Machine/deep learning‐assisted hemoglobin level prediction using palpebral conjunctival images

Summary Palpebral conjunctival hue alteration is used in non‐invasive screening for anaemia, whereas it is a qualitative measure. This study constructed machine/deep learning models for predicting haemoglobin values using 150 palpebral conjunctival images taken by a smartphone. The median haemoglobin value was 13.1 g/dL, including 10 patients with <11 g/dL. A segmentation model using U‐net was successfully constructed. The segmented images were subjected to non‐convolutional neural network (CNN)‐based and CNN‐based regression models for predicting haemoglobin values. The correlation coefficients between the actual and predicted haemoglobin values were 0.38 and 0.44 in the non‐CNN‐based and CNN‐based models, respectively. The sensitivity and specificity for anaemia detection were 13% and 98% for the non‐CNN‐based model and 20% and 99% for the CNN‐based model. The performance of the CNN‐based model did not improve with a mask layer guiding the model's attention towards the conjunctival regions, however, slightly improved with correction by the aspect ratio and exposure time of input images. The gradient‐weighted class activation mapping heatmap indicated that the lower half area of the conjunctiva was crucial for haemoglobin value prediction. In conclusion, the CNN‐based model had better results than the non‐CNN‐based model. The prediction accuracy would improve by using more input data with anaemia.