Dawn of artificial intelligence -Enable digital Pathology in Pakistan-A paradigm shift

2021 ◽  
pp. 1-5
Author(s):  
Talat Zehra ◽  
Asma Shaikh ◽  
Maheen Shams
Keyword(s):  
Artificial Intelligence ◽  
Data Storage ◽  
Digital Pathology ◽  
Whole Slide Imaging ◽  
Storage Devices ◽  
New Horizons ◽  
Novel Technology ◽  
The World ◽  
Slide Image ◽  
Computational Pathology

Pathology particularly histopathology is considered to be a busy and challenging field. It is considered as gold standard for the diagnosis and management of patient particularly in cases of tumor. It has been more than twenty years since the introduction of whole slide imaging (WSI) in the developed part of the world. Various whole slide image (WSI) devices and use of artificial intelligence (AI) based softwares have transformed the field of Pathology1. Digital pathology is a novel technology and currently being implemented in most of the developed part of the world.2 Once the patient’s data becomes digital, it is easily stored, reproducible on a single click and quality remains same. This data can be used to make disease models, disease trends and predict the outcome of a particular disease through data mining which will open new horizons of precise medicine. The use of WSI with computational pathology and data storage devices have revolutionized the working in histopathology. The world witnessed an exponential rise in its adoption particularly after Covid-19 pandemic1. However, in the developing world either it is not being implemented or its use is still sub-optimal. By realizing the potential of digital and computational pathology along with the use of artificial intelligence software, we can bring a drastic change in the field of personalized medicine in the developing part of the world 3. Numerous validation studies have been published indicating that WSI is a reliable tool for routine diagnosis in surgical pathology 4   Continuous...

scholarly journals Ethical issues in computational pathology

2021 ◽  
pp. medethics-2020-107024
Author(s):  
Tom Sorell ◽  
Nasir Rajpoot ◽  
Clare Verrill
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Ethical Issues ◽  
Recent Literature ◽  
Personal Data ◽  
Ethical Problems ◽  
Data Ethics ◽  
Slide Image ◽  
Data Protection Law ◽  
Computational Pathology

This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. These arise from (1) the tension between artificial intelligence (AI) research—with its hunger for more and more data—and the default preference in data ethics and data protection law for the minimisation of personal data collection and processing; (2) the fact that computational pathology lends itself to kinds of data fusion that go against data ethics norms and some norms of biobanking; (3) the fact that AI methods are esoteric and produce results that are sometimes unexplainable (the so-called ‘black box’problem) and (4) the fact that computational pathology is particularly dependent on scanning technology manufacturers with interests of their own in profit-making from data collection. We shall suggest that most of these issues are resolvable.

scholarly journals Use of whole slide imaging (WSI) for distance teaching

2020 ◽  
pp. jclinpath-2020-206763
Author(s):  
Andrew John Evans ◽  
Nadia Depeiza ◽  
Shara-Gaye Allen ◽  
Kimone Fraser ◽  
Suzanne Shirley ◽  
...  
Keyword(s):  
Distance Education ◽  
Digital Pathology ◽  
Daily Practice ◽  
Distance Teaching ◽  
Whole Slide Imaging ◽  
Glass Slides ◽  
Slide Image ◽  
Technical Issues ◽  
Initial Sessions ◽  
The University

BackgroundTime, travel and financial constraints have meant that traditional visiting teaching engagements are more difficult to accomplish. This has been exacerbated with the advent of the COVID-19 pandemic. The use of digital pathology and whole slide imaging (WSI) as an educational tool for distance teaching is underutilised and not fully exploited. This paper highlights the utility and feedback on the use of WSI for distance education/teaching.Materials and methodsBuilding on an existing relationship with the University of the West Indies (UWI), pathologists at University Health Network, Toronto, provided distance education using WSI, a digitised slide image hosting repository and videoconferencing facilities to provide case-based teaching to 15 UWI pathology trainees. Feedback was obtained from residents via a questionnaire and from teachers via a discussion.ResultsThere was uniform support from teachers who felt that teaching was not hampered by the ‘virtual’ engagement. Comfort levels grew with each engagement and technical issues with sound diminished with the use of a portable speaker. The residents were very supportive and enthusiastic in embracing this mode of teaching. While technical glitches marred initial sessions, the process evened out especially when the slide hosting facility, teleconferencing and sound issues were changed.ConclusionsThere was unanimous endorsement that use of WSI was the future, especially for distance teaching. However, it was not meant to supplant the use of glass slides in their current routine, daily practice.

