Mobile oral cancer screening system for low-resource settings
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 350
- 试验地点
- 3
- 主要终点
- The proposed dual mode intraoral imaging system will deliver new & imperatively
研究概览
简要总结
Oral cancer is the most common cancer in India, accounting for 40% of all cancers overall and accounting for one-third of the world burden. The poor survival rate in India is mainly due to late diagnosis and the resultant progression of disease to an advanced stage at diagnosis. Therefore, there exists an urgent need for low-cost, easy to use imaging device that enables oral cancer screening and triage patients in Low- Resource Settings (LRS). The major goal of the study is to validate a low cost dual mode mobile intraoral imaging system for oral cancer detection in LRS. This project provides early detection of oral lesions and timely patient referral to specialists can avoid disease progression, reduce morbidity and mortality. The successful demonstration of this mobile dual mode intra-oral imaging screening system will lead to a sustainable solution for early detection of oral cancers in community settings, eventually improving oral cancer detection rates, treatment outcomes, and quality of life of patients in LRS.
The project team has developed mobile imaging platform that specifically addresses critical barriers to improve oral cancer screening and management in LRS. The mobile imaging platform consists of a smart phone, mobile App, intraoral imaging probe, cloud computing, and web application. More than 5,000 patients in three locations in India (cancer center, dental school, and remote region) were screened to demonstrate the performance. Remote specialists using the mobile oral cancer screening data achieved diagnostic sensitivities and specificities of 92% and 85% respectively vs. the standard-of-care (SOC). A convolutional neural network (CNN) based deep learning method for classifying oral images and achieved sensitivity of 87% and specificity of 87%, for delineating oral cancer and pre-cancer lesions. The aim of this research
is to modify the dual mode mobile imaging platform (prototype) for real time clinical use in low resource setting.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- 18.00 Year(s) 至 95.00 Year(s)（—）
- 性别
- All
入选标准
- •ALl individuals above 18 years and having Clinically suspicious oral lesions which are indicated for biopsy.
排除标准
- •Individuals less than 18 years
- •Pregnant women
- •Individuals currently undergoing treatment for malignancy
- •Individuals having acute illness or undergoing treatment for tuberculosis.
结局指标
主要结局
The proposed dual mode intraoral imaging system will deliver new & imperatively
时间窗: 1. Optimize a mobile imaging system for low-resource setting in year 1 | 2. Develop mobile based deep learning image classification in year 1 | 3. Validate the clinical usefulness of the mobile imaging system in year 2
needed capabilities to the end users in low resource setting. This artificial intelligence
时间窗: 1. Optimize a mobile imaging system for low-resource setting in year 1 | 2. Develop mobile based deep learning image classification in year 1 | 3. Validate the clinical usefulness of the mobile imaging system in year 2
integrated system will: (1) detect suspicious regions with high sensitivity & specificity;
时间窗: 1. Optimize a mobile imaging system for low-resource setting in year 1 | 2. Develop mobile based deep learning image classification in year 1 | 3. Validate the clinical usefulness of the mobile imaging system in year 2
(2) enable automatic, accurate, & objective in situ diagnosis; & in screening program
时间窗: 1. Optimize a mobile imaging system for low-resource setting in year 1 | 2. Develop mobile based deep learning image classification in year 1 | 3. Validate the clinical usefulness of the mobile imaging system in year 2
settings or nodal center settings, the device will triage suspicious or non-suspicious
时间窗: 1. Optimize a mobile imaging system for low-resource setting in year 1 | 2. Develop mobile based deep learning image classification in year 1 | 3. Validate the clinical usefulness of the mobile imaging system in year 2
lesions.
时间窗: 1. Optimize a mobile imaging system for low-resource setting in year 1 | 2. Develop mobile based deep learning image classification in year 1 | 3. Validate the clinical usefulness of the mobile imaging system in year 2
次要结局
- Final outcome of the study is to achieve Diagnostic sensitivity & specificity of: 1) 95(% for distinguishing OSCC from healthy sites; (2) 90% for distinguishing OPML from)