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

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 CoAI: Cost-Aware Artificial Intelligence for Health Care

2021 ◽  
Author(s):  
Gabriel Erion ◽  
Joseph D. Janizek ◽  
Carly Hudelson ◽  
Richard B. Utarnachitt ◽  
Andrew M. McCoy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intensive Care ◽  
Predictive Model ◽  
Low Cost ◽  
The United States ◽  
Disease Diagnosis ◽  
Performance Model ◽  
Risk Scores ◽  
Recent Emergence ◽  
The Cost

AbstractThe recent emergence of accurate artificial intelligence (AI) models for disease diagnosis raises the possibility that AI-based clinical decision support could substantially lower the workload of healthcare providers. However, for this to occur, the input data to an AI predictive model, i.e., the patient’s features, must themselves be low-cost, that is, efficient, inexpensive, or low-effort to acquire. When time or financial resources for gathering data are limited, as in emergency or critical care medicine, modern high-accuracy AI models that use thousands of patient features are likely impractical. To address this problem, we developed the CoAI (Cost-aware AI) framework to enable any kind of AI predictive model (e.g., deep neural networks, tree ensemble models, etc.) to make accurate predictions given a small number of low-cost features. We show that CoAI dramatically reduces the cost of predicting prehospital acute traumatic coagulopathy, intensive care mortality, and outpatient mortality relative to existing risk scores, while improving prediction accuracy. It also outperforms existing state-of-the-art cost-sensitive prediction approaches in terms of predictive performance, model cost, and training time. Extrapolating these results to all trauma patients in the United States shows that, at a fixed false positive rate, CoAI could alert providers of tens of thousands more dangerous events than other risk scores while reducing providers’ data-gathering time by about 90 percent, leading to a savings of 200,000 cumulative hours per year across all providers. We extrapolate similar increases in clinical utility for CoAI in intensive care. These benefits stem from several unique strengths: First, CoAI uses axiomatic feature attribution methods that enable precise estimation of feature importance. Second, CoAI is model-agnostic, allowing users to choose the predictive model that performs the best for the prediction task and data at hand. Finally, unlike many existing methods, CoAI finds high-performance models within a given budget without any tuning of the cost-vs-performance tradeoff. We believe CoAI will dramatically improve patient care in the domains of medicine in which predictions need to be made with limited time and resources.

mHealth

2020 ◽  
pp. 1-21
Author(s):  
Gloria Ejehiohen Iyawa ◽  
Collins Oduor Ondiek ◽  
Jude Odiakaosa Osakwe
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Text Messaging ◽  
Driving Force ◽  
Healthcare Service ◽  
Low Cost ◽  
Healthcare Services ◽  
Disease Diagnosis ◽  
Mobile Technologies ◽  
Present Information

Mobile health (mHealth), the application of mobile technologies for healthcare services, has been the driving force in healthcare in the last few decades; from healthcare service delivery to low-cost tools for effective disease diagnosis, prediction, monitoring, and management. The main purpose of this chapter was to identify the scope and range of studies on mHealth used as low-cost tools for effective disease diagnosis, prediction, monitoring, and management. The authors identified 55 papers that met the inclusion and exclusion criteria after searching different academic databases. The findings revealed that low-cost mHealth approaches such as text messaging and mobile applications developed using artificial intelligence algorithms have been used for disease diagnosis, prediction, monitoring, and management. The findings of this scoping review present information regarding different mHealth approaches that can be used by researchers and practitioners interested in the application of low-cost mHealth solutions in low-resource settings.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Bringing Security to The Smallest Embedded Systems

2017 ◽  
Author(s):  
JOSEPH YIU
Keyword(s):  
Internet Of Things ◽  
Embedded Systems ◽  
Low Cost ◽  
Security Requirements ◽  
The Internet ◽  
Security Measures ◽  
Significant Challenge ◽  
Iot Devices

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:

scholarly journals CioSy: A Collaborative Blockchain-Based Insurance System

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1343
Author(s):  
Faiza Loukil ◽  
Khouloud Boukadi ◽  
Rasheed Hussain ◽  
Mourad Abed
Keyword(s):  
Data Storage ◽  
Insurance Industry ◽  
Emerging Technology ◽  
Third Party ◽  
Insurance Companies ◽  
Smart Contracts ◽  
Insurance Policy ◽  
Insurance System ◽  
Advanced Technologies ◽  
Multiple Challenges

The insurance industry is heavily dependent on several processes executed among multiple entities, such as insurer, insured, and third-party services. The increasingly competitive environment is pushing insurance companies to use advanced technologies to address multiple challenges, namely lack of trust, lack of transparency, and economic instability. To this end, blockchain is used as an emerging technology that enables transparent and secure data storage and transmission. In this paper, we propose CioSy, a collaborative blockchain-based insurance system for monitoring and processing the insurance transactions. To the best of our knowledge, the existing approaches do not consider collaborative insurance to achieve an automated, transparent, and tamper-proof solution. CioSy aims at automating the insurance policy processing, claim handling, and payment using smart contracts. For validation purposes, an experimental prototype is developed on Ethereum blockchain. Our experimental results show that the proposed approach is both feasible and economical in terms of time and cost.

scholarly journals The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Daniele Giansanti
Keyword(s):  
Artificial Intelligence ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Mobile Health ◽  
Digital Pathology ◽  
Digital Radiology ◽  
Cellular Diagnostics ◽  
Information And Communication ◽  
Electronic Health ◽  
New Applications

Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...]

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