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

Haron, Nabihah; Zain, Rosnah Binti; Ramanathan, Anand; Abraham, Mannil Thomas; Liew, Chee Sun; Ng, Kheng Ghee; Cheng, Lee Soo; Husin, Rozihan Binti; Chong, Sherrie Mei Yee; Logesvari, A; Thangavalu, P; Mat, Azizah; Ismail, Hasmah Binti; Sumitha, A; Mahalingam, P; Cheong, Sok Ching

doi:10.1089/tmj.2018.0285

Cited by 46 publications

(46 citation statements)

References 22 publications

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“…With the long term goal of using telemedicine to facilitate the management of patients, Cancer Research Malaysia has developed a mobile phone App called MeMoSA ® (Mobile Mouth Screening Anywhere) [34]. MeMoSA ® allows for the easy documentation of oral lesions through a mobile phone camera and enables seamless two-way communication between primary healthcare practitioners and specialists located off-site.…”

Section: Methodsmentioning

confidence: 99%

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

et al. 2020

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Oral cancer is a major global health issue accounting for 177,384 deaths in 2018 and it is most prevalent in low-and middle-income countries. Enabling automation in the identification of potentially malignant and malignant lesions in the oral cavity would potentially lead to low-cost and early diagnosis of the disease. Building a large library of well-annotated oral lesions is key. As part of the MeMoSA ® (Mobile Mouth Screening Anywhere) project, images are currently in the process of being gathered from clinical experts from across the world, who have been provided with an annotation tool to produce rich labels. A novel strategy to combine bounding box annotations from multiple clinicians is provided in this paper. Further to this, deep neural networks were used to build automated systems, in which complex patterns were derived for tackling this difficult task. Using the initial data gathered in this study, two deep learning based computer vision approaches were assessed for the automated detection and classification of oral lesions for the early detection of oral cancer, these were image classification with ResNet-101 and object detection with the Faster R-CNN. Image classification achieved an F1 score of 87.07% for identification of images that contained lesions and 78.30% for the identification of images that required referral. Object detection achieved an F1 score of 41.18% for the detection of lesions that required referral. Further performances are reported with respect to classifying according to the type of referral decision. Our initial results demonstrate deep learning has the potential to tackle this challenging task. INDEX TERMS Composite annotation, deep learning, image classification, object detection, oral cancer, oral potentially malignant disorders.

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Section: Methodsmentioning

confidence: 99%

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

et al. 2020

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“…An addition to telediagnosis is telecytology, a system for early detection of oral potentially malignant or malignant lesions [18]. Haron et al developed Mobile Mouth Screening Anywhere (MeMoSA®) to facilitate early detection of oral cancer and found it to be beneficial for patients with limited access to specialists [19]. Skandarajah et al evaluated a tablet-based mobile microscope (CellScope device) as an adjunct for screening of oral cancer [20].…”

Section: Telediagnosismentioning

confidence: 99%

Teledentistry during COVID-19 pandemic

Ghai

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

216

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Background and aims: Dentistry involves close face-to-face interaction with patients, hence during the COVID-19 pandemic, it has mostly been suspended. Teledentistry can offer an innovative solution to resume dental practice during this pandemic. In this review, we provide a brief overview of applications of teledentistry. Methods: Articles on teledentistry, relevant to this review, were searched and consulted from PubMed, Google Scholar, and Cochrane database. Results: Teledentistry is the remote facilitating of dental treatment, guidance, and education via the use of information technology instead of direct face-to-face contact with patients. Teleconsultation, telediagnosis, teletriage, and telemonitoring are subunits of teledentistry that have important functions relevant to dental practice. There are many challenges for acceptance of teledentistry by the dentists as well as patients, which need to be addressed urgently. Conclusion: Teledentistry can offer a novel solution to resume dental practice during the current pandemic, hence, the need of the hour is to incorporate teledentistry into routine dental practice. If not fully replace, at least teledentistry can complement the existing compromised dental system during the current pandemic.

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“…In this study, the most preferred modality of telemedicine was mobile health (mHealth) approach and this could be due to easy access of mobile phones and expanding telephone network. Given the infrastructural and skilled human resource limitations in low-income settings, mobile phone-based interventions are reported as effective methods in improving adherence, compliance, early detection and prevention [43,44] In addition, the mobile phone access and penetration in low-income countries have phenomenally increased in the past decade indicating a huge potential of success in addressing larger population. Therefore, an intervention using mobile phone could probably result in better success in implementing telemedicine in resource constrained environments like Ethiopia.…”

Section: Plos Onementioning

confidence: 99%

Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model

et al. 2021

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Background In almost all lower and lower middle-income countries, the healthcare system is structured in the customary model of in-person or face to face model of care. With the current global COVID-19 pandemics, the usual health care service has been significantly altered in many aspects. Given the fragile health system and high number of immunocompromised populations in lower and lower-middle income countries, the economic impacts of COVID-19 are anticipated to be worse. In such scenarios, technological solutions like, Telemedicine which is defined as the delivery of healthcare service remotely using telecommunication technologies for exchange of medical information, diagnosis, consultation and treatment is critical. The aim of this study was to assess healthcare providers’ acceptance and preferred modality of telemedicine and factors thereof among health professionals working in Ethiopia. Methods A multi-centric online survey was conducted via social media platforms such as telegram channels, Facebook groups/pages and email during Jul 1- Sep 21, 2020. The questionnaire was adopted from previously validated model in low income setting. Internal consistency of items was assessed using Cronbach alpha (α), composite reliability (CR) and average variance extracted (AVE) to evaluate both discriminant and convergent validity of constructs. The extent of relationship among variables were evaluated by Structural equation modeling (SEM) using SPSS Amos version 23. Results From the expected 423 responses, 319 (75.4%) participants responded to the survey questionnaire during the data collection period. The majority of participants were male (78.1%), age <30 (76.8%) and had less than five years of work experience (78.1%). The structural model result confirmed the hypothesis “self-efficacy has a significant positive effect on effort expectancy” with a standardized coefficient estimate (β) of 0.76 and p-value <0.001. The result also indicated that self-efficacy, effort expectancy, performance expectancy, facilitating conditions and social influence have a significant direct effect on user’s attitude toward using telemedicine. User’s behavioral intention to use telemedicine was also influenced by effort expectancy and attitude. The model also ruled out that performance expectancy, facilitating conditions and social influence does not directly influence user’s intention to use telemedicine. The squared multiple correlations (r2) value indicated that 57.1% of the variance in attitude toward using telemedicine and 63.6% of the variance in behavioral intention to use telemedicine is explained by the current structural model. Conclusion This study found that effort expectancy and attitude were significantly predictors of healthcare professionals’ acceptance of telemedicine. Attitude toward using telemedicine systems was also highly influenced by performance expectancy, self-efficacy and facilitating conditions. effort expectancy and attitude were also significant mediators in predicting users’ acceptance of telemedicine. In addition, mHealth approach was the most preferred modality of telemedicine and this opens an opportunity to integrate telemedicine systems in the health system during and post pandemic health services in low-income countries.

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

Cited by 46 publications

References 22 publications

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

Teledentistry during COVID-19 pandemic

Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model

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