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A Mobile Online Platform and Deep-Learning AI for Oral Cancer Screening

วสิศ ลิ้มประเสริฐ; Wasit Limprasert; กฤษสิทธิ์ วารินทร์; Kritsasith Warin;
Date: 2566-06
Abstract
The research in this online mobile platform and deep learning artificial intelligence for oral cancer screening project aims 1) To develop an online platform for collecting clinical images of lesions at may change into oral cancer and oral cancer 2) To develop deep learning artificial intelligence technology for the screening of lesions that may turn into oral cancer and oral cancer from a database of clinical photographs and biopsy results in a developed online platform; and 3) To develop an automated oral cancer screening system for assisting medical personnel in oral cancer screening for oral cancer surveillance in areas lacking specialists using a qualitative research model. The samples were patients at risk of developing oral cancer and medical personnel/dentists/medical students which brings the oral cancer screening application to trial use. The results of this research revealed the development of an online platform for collecting clinical images of lesions that may change into oral cancer and oral cancer. There are 350 images of oral cancer images, 300 images of lesions that may be transformed into oral cancer, and 300 images of lesions that may be transformed into oral cancer, and 300 images of normal tissues in the oral cavity, 350 mouth images (this is the minimum number of images that can be used to create a deep learning model. The number of images will increase in the future according to the use of the developed system, which will make the automatic screening system more accurate), with the entry criteria being intraoral photographs of oral cancer and lesions. Before all oral cancers were confirmed by standard lesion diagnostic methods, namely pathological biopsy and the exclusion criterion was intraoral photograph data without pathological confirmation and incomplete oral photographic data for use as prototype images for the development of automatic screening systems. Development of deep-learning artificial intelligence technology for the screening of lesions that may turn into oral cancer and oral cancer. From a database of clinical photographs and biopsy results in a developed online platform, the analysis process is to mark the location of the lesion from the image (annotate) before analyzing the lesion with a program by a dental specialist in oral surgery. Then, a deep-learning artificial intelligence program was used to classify clinical images of oral cancer and oral precancerous lesions. And development of an automated oral cancer screening system assists medical personnel in oral cancer screening for oral cancer surveillance in areas lacking specialists by medical personnel able to take oral pictures of patients at risk of developing oral cancer with a smartphone. And can choose the picture to send to the server for analysis for preliminary diagnosis including being able to contact experts to consult the analysis results. From the development of mobile online platforms and deep learning artificial intelligence. For oral cancer screening PrimeKG (Precision Medicine Knowledge Graph) has been shown to have the potential to help a new generation of dentists and researchers understand the relationship between risk factors and oral cancer. Deep learning (Deep Learning) to detect oral cancer from pictures taken by smartphones. It has added significant potential to this application for effective diagnostics. A new generation of dentists and researchers can compare their own diagnoses with the results obtained from the model. In addition, the usability and utility of the application was evaluated by 100 subjects, showing good results in increasing the knowledge and diagnostic skills of young dentists and researchers. By evaluation, it was found that the application is highly effective in adding new knowledge and is useful in identifying associations between risk factors and oral cancer.
Copyright ผลงานวิชาการเหล่านี้เป็นลิขสิทธิ์ของสถาบันวิจัยระบบสาธารณสุข หากมีการนำไปใช้อ้างอิง โปรดอ้างถึงสถาบันวิจัยระบบสาธารณสุข ในฐานะเจ้าของลิขสิทธิ์ตามพระราชบัญญัติสงวนลิขสิทธิ์สำหรับการนำงานวิจัยไปใช้ประโยชน์ในเชิงพาณิชย์
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