Identification of the Facial Features of Patients With Cancer: A Deep Learning–Based Pilot Study

Abstract

Background Cancer has become the second leading cause of death globally. Most cancer cases are due to genetic mutations, which affect metabolism and result in facial changes. Objective In this study, we aimed to identify the facial features of patients with cancer using the deep learning technique. Methods Images of faces of patients with cancer were collected to build the cancer face image data set. A face image data set of people without cancer was built by randomly selecting images from the publicly available MegaAge data set according to the sex and age distribution of the cancer face image data set. Each face image was preprocessed to obtain an upright centered face chip, following which the background was filtered out to exclude the effects of nonrelative factors. A residual neural network was constructed to classify cancer and noncancer cases. Transfer learning, minibatches, few epochs, L2 regulation, and random dropout training strategies were used to prevent overfitting. Moreover, guided gradient-weighted class activation mapping was used to reveal the relevant features. Results A total of 8124 face images of patients with cancer (men: n=3851, 47.4%; women: n=4273, 52.6%) were collected from January 2018 to January 2019. The ages of the patients ranged from 1 year to 70 years (median age 52 years). The average faces of both male and female patients with cancer displayed more obvious facial adiposity than the average faces of people without cancer, which was supported by a landmark comparison. When testing the data set, the training process was terminated after 5 epochs. The area under the receiver operating characteristic curve was 0.94, and the accuracy rate was 0.82. The main relative feature of cancer cases was facial skin, while the relative features of noncancer cases were extracted from the complementary face region. Conclusions In this study, we built a face data set of patients with cancer and constructed a deep learning model to classify the faces of people with and those without cancer. We found that facial skin and adiposity were closely related to the presence of cancer.

The Impact of COVID-19 on Cancer Screening: Challenges and Opportunities

“…On a higher-technological scale, using a deep learning technique, researchers found that AI can help identify faces of patients with cancer from those without [ 126 ]. This promising finding, not currently in use, suggests that AI-based telemedicine tools have the future potential to assist patients and health care practitioners with cancer screening and improve screening accuracy.…”

A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts

“…Recent work has demonstrated that under certain conditions individuals can be reidentified from seemingly anonymous MRI scan images [ 11 ]. Patients’ medical images have inadvertently appeared in Google image searches [ 12 ], and analysis of human faces can enable inferences about one’s health status [e.g., 13 – 20 ] and even one’s genomic information [ 21 ]. Understandably, many in and out of the precision health research community wonder whether the measures taken to ensure responsible stewardship of facial imaging and imaging-derived data are appropriate and adequate.…”

Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights

“…With AI technologies, machines can identify patterns too intricate for humans to identify and process quickly. AI techniques are widely used in areas such as natural language processing, speech recognition, machine vision, targeted marketing, and health care, including efforts to combat COVID-19 [96][97][98][99]. While technologies such as virtual reality, smart sensors, drones, and robotics could play a positive role in supporting health care professionals to cope with the pandemic [100][101][102], AI technologies are arguably most instrumental in addressing some of the most prominent issues health experts and government officials are faced with, ranging from pandemic surveillance to COVID-19 drug and vaccine development [103][104][105][106].…”