An anemia screening tool based on deep learning with conjunctiva images

Abstract

Objective To explore the application of deep learning in automatic classification of anemia with conjunctival images as input. Methods The conjunctival images of 284 patients undergoing elective surgery in the Department of Anesthesiology of the First Affiliated Hospital of Army Medical University from March 18 to April 26, 2021 were collected and analyzed prospectively. The images divided into 2 types: normal and anemia according to the corresponding hemoglobin concentration. Four deep learning algorithms, including InceptionV3, ResNet50V2, EfficientNetV2B0 and DenseNet121, were used to construct a prediction model for anemia. The performance of the model was evaluated by receiver operating characteristic (ROC) curve with accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Results The area under ROC curve (AUC) was 0.709 (95%CI: 0.643~0.769), 0.661 (95%CI: 0.594~0.725), 0.670 (95%CI: 0.603~0.733), and 0.695 (95%CI: 0.628~0.756), respectively for the 4 deep learning algorithms. The InceptionV3 model showed superior predictive performance on the test set, with an AUC value of 0.709 (95%CI: 0.643~0.769), an accuracy of 0.695, a sensitivity of 0.750, a specificity of 0.412, a positive predictive value of 0.707 and a negative predictive value of 0.629. Based on the optimal algorithm, a network service application which can be used for online prediction of anemia was developed (http://150.158.58.4). Conclusion Our model, which is established based on deep learning algorithm with conjunctiva image as input, has a good performance on fast and automatic prediction for anemia. The InceptionV3model has better comprehensive prediction performance.

Keywords

• deep learning
• anemia
• conjunctiva
• non-invasiveness