Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

[Effect of Jianpi Shengxue Granule on Fertility and Early Embryo Development Toxicity of Rats].

Objective To assess the effects of Jianpi Shengxue Granule (JSG) on the fertility and early embryo development in rats. Methods Totally 176 SD rats were stratified by sex, and divided into 4 groups, i.e., the control group, low, medium, high dose JSG treatment groups. Rats in the 3 JSG treatment groups were administered with JSG at 0, 1. 16, 3. 48, 5. 80 g/kg per day by gastrogavage, re- spectively. After 2 weeks of administration for females and 4 weeks of administration for males, males and females were caged in the ratio of 1:1. Females were administered to the gestation day 7. Males were administered for 59 -63 days until the day before anatomy. All parental generations were anatomized to observe signs and morphologies. Pathological examination was performed. General toxicity was detected. The testis and epididymis were weighed. Spermatozoa number was counted from epididymis, and repro- duction toxicity of sperm motility was checked. The numbers of corpus luteum, live fetus, dead fetus, and absorbed fetus were counted. The implantation number was calculated to observe early embryo de- velopment toxicity. Results (1) General toxicity: The body weight growth slowed down in male rats of the high dose JSG treatment group. No abnormality was found in low and medium dose JSG treatment groups. (2)Fertility toxicity: There was no obvious toxic effect on testis, epididymis, and spermatozoa number from epididymis sperm in all JSG treatment groups. (3) Early embryo development toxicity: No significant effect on the formation of early embryos was found in all JSG treatment groups. Conclusions Certain gastrointestinal toxicity of JSG might exist to some extent. The dose for non-adverse effect of JSG on fertility and early embryo development was 3. 38 g/kg per day. No obvious fertility or early embryo development toxicity occurred in each JSG treatment group.

[Laparoscopic choledochocele resection report of 48 cases].

OBJECTIVE: To summarize surgery experience and efficiency of laparoscopic excision of choledochal cyst and reconstruction of biliary tract and to analyze the treatments administered during intra and post-operation of the biliary reconstruction. METHODS: We enrolled 48 cases of video-guided laparoscopic intraoperative cholangiography, cholecystectomy, choledochocele resection, Roux-en-Y hepaticojejunostomy with an anti-reflux valve from August, 2001 to October, 2003. Their operative procedures and aims were retrospectively analyzed. RESULTS: Forty-four out of 48 patients successfully underwent laparoscopy and recovered soon. Only 4 cases were transformed to open operation. The post-operation complications were found in 4 cases. CONCLUSION: Video-guided laparoscopic excision of choledochal cyst, biliary tract reconstruction and Roux-en-Y hepaticojejunostomy are an effective method of treatment with advantages of minimal injury, less bleeding, and sooner recovery.