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review

2021 ◽  
pp. 030098582110404
Author(s):  
Aleksandra Zuraw ◽  
Famke Aeffner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Work Environment ◽  
Digital Pathology ◽  
Whole Slide Imaging ◽  
Peer Reviews ◽  
Veterinary Pathology ◽  
Diagnostic Applications ◽  
Emerging Topics

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence–based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist’s assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

scholarly journals Counting Mitoses With Digital Pathology in Breast Phyllodes Tumors

2020 ◽  
Vol 144 (11) ◽  
pp. 1397-1400 ◽  
Author(s):  
Zi Long Chow ◽  
Aye Aye Thike ◽  
Hui Hua Li ◽  
Nur Diyana Md Nasir ◽  
Joe Poh Sheng Yeong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Correlation Coefficients ◽  
Routine Practice ◽  
Phyllodes Tumors ◽  
Slide Image ◽  
Clinicopathologic Parameters ◽  
Histologic Criterion ◽  
Mitotic Counting ◽  
Tumor Area

Context.— Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists evaluating neoplasms, but different microscopes, variable field selection, and areas have led to possible misclassification. Objective.— To determine whether 10 high-power fields (HPFs) or whole slide mitotic counts correlated better with PT clinicopathologic parameters using digital pathology (DP). We also aimed to find out whether this study might serve as a basis for an artificial intelligence (AI) protocol to count mitosis. Design.— Representative slides were chosen from 93 cases of PTs diagnosed between 2014 and 2015. The slides were scanned and viewed with DP. Mitotic counting was conducted on the whole slide image, before choosing 10 HPFs and demarcating the tumor area in DP. Values of mitoses per millimeter squared were used to compare results between 10 HPFs and the whole slide. Correlations with clinicopathologic parameters were conducted. Results.— Both whole slide counting of mitoses and 10 HPFs had similar statistically significant correlation coefficients with grade, stromal atypia, and stromal hypercellularity. Neither whole slide mitotic counts nor mitoses per 10 HPFs showed statistically significant correlations with patient age and tumor size. Conclusions.— Accurate mitosis counting in breast PTs is important for grading. Exploring machine learning on digital whole slides may influence approaches to training, testing, and validation of a future AI algorithm.

scholarly journals Digital Pathology: Advantages, Limitations and Emerging Perspectives

2020 ◽  
Vol 9 (11) ◽  
pp. 3697
Author(s):  
Stephan W. Jahn ◽  
Markus Plass ◽  
Farid Moinfar
Keyword(s):  
Artificial Intelligence ◽  
Occupational Health ◽  
Large Scale ◽  
Digital Pathology ◽  
Health Issues ◽  
Routine Diagnostics ◽  
Starting Point ◽  
Slide Image ◽  
Traditional Work ◽  
Image Archiving

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist’s profession.

