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Profiling the features of pre-segmented healthy liver CT scans: towards fast detection of liver lesions in emergency scenario

Pasha MF, Hong KS, Rajeswari M

Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:5169-73.
PMID: 22255503 DOI: 10.1109/IEMBS.2011.6091280

Automating the detection of lesions in liver CT scans requires a high performance and robust solution. With CT-scan start to become the norm in emergency department, the need for a fast and efficient liver lesions detection method is arising. In this paper, we propose a fast and evolvable method to profile the features of pre-segmented healthy liver and use it to detect the presence of liver lesions in emergency scenario. Our preliminary experiment with the MICCAI 2007 grand challenge datasets shows promising results of a fast training time, ability to evolve the produced healthy liver profiles, and accurate detection of the liver lesions. Lastly, the future work directions are also presented.
Fulltext Combined measures to control the COVID-19 pandemic in Wuhan, Hubei, China: A narrative review

Hasnain M, Pasha MF, Ghani I

J Biosaf Biosecur, 2020 Dec;2(2):51-57.
PMID: 33521592 DOI: 10.1016/j.jobb.2020.10.001

Coronavirus disease 2019 (COVID-19) is an emerging disease caused by the coronavirus, SARS-CoV-2, which leads to severe respiratory infections in humans. COVID-19 was first reported in December 2019 in Wuhan city, a populated area of the Hubei province in China. As of now, Wuhan and other cities nearby have become safe places for locals. The rapid control of the spread of COVID-19 infection was made possible due to several interventions and measures that were undertaken in Wuhan. This narrative review study was designed to evaluate the emerging literature on the combined measures taken to control the COVID-19 pandemic in Wuhan city. Science Direct, Springer, Web of Science, and the PubMed data repositories were searched for studies published between December 1, 2019, and June 07, 2020. The referred "preferred reporting items for systematic reviews and meta-analyses" (PRISMA) protocol was used to conduct this narrative review. A total of 330 research studies were found as a result of the initial search based on exclusion and inclusion criteria, and 30 articles were chosen on final evaluation. It was discovered that the combined measures to control the spread of COVID-19 in Wuhan included cordon sanitaire, social distancing, universal symptom surveys, quarantine strategies, and transport restrictions. Based on the recommendations presented in this review study, existing policies with regard to combined measures and public health policies can be enforced by other countries to implement a rapid control procedure to control the spread of the COVID-19 pandemic.
Fulltext The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities

Ayaz M, Pasha MF, Alzahrani MY, Budiarto R, Stiawan D

JMIR Med Inform, 2021 07 30;9(7):e21929.
PMID: 34328424 DOI: 10.2196/21929

BACKGROUND: Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain.
OBJECTIVE: The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications.
METHODS: In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration.
RESULTS: The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR.
CONCLUSIONS: The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future.
Fulltext Correction: The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities

Ayaz M, Pasha MF, Alzahrani MY, Budiarto R, Stiawan D

JMIR Med Inform, 2021 Aug 17;9(8):e32869.
PMID: 34403353 DOI: 10.2196/32869

[This corrects the article DOI: 10.2196/21929.].
Fulltext Protection challenges of pregnant women against vertical transmission during COVID-19 epidemic: A narrative review

Hasnain M, Pasha MF, Ghani I, Budiarto R

Am J Infect Control, 2020 12;48(12):1516-1519.
PMID: 32621859 DOI: 10.1016/j.ajic.2020.06.206

This paper presents a narrative review study of 5 popular data repositories focusing on challenges of pregnant women protection during the COVID-19 pandemic. The study concludes that the likelihood of a vertical transmission of COVID-19 infection from pregnant women to neonates was not observed. Nevertheless, it remains a serious risk for them during their earlier stage of pregnancy, thus, special attention from health professionals has been recommended.
Fulltext The COVID-19 impact on air condition usage: a shift towards residential energy saving

Aliero MS, Pasha MF, Toosi AN, Ghani I

Environ Sci Pollut Res Int, 2022 Dec;29(57):85727-85741.
PMID: 35001275 DOI: 10.1007/s11356-021-17862-z

The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model's thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios.
Fulltext Analysis of DICOM Image Compression Alternative Using Huffman Coding

Rahmat RF, Andreas TSM, Fahmi F, Pasha MF, Alzahrani MY, Budiarto R

J Healthc Eng, 2019;2019:5810540.
PMID: 31316743 DOI: 10.1155/2019/5810540

Compression, in general, aims to reduce file size, with or without decreasing data quality of the original file. Digital Imaging and Communication in Medicine (DICOM) is a medical imaging file standard used to store multiple information such as patient data, imaging procedures, and the image itself. With the rising usage of medical imaging in clinical diagnosis, there is a need for a fast and secure method to share large number of medical images between healthcare practitioners, and compression has always been an option. This work analyses the Huffman coding compression method, one of the lossless compression techniques, as an alternative method to compress a DICOM file in open PACS settings. The idea of the Huffman coding compression method is to provide codeword with less number of bits for the symbol that has a higher value of byte frequency distribution. Experiments using different type of DICOM images are conducted, and the analysis on the performances in terms of compression ratio and compression/decompression time, as well as security, is provided. The experimental results showed that the Huffman coding technique has the capability to compress the DICOM file up to 1 : 3.7010 ratio and up to 72.98% space savings.
Fulltext An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network

Ali A, Almaiah MA, Hajjej F, Pasha MF, Fang OH, Khan R, et al.

Sensors (Basel), 2022 Jan 12;22(2).
PMID: 35062530 DOI: 10.3390/s22020572

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.