Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.

COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the worst pandemics in recent history. The identification of patients suspected to be infected with COVID-19 is becoming crucial to reduce its spread. We aimed to validate and test a deep learning model to detect COVID-19 based on chest X-rays. The recent deep convolutional neural network (CNN) RegNetX032 was adapted for detecting COVID-19 from chest X-ray (CXR) images using polymerase chain reaction (RT-PCR) as a reference. The model was customized and trained on five datasets containing more than 15,000 CXR images (including 4148COVID-19-positive cases) and then tested on 321 images (150 COVID-19-positive) from Montfort Hospital. Twenty percent of the data from the five datasets were used as validation data for hyperparameter optimization. Each CXR image was processed by the model to detect COVID-19. Multi-binary classifications were proposed, such as: COVID-19 vs. normal, COVID-19 + pneumonia vs. normal, and pneumonia vs. normal. The performance results were based on the area under the curve (AUC), sensitivity, and specificity. In addition, an explainability model was developed that demonstrated the high performance and high generalization degree of the proposed model in detecting and highlighting the signs of the disease. The fine-tuned RegNetX032 model achieved an overall accuracy score of 96.0%, with an AUC score of 99.1%. The model showed a superior sensitivity of 98.0% in detecting signs from CXR images of COVID-19 patients, and a specificity of 93.0% in detecting healthy CXR images. A second scenario compared COVID-19 + pneumonia vs. normal (healthy X-ray) patients. The model achieved an overall score of 99.1% (AUC) with a sensitivity of 96.0% and specificity of 93.0% on the Montfort dataset. For the validation set, the model achieved an average accuracy of 98.6%, an AUC score of 98.0%, a sensitivity of 98.0%, and a specificity of 96.0% for detection (COVID-19 patients vs. healthy patients). The second scenario compared COVID-19 + pneumonia vs. normal patients. The model achieved an overall score of 98.8% (AUC) with a sensitivity of 97.0% and a specificity of 96.0%. This robust deep learning model demonstrated excellent performance in detecting COVID-19 from chest X-rays. This model could be used to automate the detection of COVID-19 and improve decision making for patient triage and isolation in hospital settings. This could also be used as a complementary aid for radiologists or clinicians when differentiating to make smart decisions.

Association between newborn hypoglycemia screening and breastfeeding success in an Ottawa, Ontario, hospital: a retrospective cohort study.

BACKGROUND: There has been limited investigation of the unintended effects of routine screening for asymptomatic hypoglycemia in at-risk newborns. This study aimed to explore whether rates of exclusive breastfeeding were lower in screened babies than in unscreened babies. METHODS: This retrospective cohort study conducted in Ottawa, Canada, used data from Hôpital Montfort's electronic health information system. Healthy singleton newborns discharged between Feb. 1, 2014, and June 30, 2018, were included. We excluded babies and mothers with conditions expected to interfere with breastfeeding (e.g., twins). We investigated the association between postnatal screening for hypoglycemia and initial exclusive breastfeeding (in the first 24 hours of life). RESULTS: We included 10 965 newborns; of these, 1952 (17.8%) were fully screened for hypoglycemia. Of screened newborns, 30.6% exclusively breastfed and 64.6% took both formula and breastmilk in the first 24 hours of life. Of unscreened newborns, 45.4% exclusively breastfed and 49.8% received both formula and breastmilk. The adjusted odds ratio for exclusive breastfeeding in the first 24 hours of life among newborns screened for hypoglycemia was 0.57 (95% confidence interval 0.51-0.64). INTERPRETATION: The association of routine newborn hypoglycemia screening with a lower initial rate of exclusive breastfeeding suggests a potential effect of screening on early breastfeeding success. Confirmation of these findings might warrant a re-evaluation of the net benefit of asymptomatic postnatal hypoglycemia screening for different newborn populations at risk of hypoglycemia.

Access to psychiatrists by French-speaking patients in Ontario hospitals: 2005 to 2013.

There has been a limited amount of research suggesting that cultural and linguistic variables may affect access to health services, but no study has examined the access of French-speaking Canadians to psychiatrists. The present study used data from the Ontario Mental Health Reporting System to examine patterns of daily contact with psychiatrists in the first 3 days of admission to mental health facilities in Ontario. The results showed that after controlling for a broad range of covariates, French-speaking Ontarians were about one-third as likely to have daily contact with psychiatrists in that time period compared to English-speaking patients. These results were not explained by regional differences. Instead, they point to the possibility that language poses an important barrier to specific and highly specialized mental health services in this province.