Image collection and annotation platforms to establish a multi-source database of oral lesions.

OBJECTIVE: To describe the development of a platform for image collection and annotation that resulted in a multi-sourced international image dataset of oral lesions to facilitate the development of automated lesion classification algorithms. MATERIALS AND METHODS: We developed a web-interface, hosted on a web server to collect oral lesions images from international partners. Further, we developed a customised annotation tool, also a web-interface for systematic annotation of images to build a rich clinically labelled dataset. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions. RESULTS: The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders and other oral lesions that were collected through MeMoSA® UPLOAD. Eight-hundred images were annotated by seven oral medicine specialists on MeMoSA® ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3%-100%). CONCLUSION: This is the first description of a database with clinically labelled oral lesions. This database could accelerate the improvement of AI algorithms that can promote the early detection of high-risk oral lesions.

High referral accuracy for oral cancers and oral potentially malignant disorders using telemedicine.

OBJECTIVE: To evaluate the accuracy of MeMoSA®, a mobile phone application to review images of oral lesions in identifying oral cancers and oral potentially malignant disorders requiring referral. SUBJECTS AND METHODS: A prospective study of 355 participants, including 280 with oral lesions/variants was conducted. Adults aged ≥18 treated at tertiary referral centres were included. Images of the oral cavity were taken using MeMoSA®. The identification of the presence of lesion/variant and referral decision made using MeMoSA® were compared to clinical oral examination, using kappa statistics for intra-rater agreement. Sensitivity, specificity, concordance and F1 score were computed. Images were reviewed by an off-site specialist and inter-rater agreement was evaluated. Images from sequential clinical visits were compared to evaluate observable changes in the lesions. RESULTS: Kappa values comparing MeMoSA® with clinical oral examination in detecting a lesion and referral decision was 0.604 and 0.892, respectively. Sensitivity and specificity for referral decision were 94.0% and 95.5%. Concordance and F1 score were 94.9% and 93.3%, respectively. Inter-rater agreement for a referral decision was 0.825. Progression or regression of lesions were systematically documented using MeMoSA®. CONCLUSION: Referral decisions made through MeMoSA® is highly comparable to clinical examination demonstrating it is a reliable telemedicine tool to facilitate the identification of high-risk lesions for early management.

Factors influencing healthcare seeking in patients with dengue: Systematic review.

OBJECTIVE: Delays in seeking healthcare for dengue are associated with poor health outcomes. Despite this, the factors influencing such delays remain unclear, rendering interventions to improve healthcare seeking for dengue ineffective. This systematic review aimed to synthesise the factors influencing healthcare seeking of patients with dengue and form a comprehensive framework. METHODS: This review included both qualitative and quantitative studies. Studies were obtained by searching five databases, contacting field experts and performing backward reference searches. The best-fit meta-synthesis approach was used during data synthesis, where extracted data were fitted into the social-ecological model. Sub-analyses were conducted to identify the commonly reported factors and their level of statistical significance. RESULTS: Twenty studies were selected for meta-synthesis. Eighteen factors influencing healthcare seeking in dengue were identified and categorised under four domains: individual (11 factors), interpersonal (one factor), organisational (four factors) and community (two factors). The most reported factors were knowledge of dengue, access to healthcare, quality of health service and resource availability. Overall, more barriers to dengue health seeking than facilitators were found. History of dengue infection and having knowledge of dengue were found to be ambiguous as they both facilitated and hindered dengue healthcare seeking. Contrary to common belief, women were less likely to seek help for dengue than men. CONCLUSIONS: The factors affecting dengue healthcare-seeking behaviour are diverse, can be ambiguous and are found across multiple social-ecological levels. Understanding these complexities is essential for the development of effective interventions to improve dengue healthcare-seeking behaviour.

The establishment of the Asia-Pacific Oral Cancer Network-Inaugural stakeholders' Meeting.

