Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study
ConclusionsThe 3D U-Net model showed good performance for CG and PZ auto-segmentation in all the testing groups and outperformed the junior radiologist for PZ segmentation. The model performance was susceptible to prostate morphology and MR scanner parameters. (Source: Insights into Imaging)
Source: Insights into Imaging - March 16, 2023 Category: Radiology Source Type: research
New transperineal ultrasound-guided biopsy for men in whom PSA is increasing after Miles ’ operation
ConclusionsNew TPUS-guided biopsy technique may contribute to detecting large PI-RADS 5 prostate cancer in men after Miles ’ operation. (Source: Insights into Imaging)
Source: Insights into Imaging - March 16, 2023 Category: Radiology Source Type: research
Does clinical decision support system promote expert consensus for appropriate imaging referrals? Chest –abdominal–pelvis CT as a case study
ConclusionsAccording to both the experts and the ESR iGuide, inappropriate testing was prevalent, in terms of both frequency of the scans and also inappropriately chosen body regions. These findings raise the need for unified workflows that might be achieved using a CDSS. Further studies are needed to investigate the CDSS contribution to the informed decision-making and increased uniformity among different expert physicians when ordering the appropriate test. (Source: Insights into Imaging)
Source: Insights into Imaging - March 16, 2023 Category: Radiology Source Type: research
Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing
ConclusionDue to its unique ability to integrate speech into SR, this novel tool could represent a major contribution to the future of reporting. (Source: Insights into Imaging)
Source: Insights into Imaging - March 16, 2023 Category: Radiology Source Type: research
Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence algorithm
ConclusionOur deep learning AI model demonstrated a higher sensitivity for detection of animal bone impaction on lateral neck radiographs without an increased false positive rate. The application of this model in a clinical setting may effectively reduce time to diagnosis, accelerate workflow, and decrease the use of CT. (Source: Insights into Imaging)
Source: Insights into Imaging - March 16, 2023 Category: Radiology Source Type: research
