ideally requires integration with the APLIS. A digital pathology system (DPS) integrated with the APLIS was recently implemented at our institution for diagnostic use. We demonstrate how such integration supports digital workflow to sign-out anatomical pathology cases. METHODS Workflow begins when pathology cases get accessioned into the APLIS (CoPathPlus). Glass slides from these cases are then digitized (Omnyx VL120 scanner) and automatically uploaded into the DPS (Omnyx(®) Integrated Digital Pathology (IDP) software v.1.3). The APLIS transmits case data to the DPS via a publishing web service. The DPS associates scanned images with the correct case using barcode labels on slides and information received from the APLIS. When pathologists remotely open a case in the DPS, additional information (e.g. gross pathology details, prior cases) gets retrieved from the APLIS through a query web service. RESULTS Following validation of this integration, pathologists at our institution have signed out more than 1000 surgical pathology cases in a production environment. Integration between the APLIS and DPS enabled pathologists to review digital slides while simultaneously having access to pertinent case metadata. The introduction of a digital workflow eliminated costly manual tasks involving matching of glass slides and avoided delays waiting for glass slides to be delivered. CONCLUSION Integrating the DPS and APLIS were instrumental for successfully implementing a digital solution at our institution for pathology sign-out. The integration streamlined our digital sign-out workflow, diminished the potential for human error related to matching slides, and improved the sign-out experience for pathologists.
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Affiliation(s)
- Huazhang Guo
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Joe Birsa
- Omnyx, LLC, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Navid Farahani
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Douglas J Hartman
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anthony Piccoli
- Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Matthew O'Leary
- Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jeffrey McHugh
- Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Mark Nyman
- Omnyx, LLC, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Curtis Stratman
- Omnyx, LLC, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Vanja Kvarnstrom
- Omnyx, LLC, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Samuel Yousem
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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Abstract
Telepathology is the practice of remote pathology using telecommunication links to enable the electronic transmission of digital pathology images. Telepathology can be used for remotely rendering primary diagnoses, second opinion consultations, quality assurance, education, and research purposes. The use of telepathology for clinical patient care has been limited mostly to large academic institutions. Barriers that have limited its widespread use include prohibitive costs, legal and regulatory issues, technologic drawbacks, resistance from pathologists, and above all a lack of universal standards. This article provides an overview of telepathology technology and applications.
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Affiliation(s)
- Navid Farahani
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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