The Experts below are selected from a list of 9021 Experts worldwide ranked by ideXlab platform
Anant Madabhushi - One of the best experts on this subject based on the ideXlab platform.
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Digital Pathology and computational image analysis in nephroPathology.
Nature Reviews Nephrology, 2020Co-Authors: Laura Barisoni, Anant Madabhushi, Kyle Lafata, Stephen M. Hewitt, Ulysses J. BalisAbstract:The emergence of Digital Pathology - an image-based environment for the acquisition, management and interpretation of Pathology information supported by computational techniques for data extraction and analysis - is changing the Pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate Digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal Pathology is just entering the Digital era, with the establishment of consortia and Digital Pathology repositories for the collection, analysis and integration of Pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing Pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.
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Artificial intelligence in Digital Pathology — new tools for diagnosis and precision oncology
Nature Reviews Clinical Oncology, 2019Co-Authors: Kaustav Bera, Vamsidhar Velcheti, Kurt A Schalper, David L Rimm, Anant MadabhushiAbstract:In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in Digital Pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for Digital Pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for Digital Pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
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artificial intelligence in Digital Pathology new tools for diagnosis and precision oncology
Nature Reviews Clinical Oncology, 2019Co-Authors: Kaustav Bera, Vamsidhar Velcheti, Kurt A Schalper, David L Rimm, Anant MadabhushiAbstract:In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in Digital Pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for Digital Pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology. The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for Digital Pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
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Image analysis and machine learning in Digital Pathology: Challenges and opportunities
Medical Image Analysis, 2016Co-Authors: Anant Madabhushi, George LeeAbstract:With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived Digitally. While Digital Pathology has substantial implications for telePathology, second opinions, and education there are also huge research opportunities in image computing with this new source of “big data”. It is well known that there is fundamental prognostic data embedded in Pathology images. The ability to mine “sub-visual” image features from Digital Pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big Digital Pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from Digital Pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of Digital Pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and Pathology images and also combining Digital Pathology derived image measurements with molecular “omics” features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histoPathology.
Darren Treanor - One of the best experts on this subject based on the ideXlab platform.
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A Point-of-Use Quality Assurance Tool for Digital Pathology Remote Working
Journal of Pathology Informatics, 2020Co-Authors: Alexander Wright, Darren Treanor, Bethany Jill Williams, Emily L. Clarke, Catriona M Dunn, David BrettleAbstract:Pathology services are facing pressures due to the COVID-19 pandemic. Digital Pathology has the capability to meet some of these unprecedented challenges by allowing remote diagnoses to be made at home, during periods of social distancing or self-isolation. However, while Digital Pathology allows diagnoses to be made on standard computer screens, unregulated home environments may not be conducive for optimal viewing conditions. There is also a paucity of experimental evidence available to support the minimum display requirements for Digital Pathology. This study presents a Point-of-Use Quality Assurance (POUQA) tool for remote assessment of viewing conditions for reporting Digital Pathology slides. The tool is a psychophysical test combining previous work from successfully implemented quality assurance tools in both Pathology and radiology to provide a minimally intrusive display screen validation task, before viewing Digital slides. The test is specific to Pathology assessment in that it requires visual discrimination between colors derived from hematoxylin and eosin staining, with a perceptual difference of ±1 delta E (dE). This tool evaluates the transfer of a 1 dE signal through the Digital image display chain, including the observers' contrast and color responses within the test color range. The web-based system has been rapidly developed and deployed as a response to the COVID-19 pandemic and may be used by anyone in the world to help optimize flexible working conditions at: http://www. virtualPathology.leeds.ac.uk/res earch/systems/pouqa/.
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Practical guide to training and validation for primary diagnosis with Digital Pathology.
Journal of Clinical Pathology, 2019Co-Authors: Bethany Jill Williams, Darren TreanorAbstract:Numerous clinical Pathology departments are deploying or planning to deploy Digital Pathology systems for all or part of their diagnostic output. Digital Pathology is an evolving technology, and it is important that departments uphold or improve on current standards. Leeds Teaching Hospitals NHS Trust has been scanning 100% of histology slides since September 2018. In this practical paper, we will share our approach to training and validation, which has been incorporated into the Royal College of Pathologists’ guidance for Digital Pathology implementation. We will offer an overview of the Royal College endorsed training and validation protocol and the evidence base on which it is based. We will provide practical advice on implementation of the protocol and highlight areas of Digital reporting that can prove difficult for the novice Digital pathologist. In addition, we will share a detailed topographical list of types of diagnostic tasks and features which should form the basis of Digital slide training sets.
