Letter to the Editor: Potential pitfalls in deep learning-based imaging for spontaneous intracerebral hemorrhage

The study by Yang and Li in the World Journal of Radiology reports strong predictive performance for hematoma enlargement, perihematomal edema (PHE) and hospital mortality using hand crafted quantitative radiomics and deep learning models in patients with spontaneous intracerebral haemorrhage (ICH). While the authors report strong predictive performance using quantitative radiomics and deep learning features, several methodological concerns warrant discussion. By letting the deep learning models based on pretrained Convolutional Neural Network (CNN) to detect the density range of 50-400 Hounsfield units, may falsely detect age related and pathological basal ganglionic calcifications as heterogeneity within the ICH. This may lead to increased false positive rates in the prediction of hematoma enlargement. Similarly, by not restricting the density range between 25-35 Hounsfield units for pretrained CNN to detect PHE, may also add bias by falsely detecting age related micro ischaemic areas and chronic lacunar infarcts in basal ganglionic region as PHE. Further, hand crafted quantitative radiomics by trained clinical radiologists will calculate volume of ICH and PHE more accurately as compared to the CNN models using only three consecutive axial slices centered on the maximum haematomal cross-sectional area, as reported in the original study. Hence, an integrated model consisting of hand crafted quantitative radiomics by trained clinical radiologist with deep learning models based on pre trained CNN with the above-mentioned enhancements and a human touch by a trained clinical radiologist to remove the mentioned confounding factors is highly recommended for better outcomes.