Development and assessment of deep learning system for the location and classification of rib fractures via computed tomography.

PURPOSE: The purpose of this study was to evaluate the performance of a deep learning system for the automatic diagnosis and classification of rib fractures. METHODS: This retrospective study analyzed computed tomography (CT) data of patients diagnosed with a rib fracture between 1 January 2019 and 23 July 2020 in two hospitals, including 591 patients from Suzhou TCM hospital and 75 patients from Jintan TCM hospital. A deep learning system (Dr.Wise@ChestFracture v1.0) based on a convolutional neural network framework was used as a diagnostic tool, and a human-model comparison experiment was designed to compare the diagnostic efficiencies of the deep learning system and radiologists. Furthermore, a secondary classification model was established to distinguish the different types of fracture. First, a classification model to differentiate between fresh and old fractures was developed. Second, a submodel to determine any misalignment in fresh fractures was established. RESULTS: For all fracture types, the detection efficiency (recall) of the system was statistically significantly better than that of radiologists with different levels of experience (all p < 0.0167 except for senior radiologists). The F1-score of the system for diagnosing rib fractures was similar to that of the radiologists. The system was much faster than the radiologists in assessing rib fractures (all p < 0.0167). The two classification models can distinguish between fresh and old fractures (accuracy = 87.63%) and determine whether there is any misalignment in fresh fractures (accuracy = 95.22%) or not. CONCLUSION: The use of a deep learning system can accurately, automatically, and rapidly diagnose and classify rib fractures, helping doctors improve the diagnostic efficiency and reducing their workload. The classification models can distinguish different types of rib fracture well.

The effect of pitavastatin calcium on endothelial dysfunction induced by hypercholesterolemia.

OBJECTIVE: To investigate whether endothelial function can be improved by the treatment of pitavastatin calcium via its antioxidant properties in hypercholesteremia patients. METHODS: Forty patients with hypercholesteremia were randomized to receive pitavastatin calcium 1 or 2 mg/day for 8 weeks. Among them, four people were lost in the follow-up period. Before and after treatment, clinical and biochemical characteristics, markers of oxidative stress (plasma 8-iso-prostaglandin F(2α) and serum gp91phox) were determined and concomitantly endothelium-dependent brachial artery flow-mediated dilation (FMD) was measured by ultrasound examination. Thirty healthy subjects were chosen as controls. RESULTS: For individuals with hypercholesteremia, total cholesterol, low-density lipoprotein cholesterol (LDL-C) and serum gp91phox were significantly increased (p<0.001 for all) and plasma 8-iso-prostaglandinF2α (8-iso-PGF2α) was significantly higher (p<0.05), while FMD was obviously impaired (p<0.001). Total cholesterol, LDL-C and serum gp91phox were significantly reduced (p<0.001 for all), plasma 8-iso-PGF2α was lower and FMD was significantly improved after pitavastatin calcium treatment compared with those before treatment in any group (p<0.05 for both). However, there was no significant difference between the 1-mg and 2-mg pitavastatin calcium groups post-therapy. CONCLUSIONS: Endothelial dysfunction induced by hypercholesteremia can be ameliorated by pitavastatin calcium treatment, which occurs in part through its antioxidative properties.