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Maintaining quality diagnosis with Digital Pathology: a practical guide to ISO 15189 accreditation.
Journal of Clinical Pathology, 2019Co-Authors: Bethany Jill Williams, Chloe Knowles, Darren TreanorAbstract:An ever-increasing number of clinical Pathology departments are deploying, or planning to deploy Digital Pathology systems for all, or part of their diagnostic output. Digital Pathology is an evolving technology, and it is important that departments uphold or improve on current standards. Leeds Teaching Hospitals NHS Trust has been scanning 100% of histology slides since September 2018, and has developed validation and validation protocols to train 38 histoPathology consultants in primary Digital diagnosis. In this practical paper, we will share our approach to ISO inspection of our Digital Pathology service, which resulted in successful ISO accreditation for primary Digital diagnosis. We will offer practical advice on what types of procedure and documentation are necessary, both from the point of view of the laboratory and your reporting pathologists. We will explore topics including risk assessment, standard operating procedures, validation and training, calibration and quality assurance, and provide a checklist of the key Digital Pathology components you need to consider in your inspection preparations. The continuous quest for quality and safety improvements in our practice should underpin everything we do in Pathology, including our Digital Pathology operations. We hope this publication will make it easier for subsequent departments to successfully achieve ISO 15189 accreditation and feel confident in their Digital Pathology services.
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Future-proofing Pathology part 2: building a business case for Digital Pathology.
Journal of Clinical Pathology, 2018Co-Authors: Bethany Jill Williams, David Bottoms, David Clark, Darren TreanorAbstract:Diagnostic histoPathology departments are experiencing unprecedented economic and service pressures, and many institutions are now considering Digital Pathology as part of the solution. In this document, a follow on to our case for adoption report, we provide information and advice to help departments create their own clear, succinct, individualised business case for the clinical deployment of Digital Pathology.
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Future-proofing Pathology: the case for clinical adoption of Digital Pathology.
Journal of Clinical Pathology, 2017Co-Authors: Bethany Jill Williams, David Bottoms, Darren TreanorAbstract:This document clarifies the strategic context of Digital Pathology adoption, defines the different use cases a healthcare provider may wish to consider as part of a Digital adoption and summarises existing reasons for Digital adoption and its potential benefits. The reader is provided with references to the relevant literature, and illustrative case studies. The authors hope this report will be of interest to healthcare providers, Pathology managers, departmental heads, pathologists and biomedical scientists that are considering Digital Pathology, deployments or preparing business cases for Digital Pathology adoption in clinical settings. The information contained in this document can be shared and used in any documentation the reader wishes to present for their own institutional case for adoption report or business case.
Kaustav Bera - One of the best experts on this subject based on the ideXlab platform.
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Artificial intelligence in Digital Pathology — new tools for diagnosis and precision oncology
Nature Reviews Clinical Oncology, 2019Co-Authors: Kaustav Bera, Vamsidhar Velcheti, Kurt A Schalper, David L Rimm, Anant MadabhushiAbstract:In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in Digital Pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for Digital Pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for Digital Pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
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artificial intelligence in Digital Pathology new tools for diagnosis and precision oncology
Nature Reviews Clinical Oncology, 2019Co-Authors: Kaustav Bera, Vamsidhar Velcheti, Kurt A Schalper, David L Rimm, Anant MadabhushiAbstract:In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in Digital Pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for Digital Pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology. The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for Digital Pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
Liron Pantanowitz - One of the best experts on this subject based on the ideXlab platform.
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Digital Pathology: Review of current opportunities and challenges for oral pathologists.
Journal of Oral Pathology & Medicine, 2019Co-Authors: Yingci Liu, Liron PantanowitzAbstract:Whole slide imaging (WSI) has impacted the practice of Pathology in the arenas of education, clinical practice, and research. With Digital slides, pathologists can circumvent the limitations of traditional glass. Presently, Digital Pathology is primarily utilized for second opinion consults, clinical conferences, and education at select academic medical centers, with its mainstream adoption on the rise. However, challenges of adoption for oral pathologists are unique given the highly specialized nature of their work. The hurdles include the high-cost instrumentation and regular maintenance, need for additional training, changes in traditional workflow, and integration with present software. Given these barriers, it remains unclear the extent to which slide scanning and virtual Pathology should be adopted by oral pathologists at this conjuncture. This review seeks to shed light on the current state of WSI and analyzes the opportunities and challenges for oral Pathology in the rapidly evolving field of Digital Pathology.
