The Lancet EClinical Media: Chinese scholars develop a depth algorithm to detect oral cancer, through mobile phone APP detection, accuracy rate of 98%

Oral cancer is one of the malignant tumors that seriously threaten human health.
60% of oral cancer patients treated by oral oncologists are in the middle and late stages, and the cure rate for early oral cancer patients found to be timely and formally treated can reach 84%.
"early detection, early treatment" is the key to improve the cure rate of oral cancer.
Early mucous membrane erythema, white spots, ulcers and decay in the mouth, often because of "painless itch" so that patients and even non-specialists to relax their vigilance, and these abnormal signs in the eyes of oral oncologists are releasing "cancer" warning signs.
If the valuable clinical experience of oral oncologists can be "transferred" by ordinary people and non-specialists in a fast and inexpensive way, enabling them to quickly and accurately identify oral cancerous tissue, it can help more patients get diagnosed as early as possible.
recently, the Lancet sub-journal EClinical Mediaine published online a research paper by Dr. Xiong Xuepeng of Wuhan University Dental Hospital in collaboration with the Wanlin Research Group of the Institute of Geo-Letters of the Chinese University of Geology (Wuhan): A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study.
the study developed a deep learning algorithm based on clinical visual characteristics to automatically detect oral cancer, providing a non-invasive, fast and easy-to-use and inexpensive secondary examination tool for oral cancer screening and early diagnosis.
results can quickly detect the lesions of oral cancer from ordinary clinical oral photos, with high accuracy and sensitivity.
In the study, Dr. Xiong Xuepeng and his team collected a large number of clinical photos of oral cancer patients, modeled the visual characteristics of the lesions using artificial intelligence technology on the basis of medical big data, and after repeated iterations of training and experiments, finally obtained a deep learning algorithm comparable to that of oral cancer experts.
the algorithm can be assembled in the form of an app on the current mainstream smartphone device, to achieve rapid real-time detection of oral cancer.
operators can complete the test within 10 seconds of submitting the model by taking photos of suspected lesions in the mouth, initially determining whether the patient has oral cancer with an accuracy rate of up to 98%.
the study has broad clinical applications, especially in areas where medical resources are scarce, a mobile phone with intelligent detection algorithms could be a weapon for oral cancer detection.
study, the first paper to use the clinical visual characteristics of oral cancer for disease detection, was presented on the homepage of The Oral Cancer Foundation, an international non-profit organization.
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