The artificial intelligence watcher predicts cancer risk by facial features

Background: Early diagnosis of malignant tumors effectively reduces the mortality rate. The special craniofacial structure serves as a diagnosis basis of early screening for many hereditary diseases. However, cancer is also considered a genetic disorder. Could facial images direct tumor screening? Methods: We developed an image recognition program, the artificial intelligence watcher, which could extract implicit knowledge from faces and distinguish cancer patients from normal persons. The artificial intelligence program used a convolution neural network with 6 layers. Then, we conducted a retrospective clinical study of 643 cancer patients and 219 local normal people at 20-80 ages from China to analyze their photos and medical history. By analyzing their facial features, disease and family gene sequencing, the relationship between appearance and cancer can be inferred. Results: We demonstrated that the accuracy of artificial intelligence watcher achieved up to about 90%. Statistical results showed lung and gastric cancer patients have more narrow-set eyes. Furthermore, we suspected the physiological basis for artificial intelligence watcher is craniofacial genes are closely related to cancer susceptibility genes. Through the analysis of the tumor database and craniofacial development gene database, we found many single nucleotide polymorphism mutations related to appearance are also related to the tumor, in particular, WW domain containing E3 ubiquitin protein ligase 2, SH3 and PX domains 2B, DNA-directed RNA polymerase III core subunit and coatomer subunit zeta. Conclusion: According to our gene sequencing results, there are some polymorphisms of the same locus in patients with similar facial features different from their healthy relatives, such as WW domain containing E3 ubiquitin protein ligase 2 and ATP binding cassette subfamily A member 4 genes. Our model provides a new efficient auxiliary diagnosis method for tumor screening.