Improving Diagnosis through Digital Pathology: A Proof-of-concept Implementation using Smart Contracts and Decentralized file storage (IPFS) (Preprint)

2021 ◽  
Author(s):  
Hemang Subramanian ◽  
Susmitha Subramanian
Keyword(s):  
Artificial Intelligence ◽  
Data Storage ◽  
Low Cost ◽  
Digital Pathology ◽  
Disease Diagnosis ◽  
Security Requirements ◽  
Smart Contracts ◽  
Data Systems ◽  
Network Bandwidth ◽  
File Storage

BACKGROUND Recent advancements in digital pathology resulting from advances in imaging and digitization have increased the convenience and usability of pathology for disease diagnosis, especially in oncology, urology, and gastro-enteric diagnosis. However, despite the possibilities to include low-cost diagnosis and viable telemedicine, remote diagnosis potential, digital pathology is not yet accessible due to expensive storage, data security requirements, and network bandwidth limitations to transfer high-resolution images and associated data. The increase in storage, transmission and security complexity concerning data collection and diagnosis makes it even more challenging to use artificial intelligence algorithms for machine-assisted disease diagnosis. We design and prototype a digital pathology system that uses blockchain-based smart contracts using the Non-fungible Token standard and the Inter-Planetary File System (IPFS) for data storage. Our design remediates shortcomings in the existing digital pathology systems infrastructure, which is centralized. The proposed design is extendable to other fields of medicine that require high-fidelity image and data storage. Our solution is implemented in data systems that can improve access, quality of care and reduce the cost of access to specialized pathological diagnosis, reducing cycle times for diagnosis. OBJECTIVE The study's main objectives are to highlight the issues in digital pathology and suggest a software architecture-based blockchain and IPFS create a low-cost data storage and transmission technology. METHODS We use the design science research method (DSRM) consisting of six stages to inform our design overall. We innovate over existing public-private designs for blockchains but using a two-layered approach that separates actual file storage from meta-data and data persistence. RESULTS Here, we identify key challenges to adopting digital pathology, including challenges concerning long-term storage, the transmission of information, etc. Next, using accepted frameworks in non-fungible token-based intelligent contracts and recent innovations in distributed secure storage, we propose a decentralized, secure, and privacy-preserving digital pathology system. Our design and prototype implementation using Solidity, web3.js, Ethereum, and node.js help us address several challenges facing digital pathology. We demonstrate how our solution that combines non-fungible token (NFT) smart contract standard with persistent decentralized file storage to solve most of the challenges of digital pathology and sets the stage for reducing costs and improving patient care and speed of diagnosis. CONCLUSIONS We identify technical limitations that increase costs and reduce mass adoption of digital pathology. We present several design innovations by using standards in NFT decentralized storage to prototype a system. We also present implementation details of a unique security architecture for a digital pathology system. We illustrate how this design can overcome privacy, security, network-based storage, and data transmission limitations. We illustrate how improving these factors sets the stage for improving data quality and standardized application of machine learning and Artificial Intelligence to such data CLINICALTRIAL Not applicable

scholarly journals The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups

2021 ◽  
Author(s):  
Celine N Heinz ◽  
Amelie Echle ◽  
Sebastian Foersch ◽  
Andrey Bychkov ◽  
Jakob Nikolas Kather
Keyword(s):  
Artificial Intelligence ◽  
Treatment Response ◽  
Online Survey ◽  
Digital Pathology ◽  
Genetic Alterations ◽  
Future Application ◽  
Cell Detection ◽  
Tissue Segmentation ◽  
Computational Pathology ◽  
Near Future

Artificial intelligence (AI) provides a powerful tool to extract information from digitized histopathology whole slide images. In the last five years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into. To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing sub-fields of computational pathology with a focus on solid tumors. The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in sub-groups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high across subgroups. Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.

DICOM Format and Protocol Standardization—A Core Requirement for Digital Pathology Success

2020 ◽  
pp. 019262332096589
Author(s):  
David A. Clunie
Keyword(s):  
Digital Pathology ◽  
Virtual Microscopy ◽  
File Format ◽  
Whole Slide Imaging ◽  
Dicom Standard ◽  
Bright Field ◽  
Storage Security ◽  
Image File Format ◽  
Computational Pathology ◽  
Protocol Standardization

