Prediction of age and sex from paranasal sinus images using a deep learning network

• Published in: Medicine (Baltimore) Vol. 100; no. 7; p. e24756
• Main Authors: Kim, Dong-Kyu, Cho, Bum-Joo, Lee, Myung-Je, Kim, Ju Han
• Format: Journal Article
• Language: English
• Published: United States Lippincott Williams & Wilkins 19.02.2021
• Subjects: Adult; Aged; Algorithms; Area Under Curve; Data Management; Databases, Factual; Deep Learning - statistics & numerical data; Female; Humans; Male; Maxillary Sinus - diagnostic imaging; Middle Aged; Neural Networks, Computer; Observational Study; Paranasal Sinuses - diagnostic imaging; Predictive Value of Tests; Radiography - methods; Retrospective Studies; Sensitivity and Specificity
• ISSN: 0025-7974; 1536-5964
• DOI: 10.1097/MD.0000000000024756

This study was conducted to develop a convolutional neural network (CNN)-based model to predict the sex and age of patients by identifying unique unknown features from paranasal sinus (PNS) X-ray images.We employed a retrospective study design and used anonymized patient imaging data. Two CNN models, adopting ResNet-152 and DenseNet-169 architectures, were trained to predict sex and age groups (20-39, 40-59, 60+ years). The area under the curve (AUC), algorithm accuracy, sensitivity, and specificity were assessed. Class-activation map (CAM) was used to detect deterministic areas. A total of 4160 PNS X-ray images were collected from 4160 patients. The PNS X-ray images of patients aged ≥20 years were retrieved from the picture archiving and communication database system of our institution. The classification performances in predicting the sex (male vs female) and 3 age groups (20-39, 40-59, 60+ years) for each established CNN model were evaluated.For sex prediction, ResNet-152 performed slightly better (accuracy = 98.0%, sensitivity = 96.9%, specificity = 98.7%, and AUC = 0.939) than DenseNet-169. CAM indicated that maxillary sinuses (males) and ethmoid sinuses (females) were major factors in identifying sex. Meanwhile, for age prediction, the DenseNet-169 model was slightly more accurate in predicting age groups (77.6 ± 1.5% vs 76.3 ± 1.1%). CAM suggested that the maxillary sinus and the periodontal area were primary factors in identifying age groups.Our deep learning model could predict sex and age based on PNS X-ray images. Therefore, it can assist in reducing the risk of patient misidentification in clinics.