A Method for Detecting and Analyzing Facial Features of People with Drug Use Disorders.

Drug use disorders caused by illicit drug use are significant contributors to the global burden of disease, and it is vital to conduct early detection of people with drug use disorders (PDUD). However, the primary care clinics and emergency departments lack simple and effective tools for screening PDUD. This study proposes a novel method to detect PDUD using facial images. Various experiments are designed to obtain the convolutional neural network (CNN) model by transfer learning based on a large-scale dataset (9870 images from PDUD and 19,567 images from GP (the general population)). Our results show that the model achieved 84.68%, 87.93%, and 83.01% in accuracy, sensitivity, and specificity in the dataset, respectively. To verify its effectiveness, the model is evaluated on external datasets based on real scenarios, and we found it still achieved high performance (accuracy > 83.69%, specificity > 90.10%, sensitivity > 80.00%). Our results also show differences between PDUD and GP in different facial areas. Compared with GP, the facial features of PDUD were mainly concentrated in the left cheek, right cheek, and nose areas (p < 0.001), which also reveals the potential relationship between mechanisms of drugs action and changes in facial tissues. This is the first study to apply the CNN model to screen PDUD in clinical practice and is also the first attempt to quantitatively analyze the facial features of PDUD. This model could be quickly integrated into the existing clinical workflow and medical care to provide capabilities.