Oral cancer screening using mobile phone-based(mHealth) approach versus conventional oral examination approach, protocol of a cluster randomized study with cost-effectiveness analysis

Thankappan, Krishnakumar; Birur, Praveen; Raj, Manu; Djalalov, S.; Subramanian, Sujha; Iyer, Subramania; Kuriakose, Moni Abraham

doi:10.1016/j.isjp.2020.07.001

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Unfortunately, oral cancer is often diagnosed at late stages despite that the oral cavity is readily accessible for visual inspection, and the majority of oral cancers are preceded by noticeable mucosal changes (Ojeda et al, 2020). The use of artificial intelligence to identify suspicious oral mucosal changes can provide a practical, low-cost and universal approach for early detection of oral SCC and OPMDs (Kar et al, 2020;Thankappan et al, 2020;van der Waal, 2018).…”

Section: Discussionmentioning

confidence: 99%

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A novel lightweight deep convolutional neural network for early detection of oral cancer

et al. 2021

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Objectives To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or potentially malignant using standard real‐time clinical images. Methods A small deep CNN, that uses a pretrained EfficientNet‐B0 as a lightweight transfer learning model, was proposed. A data set of 716 clinical images was used to train and test the proposed model. Accuracy, specificity, sensitivity, receiver operating characteristics (ROC) and area under curve (AUC) were used to evaluate performance. Bootstrapping with 120 repetitions was used to calculate arithmetic means and 95% confidence intervals (CIs). Results The proposed CNN model achieved an accuracy of 85.0% (95% CI: 81.0%–90.0%), a specificity of 84.5% (95% CI: 78.9%–91.5%), a sensitivity of 86.7% (95% CI: 80.4%–93.3%) and an AUC of 0.928 (95% CI: 0.88–0.96). Conclusions Deep CNNs can be an effective method to build low‐budget embedded vision devices with limited computation power and memory capacity for diagnosis of oral cancer. Artificial intelligence (AI) can improve the quality and reach of oral cancer screening and early detection.

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Section: Discussionmentioning

confidence: 99%

“…The use of AI methods for image analysis can potentially provide a practical, low-cost and universal approach for early detection of oral cancer (IIhan et al, 2020;Kar et al, 2020;Thankappan et al, 2020;Waal, 2018). Only few studies applied AI methods for early detection of oral cancer using clinical images.…”

Section: Introductionmentioning

confidence: 99%

A novel lightweight deep convolutional neural network for early detection of oral cancer

et al. 2021

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“…No additional articles were found after screening the reference list of initially identified papers. Twenty articles 14,15,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] were eliminated for a reason, and seven articles [36][37][38][39][40][41][42] were eligible and included.…”

Section: Resultsmentioning

confidence: 99%

Cost‐effectiveness of oral cancer screening approaches by visual examination: Systematic review

et al. 2021

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The present study is the first systematic review of papers that have performed a full economic evaluation on oral cancer screening strategies using visual oral examination. The review questions were (1) Is screening a cost-effective strategy in oral cancer? (2) What is the most cost-effective strategy among the different screening approaches in oral cancer? The main outcome measure was the incremental cost-effectiveness ratio. The study identifies and reviews seven full economic evaluations. The included studies scored 75%-100% on the methodological appraisal. Majority of the studies reports that oral cancer screening is a cost-effective strategy, especially in an opportunistic setting and high-risk subset of patients. The results were sensitive to cost and effectiveness parameters. Oral cancer screening, though found cost-effective, the uncertainty around these parameters necessitates additional studies that include better estimates in the modeling assessments. The heterogeneity in studies limited comparison and generalization.

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“…Frontline health workers (FHP) were trained to take images with smartphones and send them to medical experts, who in turn analyzed the images and suggested a diagnostic or possible treatment. The approach was evaluated and validated on more than 45,000 subjects, during a period of eight years (2010–2018) [ 20 , 21 ]. Brown-Johnson et al proposed a mHealth perspective for patients with lung cancer to manage experiences of stigma.…”

Section: Evaluation Of Mhealth Systemsmentioning

confidence: 99%

A Blockchain-Enabled Framework for mHealth Systems

Ţarălungă

Florea

2021

Sensors

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Presently modern technology makes a significant contribution to the transition from traditional healthcare to smart healthcare systems. Mobile health (mHealth) uses advances in wearable sensors, telecommunications and the Internet of Things (IoT) to propose a new healthcare concept centered on the patient. Patients’ real-time remote continuous health monitoring, remote diagnosis, treatment, and therapy is possible in an mHealth system. However, major limitations include the transparency, security, and privacy of health data. One possible solution to this is the use of blockchain technologies, which have found numerous applications in the healthcare domain mainly due to theirs features such as decentralization (no central authority is needed), immutability, traceability, and transparency. We propose an mHealth system that uses a private blockchain based on the Ethereum platform, where wearable sensors can communicate with a smart device (a smartphone or smart tablet) that uses a peer-to-peer hypermedia protocol, the InterPlanetary File System (IPFS), for the distributed storage of health-related data. Smart contracts are used to create data queries, to access patient data by healthcare providers, to record diagnostic, treatment, and therapy, and to send alerts to patients and medical professionals.

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Oral cancer screening using mobile phone-based(mHealth) approach versus conventional oral examination approach, protocol of a cluster randomized study with cost-effectiveness analysis

Cited by 6 publications

References 7 publications

A novel lightweight deep convolutional neural network for early detection of oral cancer

A novel lightweight deep convolutional neural network for early detection of oral cancer

Cost‐effectiveness of oral cancer screening approaches by visual examination: Systematic review

A Blockchain-Enabled Framework for mHealth Systems

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