CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography.

OBJECTIVES: To assess the effectiveness of computer-aided detection (CAD) as a second reader or concurrent reader in helping radiologists who are moderately experienced in computed tomographic colonography (CTC) to detect colorectal polyps. METHODS: Seventy CTC datasets (34 patients: 66 polyps ≥6 mm; 36 patients: no abnormalities) were retrospectively reviewed by seven radiologists with moderate CTC experience. After primary unassisted evaluation, a CAD second read and, after a time interval of ≥4 weeks, a CAD concurrent read were performed. Areas under the receiver operating characteristic (ROC) curve (AUC), along with per-segment, per-polyp and per-patient sensitivities, and also reading times, were calculated for each reader with and without CAD. RESULTS: Of seven readers, 86% and 71% achieved a higher accuracy (segment-level AUC) when using CAD as second and concurrent reader respectively. Average segment-level AUCs with second and concurrent CAD (0.853 and 0.864) were significantly greater (p < 0.0001) than average AUC in the unaided evaluation (0.781). Per-segment, per-polyp, and per-patient sensitivities for polyps ≥6 mm were significantly higher in both CAD reading paradigms compared with unaided evaluation. Second-read CAD reduced readers' average segment and patient specificity by 0.007 and 0.036 (p = 0.005 and 0.011), respectively. CONCLUSIONS: CAD significantly improves the sensitivities of radiologists moderately experienced in CTC for polyp detection, both as second reader and concurrent reader. KEY POINTS: • CAD helps radiologists with moderate CTC experience to detect polyps ≥6 mm. • Second and concurrent read CAD increase the radiologist's sensitivity for detecting polyps ≥6 mm. • Second read CAD slightly decreases specificity compared with an unassisted read. • Concurrent read CAD is significantly more time-efficient than second read CAD.

Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models.

INTRODUCTION: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates. METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase). RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.