The majority of oral cancer cases occur in Asia and the incidence is expected to continue to rise. Oral cancer is amenable to early detection through visual oral examination yet in many Asian countries, the disease presents at a late stage. The barriers to early detection are similar across the Asia-Pacific countries therefore, strategies to address these could be more effective if there were concerted efforts and joint resources amongst the countries. To facilitate better engagement and collaboration between stakeholders in oral cancer detection and management, the Asia-Pacific Oral Cancer Network (APOCNET) was established and the inaugural meeting was held in Kuala Lumpur on the 13th to 15th of September 2019. In this meeting, we identified the challenges faced in the early detection and management of oral cancer amongst the stakeholder countries, showcased the successful oral cancer programs in the region and identified strategic areas for collaboration. For this, notable international speakers and those from local universities and the Ministry of Health Malaysia were invited to share their experiences. The lessons learned from our neighbouring countries could lead to the implementation of similar programs that could help reduce the oral cancer burden in the region.

m-Health for Early Detection of Oral Cancer in Low- and Middle-Income Countries.

Background: Up to 86% of oral cancer (OC) patients present at the late stage where survival is dismal. Limited access to specialist diagnosis is a significant factor for late presentation. The increasing use of smartphones presents an opportunity to use digital technology to facilitate early detection of OC. Aim: To evaluate the feasibility of using Mobile Mouth Screening Anywhere (MeMoSA®) to facilitate early detection of OC. Methods: A mobile phone app named MeMoSA was developed and the feasibility of integrating this for documentation of oral lesions, and communication between dentists and specialists for management decisions were evaluated. The experience of dentists and specialists in using MeMoSA was determined using qualitative questionnaires. Results: Communication between specialist and dentists using MeMoSA stratified cases and streamlined referral of patients. Twelve of 48 patients were found to have oral lesions or signs suspicious of cancer and 3 required referrals. The patient's compliance for referral was tracked with MeMoSA. All dentists agreed that MeMoSA could facilitate early detection of OC and believed that MeMoSA could assist in the identification of oral mucosal lesions through direct communication with specialists and continuous learning in the recognition of high-risk lesions. Conclusions: MeMoSA has the potential to be used to promote equitable health care and streamline patient management that could result in early detection of OC.

An Interoperable Component-Based Architecture for Data-Driven IoT System.

The advancement of the Internet of Things (IoT) as a solution in diverse application domains has nurtured the expansion in the number of devices and data volume. Multiple platforms and protocols have been introduced and resulted in high device ubiquity and heterogeneity. However, currently available IoT architectures face challenges to accommodate the diversity in IoT devices or services operating under different operating systems and protocols. In this paper, we propose a new IoT architecture that utilizes the component-based design approach to create and define the loosely-coupled, standalone but interoperable service components for IoT systems. Furthermore, a data-driven feedback function is included as a key feature of the proposed architecture to enable a greater degree of system automation and to reduce the dependency on mankind for data analysis and decision-making. The proposed architecture aims to tackle device interoperability, system reusability and the lack of data-driven functionality issues. Using a real-world use case on a proof-of-concept prototype, we examined the viability and usability of the proposed architecture.

DeSigN: connecting gene expression with therapeutics for drug repurposing and development.

BACKGROUND: The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. RESULTS: We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8-1.2 µM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. CONCLUSIONS: DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.

Mining personal data using smartphones and wearable devices: a survey.

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.

Barriers to early detection and management of oral cancer in the Asia Pacific region.

OBJECTIVE: Oral cancer is amenable to early detection but remains a prominent cause of mortality in the Asia Pacific region. This study aimed to identify barriers to early detection and management of oral cancer in the Asia Pacific region. METHODS: A mixed-methods approach was employed triangulating findings from a survey and focus groups. The survey was conducted among seven representative members of the Asia Pacific Oral Cancer Network (APOCNET) across six countries. Focus groups were conducted to gain deeper insights into the findings of the survey. RESULTS: The identified barriers were a lack of national cancer control strategies and cancer registries and the limited availability of trained health care professionals. Overcoming these challenges in the Asia Pacific region where resources are scarce will require collaborative partnerships in data collection and novel approaches for continuous professional training including eLearning. Further, to overcome the lack of trained health care professionals, innovative approaches to the management of oral potentially malignant lesions and oral cancer including telemedicine were suggested. CONCLUSION: The findings of this study should be taken into account when charting national cancer control plans for oral cancer and will form the basis for future collaborative studies in evaluating effective measures to improve oral cancer detection and management in low- and middle-income countries.