As the use of digital techniques in toxicologic pathology expands, challenges of scalability and interoperability come to the fore. Proprietary formats and closed single-vendor platforms prevail but depend on the availability and maintenance of multiformat conversion libraries. Expedient for small deployments, this is not sustainable at an industrial scale. Primarily known as a standard for radiology, the Digital Imaging and Communications in Medicine (DICOM) standard has been evolving to support other specialties since its inception, to become the single ubiquitous standard throughout medical imaging. The adoption of DICOM for whole slide imaging (WSI) has been sluggish. Prospects for widespread commercially viable clinical use of digital pathology change the incentives. Connectathons using DICOM have demonstrated its feasibility for WSI and virtual microscopy. Adoption of DICOM for digital and computational pathology will allow the reuse of enterprise-wide infrastructure for storage, security, and business continuity. The DICOM embedded metadata allows detached files to remain useful. Bright-field and multichannel fluorescence, Z-stacks, cytology, and sparse and fully tiled encoding are supported. External terminologies and standard compression schemes are supported. Color consistency is defined using International Color Consortium profiles. The DICOM files can be dual personality Tagged Image File Format (TIFF) for legacy support. Annotations for computational pathology results can be encoded.

scholarly journals Deep Learning Based Retrieval System for Gigapixel Histopathology Cases and Open Access Literature

2018 ◽  
Author(s):  
Sebastian Otálora ◽  
Roger Schaer ◽  
Oscar Jimenez-del-Toro ◽  
Manfredo Atzori ◽  
Henning Müller
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Retrieval System ◽  
Digital Pathology ◽  
Image Features ◽  
Retrieval Performance ◽  
Slide Image ◽  
Text Information ◽  
Computation Algorithms ◽  
Whole Slide Images

ABSTRACTClinical practice is getting increasingly stressful for pathologists due to increasing complexity and time constraints. Histopathology is slowly shifting to digital pathology, thus creating opportunities to allow pathologists to improve reading quality or save time using Artificial Intelligence (AI)-based applications. We aim to enhance the practice of pathologists through a retrieval system that allows them to simplify their workflow, limit the need for second opinions, while also learning in the process. In this work, an innovative retrieval system for digital pathology is integrated within a Whole Slide Image (WSI) viewer, allowing to define regions of interest in images as queries for finding visually similar areas using deep representations. The back-end similarity computation algorithms are based on a multimodal approach, allowing to exploit both text information and content-based image features. Shallow and deep representations of the images were evaluated, the later showed a better overall retrieval performance in a set of 112 whole slide images from biopsies. The system was also tested by pathologists, highlighting its capabilities and suggesting possible ways to improve it and make it more usable in clinical practice. The retrieval system developed can enhance the practice of pathologists by enabling them to use their experience and knowledge to properly control artificial intelligence tools for navigating repositories of images for decision support purposes.

scholarly journals DESIGN OF LATVIAN METAL MUSIC ALBUM COVERS IN 2019

2020 ◽  
Vol 5 ◽  
pp. 661
Author(s):  
Ingars Gusans
Keyword(s):  
Data Storage ◽  
Comparative Method ◽  
Visual Design ◽  
The Internet ◽  
Visual Context ◽  
Storage Devices ◽  
Metal Music ◽  
Heavy Metal Music ◽  
The World ◽  
The Many

Latvian metal music has a small but stable place on the map of the metal music world. Each year several music albums of this genre are issued in Latvia. With the decrease in demand for physical data storage devices, brochures and well designed back covers are no longer popular; however the only remaining album cover has gained even more importance – the image, photograph, picture that represents the musical material and acts either as a reflection of the content or as an element for attracting attention and is published on the Internet as well as in printed press as a concrete symbol of the album. The research aim is to describe the design of Latvian metal music album covers in 2019 in the visual context of the albums issued in the world. The research was conducted using the comparative method, looking for the local and global, the typical and different in the visualisation of album covers on the basis of not only theoretical literature but also the many years of experience of the author as a musician and a collector of music records. In general, the visual look of album covers issued in Latvia is typical for heavy metal music and fits in with the visual design of metal band albums in the rest of the world. However, there have been attempts to include eye-catching accents using national or pseudo-national elements or colours. 

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