LOCALIZATION OF TABLES AND PLOTS IN DOCUMENTS USING DEEP NEURAL NETWORKS

There has been an immense increase in number of scientific publications being published every single day, it has been increasingly difficult to keep up with all the new results being published. In this research, we localized and detected all the plots and tables from documents using deep neural networks. We generated a custom document dataset and manually annotated it to train and evaluate object detection models and their customizability. We used two Single shot multi detector models with base model of MobileNet, RetinaNet and CenterNet model. We trained these models over 10000 epochs on the custom generated dataset. All three models were able to localize and detect the plots and tables with accurately predicted bounding boxes. The results were as follows with CenterNet having the highest mAP score of 92 and highest AR of 93.88 followed by RetinaNet with mAP score of 91.1 and AR of 93.76 lastly, MobileNet based SSD with mAP score of 89.04 and AR of 91.54

Alcohol Activates the Hedgehog Pathway and Induces Related Procarcinogenic Processes in the Alcohol-Preferring Rat Model of Hepatocarcinogenesis

Alcohol consumption promotes hepatocellular carcinoma (HCC). The responsible mechanisms are not well understood. Hepatocarcinogenesis increases with age and is enhanced by factors that impose a demand for liver regeneration. Because alcohol is hepatotoxic, habitual alcohol ingestion evokes a recurrent demand for hepatic regeneration. The alcohol-preferring (P) rat model mimics the level of alcohol consumption by humans who habitually abuse alcohol. Previously, we showed that habitual heavy alcohol ingestion amplified age-related hepatocarcinogenesis in P-rats, with over 80% of alcohol-consuming P rats developing HCCs after 18 months of alcohol exposure, compared to only 5% of water-drinking controls

Validation of a bone scan positivity risk table in non-metastatic castration-resistant prostate cancer.

ObjectivesTo test the external validity of a previously developed risk table, designed to predict the probability of a positive bone scan among men with non-metastatic (M0) castration-resistant prostate cancer (CRPC), in a separate cohort.Patients and methodsWe retrospectively analysed 429 bone scans of 281 patients with CRPC, with no known previous metastases, treated at three Veterans Affairs Medical Centers. We assessed the predictors of a positive scan using generalized estimating equations. Area under the curve (AUC), calibration plots and decision-curve analysis were used to assess the performance of our previous model to predict a positive scan in the current data.ResultsA total of 113 scans (26%) were positive. On multivariable analysis, the only significant predictors of a positive scan were log-transformed prostate-specific antigen (PSA): hazard ratio (HR) 2.13; 95% confidence interval (CI) 1.71-2.66 (P < 0.001) and log-transformed PSA doubling time (PSADT): HR 0.53; 95% CI 0.41-0.68 (P < 0.001). Among men with a PSA level <5 ng/mL, the rate of positive scans was 5%. The previously developed risk table had an AUC of 0.735 to predict positive bone scan with excellent calibration, and provided additional net benefit in the decision-curve analysis.ConclusionWe have validated our previously developed table to predict the risk of a positive bone scan among men with M0/Mx CRPC. Use of this risk table may allow better tailoring of patients' scanning to identify metastases early, while minimizing over-imaging. Regardless of PSADT, positive bone scans were rare in men with a PSA <5 ng/mL

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society