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Artificial intelligence and Digital Pathology: Challenges and opportunities
Journal of Pathology Informatics, 2018Co-Authors: Hamid R. Tizhoosh, Liron PantanowitzAbstract:In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in Digital Pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational Pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in Digital Pathology from both engineering and Pathology perspectives.
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Enterprise Implementation of Digital Pathology: Feasibility, Challenges, and Opportunities.
Journal of Digital Imaging, 2017Co-Authors: Douglas J Hartman, Anthony Piccoli, Liron Pantanowitz, Jeff Mchugh, Matthew J O'leary, Gonzalo Romero LauroAbstract:Digital Pathology is becoming technically possible to implement for routine Pathology work. At our institution, we have been using Digital Pathology for second opinion intraoperative consultations for over 10 years. Herein, we describe our experience in converting to a Digital Pathology platform for primary Pathology diagnosis. We implemented an incremental rollout for Digital Pathology on subspecialty benches, beginning with cases that contained small amounts of tissue (biopsy specimens). We successfully scanned over 40,000 slides through our Digital Pathology system. Several lessons (both challenges and opportunities) were learned through this implementation. A successful conversion to Digital Pathology requires pre-imaging adjustments, integrated software and post-imaging evaluations.
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Strategies And Demands For Digital Pathology Workflow Integration
Diagnostic Pathology, 2016Co-Authors: Liron PantanowitzAbstract:Digital Pathology has many benefits and Pathology laboratories around the world are capitalizing on many of these applications including education, telePathology, and image analysis. However, if not implemented well, Digital Pathology can have both positive and negative impacts on workflow. The key is to ensure that the Digital imaging solution selected overall enhances workflow. Batched scanning, failed scans and downtime are examples where whole slide imaging can negatively impact workflow. High speed digitization, load balancing, and smart algorithms on the other hand can all improve workflow. Optimal image management and integration with the laboratory information system are also essential for sustaining an efficient Digital Pathology workflow. The aim of this talk is to address many of these critical factors, their impact on Digital Pathology workflow, and to discuss novel opportunities that by enhancing workflow will help evolve the practice of Pathology.
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Comparison of the diagnostic utility of Digital Pathology systems for telemicrobiology.
Journal of Pathology Informatics, 2016Co-Authors: Daniel D. Rhoads, Douglas J Hartman, Nadia Habib-bein, Rahman Hariri, Sara E. Monaco, Andrew Lesniak, Jon Duboy, Mohamed E. Salama, Liron PantanowitzAbstract:Introduction: Telemicrobiology is a growing component of clinical microbiology informatics. However, few studies have been performed to assess the diagnostic utility of telemicroscopy systems in evaluating infectious agents. Objective: Evaluate multiple contemporary Digital Pathology platforms for use in diagnostic telemicrobiology. Materials and Methods: A mix of thirty cases that included viral, bacterial, fungal, and parasitological findings were evaluated by four experts using ×40 whole slide imaging (WSI) scans, ×83 oil-immersion WSI scans, ×100 oil-immersion WSI scans, Digital photomicrographs, and glass slides. Results: The ×83 WSI, ×100 WSI, and photomicrograph interpretations were not significantly different in quality and accuracy when compared to glass slide interpretations. The ×40 WSI interpretations were of lower quality and were more likely to be incorrect when compared to glass slide interpretations. Conclusions: In this study, high magnification, oil-immersion Digital Pathology platforms are better suited to support telemicrobiology applications and yield interpretations on par with glass slide evaluations.
Kurt A Schalper - One of the best experts on this subject based on the ideXlab platform.
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Artificial intelligence in Digital Pathology — new tools for diagnosis and precision oncology
Nature Reviews Clinical Oncology, 2019Co-Authors: Kaustav Bera, Vamsidhar Velcheti, Kurt A Schalper, David L Rimm, Anant MadabhushiAbstract:In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in Digital Pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for Digital Pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for Digital Pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
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artificial intelligence in Digital Pathology new tools for diagnosis and precision oncology
Nature Reviews Clinical Oncology, 2019Co-Authors: Kaustav Bera, Vamsidhar Velcheti, Kurt A Schalper, David L Rimm, Anant MadabhushiAbstract:In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in Digital Pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for Digital Pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology. The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for Digital Pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.