Utility and usability of an automated COVID-19 symptom monitoring system (CoSMoS) in primary care during COVID-19 pandemic: A qualitative feasibility study.

BACKGROUND: COVID-19 telemonitoring applications have been developed and used in primary care to monitor patients quarantined at home. There is a lack of evidence on the utility and usability of telemonitoring applications from end-users' perspective. OBJECTIVES: This study aimed to evaluate the feasibility of a COVID-19 symptom monitoring system (CoSMoS) by exploring its utility and usability with end-users. METHODS: This was a qualitative study using in-depth interviews. Patients with suspected COVID-19 infection who used CoSMoS Telegram bot to monitor their COVID-19 symptoms and doctors who conducted the telemonitoring via CoSMoS dashboard were recruited. Universal sampling was used in this study. We stopped the recruitment when data saturation was reached. Patients and doctors shared their experiences using CoSMoS, its utility and usability for COVID-19 symptoms monitoring. Data were coded and analysed using thematic analysis. RESULTS: A total of 11 patients and 4 doctors were recruited into this study. For utility, CoSMoS was useful in providing close monitoring and continuity of care, supporting patients' decision making, ensuring adherence to reporting, and reducing healthcare workers' burden during the pandemic. In terms of usability, patients expressed that CoSMoS was convenient and easy to use. The use of the existing social media application for symptom monitoring was acceptable for the patients. The content in the Telegram bot was easy to understand, although revision was needed to keep the content updated. Doctors preferred to integrate CoSMoS into the electronic medical record. CONCLUSION: CoSMoS is feasible and useful to patients and doctors in providing remote monitoring and teleconsultation during the COVID-19 pandemic. The utility and usability evaluation enables the refinement of CoSMoS to be a patient-centred monitoring system.

An Automated Patient Self-Monitoring System to Reduce Health Care System Burden During the COVID-19 Pandemic in Malaysia: Development and Implementation Study.

BACKGROUND: During the COVID-19 pandemic, there was an urgent need to develop an automated COVID-19 symptom monitoring system to reduce the burden on the health care system and to provide better self-monitoring at home. OBJECTIVE: This paper aimed to describe the development process of the COVID-19 Symptom Monitoring System (CoSMoS), which consists of a self-monitoring, algorithm-based Telegram bot and a teleconsultation system. We describe all the essential steps from the clinical perspective and our technical approach in designing, developing, and integrating the system into clinical practice during the COVID-19 pandemic as well as lessons learned from this development process. METHODS: CoSMoS was developed in three phases: (1) requirement formation to identify clinical problems and to draft the clinical algorithm, (2) development testing iteration using the agile software development method, and (3) integration into clinical practice to design an effective clinical workflow using repeated simulations and role-playing. RESULTS: We completed the development of CoSMoS in 19 days. In Phase 1 (ie, requirement formation), we identified three main functions: a daily automated reminder system for patients to self-check their symptoms, a safe patient risk assessment to guide patients in clinical decision making, and an active telemonitoring system with real-time phone consultations. The system architecture of CoSMoS involved five components: Telegram instant messaging, a clinician dashboard, system administration (ie, back end), a database, and development and operations infrastructure. The integration of CoSMoS into clinical practice involved the consideration of COVID-19 infectivity and patient safety. CONCLUSIONS: This study demonstrated that developing a COVID-19 symptom monitoring system within a short time during a pandemic is feasible using the agile development method. Time factors and communication between the technical and clinical teams were the main challenges in the development process. The development process and lessons learned from this study can guide the future development of digital monitoring systems during the next pandemic, especially in developing countries.

QuickCount®: a novel automated software for rapid cell detection and quantification.

We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland-Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.

Performance database: capturing data for optimizing distributed streaming workflows.

The performance database (PDB) stores performance-related data gathered during workflow enactment. We argue that, by carefully understanding and manipulating these data, we can improve efficiency when enacting workflows. This paper describes the rationale behind the PDB, and proposes a systematic way to implement it. The prototype is built as part of the Advanced Data Mining and Integration Research for Europe project. We use workflows from real-world experiments to demonstrate the usage of PDB.