Alvin B. Marcelo

Effect of image compression on telepathology. A randomized clinical trial.

CONTEXT For practitioners deploying store-and-forward telepathology systems, optimization methods such as image compression need to be studied. OBJECTIVE To determine if Joint Photographic Expert Group (JPG or JPEG) compression, a glossy image compression algorithm, negatively affects the accuracy of diagnosis in telepathology. DESIGN Double-blind, randomized, controlled trial. SETTING University-based pathology departments. PARTICIPANTS Resident and staff pathologists at the University of Illinois, Chicago, and University of Cincinnati, Cincinnati, Ohio. INTERVENTION Compression of raw images using the JPEG algorithm. MAIN OUTCOME MEASURES Image acceptability, accuracy of diagnosis, confidence level of pathologist, image quality. RESULTS There was no statistically significant difference in the diagnostic accuracy between noncompressed (bit map) and compressed (JPG) images. There were also no differences in the acceptability, confidence level, and perception of image quality. Additionally, rater experience did not significantly correlate with degree of accuracy. CONCLUSIONS For providers practicing telepathology, JPG image compression does not negatively affect the accuracy and confidence level of diagnosis. The acceptability and quality of images were also not affected.

Digital pathology – implementation challenges in low-resource countries

Background: Medical education in pathology and histology in low-resource countries face many obstacles because of equipment cost and telecommunication deficiencies. Digital Pathology may provide solutions. We report student experience to virtual slides on a local network and a remote image server. Methods: Using an iPad tablet device, fifty 3rd and 4th year medical students viewed digital pathology slides from a Web server at the National Library of Medicine and a mirror server on the local network. Results: The quality of images from both servers was found to be satisfactory, but the local server was deemed faster and preferred by the participants in this study (p < 0.005). Conclusions: Virtual slides on a local network server may provide solutions to equipment and technical obstacles and could enhance student learning in developing countries.

Information‐seeking trends of medical professionals and students from middle‐income countries: a focus on the Philippines

BACKGROUND Increased emphasis has been given to the practice of evidence-based medicine (EBM) worldwide. Access to quality health information is essential to the practice of EBM in developing countries. OBJECTIVES To understand the information needs and sources of information of physicians from low- and middle-income countries (LMICs). METHODS Medical doctors and students participated in an 18-question online or paper study. RESULTS Of the 156 respondents from six LMICs, 146 (94%) came from the Philippines. Eighty-eight per cent encountered at least one clinical question daily, while 58% were very likely to search for answers. A basic mobile phone was the most used device at home (94%) and at work (82%). More than half had Internet connectivity at home (62%) and just under half at work (46%). In decreasing order, short messaging services (SMS), email, instant messaging and multimedia messaging services (MMS) were the most commonly used messaging tools at home and at work. The primary source for medication questions was a formulary, but for diagnostic dilemmas, colleagues were consulted first. PubMed use was high for therapy and management questions. CONCLUSION The use of health information from the Internet through mobile devices may be increasing. Access to health information was higher at home than at work. These results may be useful when planning resources for healthcare givers in resource-poor settings.

ESP: an expert system for poisoning diagnosis and management.

We describe a clinical decision support system (CDSS) designed to provide timely information germane to poisoning. The CDSS aids medical decision making through recommendations to clinicians for immediate evaluation. The system is implemented as a rule-based expert system with two major components: the knowledge base and the inference engine. The knowledge base serves as the database which contains relevant poisoning information and rules that are used by the inference engine in making decisions. This expert system accepts signs and symptoms observed from a patient as input and presents a list of possible poisoning types with the corresponding management procedures which may be considered in making the final diagnosis. A knowledge acquisition tool (KAT) that allows toxicological experts to update the knowledge base was also developed. This article describes the architecture of the fully featured system, the design of the CDSS and the KAT as web applications, the utilisation of the inferencing mechanism of C Language Integrated Production System (CLIPS), which is an expert system shell that helps the system in decision-making tasks, the methods used as well as problems encountered. We also present the results obtained after testing the system and propose some recommendations for future work.

Blockchain Technology for Detecting Falsified and Substandard Drugs in Distribution: Pharmaceutical Supply Chain Intervention

Background Drug counterfeiting is a global problem with significant risks to consumers and the general public. In the Philippines, 30% of inspected drug stores in 2003 were found with substandard/spurious/falsely-labeled/falsified/counterfeit drugs. The economic burden on the population drug expenditures and on governments is high. The Philippine Food and Drug Administration (FDA) encourages the public to check the certificates of product registration and report any instances of counterfeiting. The National Police of Philippines responds to such reports through a special task force. However, no literature on its impact on the distribution of such drugs were found. Blockchain technology is a cryptographic ledger that is allegedly immutable through repeated sequential hashing and fault-tolerant through a consensus algorithm. This project will develop and test a pharmacosurveillance blockchain system that will support information sharing along the official drug distribution network. Objective This study aims to develop a pharmacosurveillance blockchain system and test its functions in a simulated network. Methods We are developing a Distributed Application (DApp) that will run on smart contracts, employing Swarm as the Distributed File System (DFS). Two instances will be developed: one for Ethereum and another for Hyperledger Fabric. The proof-of-work (PoW) consensus algorithm of Ethereum will be modified into a delegated proof-of-stake (DPoS) or practical Byzantine fault tolerance (PBFT) consensus algorithm as it is scalable and fits the drug supply chain environment. The system will adopt the GS1 pedigree standard and will satisfy the data points in the data standardization guidelines from the US FDA. Simulations will use the following 5 nodes: for FDA, manufacturer, wholesaler, retailer, and the consumer portal. Results Development is underway. The design of the system will place FDA in a supervisory data verification role, with each pedigree type–specific data source serving a primary data verification role. The supply chain process will be initiated by the manufacturer, with recursive verification for every transaction. It will allow consumers to scan a code printed on the receipt of their purchases to review the drug distribution history. Conclusions Development and testing will be conducted in a simulated network, and thus, results may differ from actual practice. The project being proposed is disruptive; once tested, the team intends to engage the Philippine FDA to discuss implementation plans and formulate policies to facilitate adoption and sustainability. Registered Report Identifier RR1-10.2196/10163