Computer-aided Detection Findings Research Articles

Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism.

Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate the performance of a commercial, computer-aided detection algorithm of computed tomography pulmonary angiograms regarding the presence of pulmonary embolism in the emergency room. This retrospective study includes all pulmonary computed tomography angiogram studies performed in a large emergency department over a period of 36months that were analyzed by two radiologists experienced in emergency radiology to set a reference standard. Original reports and computer-aided detection results were compared regarding the detection of lobar, segmental, and subsegmental pulmonary embolism. All computer-aided detection findings were analyzed concerning the underlying pathology. False-positive findings were correlated to the contrast-to-noise ratio. Expert reading revealed pulmonary embolism in 182 of 1229 patients (49 % men, 10-97 years) with a total of 504emboli. The computer-aided detection algorithm reported 3331 findings, including 258 (8 %) true-positive findings and 3073 (92 %) false-positive findings. Computer-aided detection analysis showed a sensitivity of 47 % (95 %CI: 33-61 %) on the lobar level and 50 % (95 %CI 43-56 %) on the subsegmental level. On average, there were 2.25 false-positive findings per study (median 2, range 0-25). There was no significant correlation between the number of false-positive findings and the contrast-to-noise ratio (Spearman's Rank Correlation Coefficient = 0.09). Soft tissue (61.0 %) and pulmonary veins (24.1 %) were the most common underlying reasons for false-positive findings. Applied to a population at a large emergency room, the tested commercial computer-aided detection algorithm faced relevant performance challenges that need to be addressed in future development projects. · Computed tomography pulmonary angiograms are frequently acquired in emergency radiology.. · Computer-aided detection algorithms (CADs) can support image analysis.. · CADs face challenges regarding false-positive and false-negative findings.. · Radiologists using CADs need to be aware of these limitations.. · Further software improvements are necessary ahead of implementation in the daily routine.. · Müller-Peltzer K, Kretzschmar L, Negrão de Figueiredo G et al. Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism. Fortschr Röntgenstr 2021; 193: 1436 - 1444.

An Observer Study for a Computer-Aided Reading Protocol (CARP) in the Screening Environment for Digital Mammography

The aims of this study were to investigate improving work flow efficiency by shortening the reading time of digital mammograms using a computer-aided reading protocol (CARP) in the screening environment and to increase detection sensitivity using CARP, compared to the current protocol, commonly referred to as the quadrant view (QV). A total of 200 cases were selected for a receiver-operating characteristic (ROC) study to evaluate two image display work flows, CARP and QV, in the screening environment. A Web-based tool was developed for scoring, reporting, and statistical analysis. Cases were scored for and stratified by difficulty. A total of six radiologists of differing levels of training ranging from dedicated mammographers to senior radiology residents participated. Each was timed while interpreting the 200 cases in groups of 50, first using QV and then, after a washout period, using CARP. The data were analyzed using ROC and κ analysis. Interpretation times were also assessed. Using QV, readers' average area under the ROC curve was 0.68 (range, 0.54-0.73). Using CARP, readers' average area under the ROC curve was 0.71 (range, 0.66-0.75). There was no statistically significant difference in reader performance using either work flow. However, there was a statistically significant reduction in the average interpretation time of negative cases from 64.7 seconds using QV to 58.8 seconds using CARP. CARP determines the display order of regions of interest depending on computer-aided detection findings. This is a variation of traditional computer-aided detection for digital mammography that has the potential to reduce interpretation times of studies with negative findings without significantly affecting sensitivity, thus allowing improved work flow efficiency in the screening environment, in which, in most settings, the majority of cases are negative.