Cervical spine fracture detection in computed tomography using convolutional neural networks.

Objective.In the context of primary in-hospital trauma management timely reading of computed tomography (CT) images is critical. However, assessment of the spine is time consuming, fractures can be very subtle, and the potential for under-diagnosis or delayed diagnosis is relevant. Artificial intelligence is increasingly employed to assist radiologists with the detection of spinal fractures and prioritization of cases. Currently, algorithms focusing on the cervical spine are commercially available. A common approach is the vertebra-wise classification. Instead of a classification task, we formulate fracture detection as a segmentation task aiming to find and display all individual fracture locations presented in the image.Approach.Based on 195 CT examinations, 454 cervical spine fractures were identified and annotated by radiologists at a tertiary trauma center. We trained for the detection a U-Net via four-fold-cross validation to segment spine fractures and the spine via a multi-task loss. We further compared advantages of two image reformation approaches-straightened curved planar reformatted (CPR) around the spine and spinal canal aligned volumes of interest (VOI)-to achieve a unified vertebral alignment in comparison to processing the Cartesian data directly.Main results.Of the three data versions (Cartesian, reformatted, VOI) the VOI approach showed the best detection rate and a reduced computation time. The proposed algorithm was able to detect 87.2% of cervical spine fractures at an average number of false positives of 3.5 per case. Evaluation of the method on a public spine dataset resulted in 0.9 false positive detections per cervical spine case.Significance.The display of individual fracture locations as provided with high sensitivity by the proposed voxel classification based fracture detection has the potential to support the trauma CT reading workflow by reducing missed findings.

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies.

BACKGROUND: Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services. METHODS: In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors. RESULTS: 4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results. CONCLUSION: Complementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT. TRIAL REGISTRATION: German Clinical Trials Register (DRKS-ID: DRKS00023593).

Insufficient sensitivity of joint aspiration during the two-stage exchange of the hip with spacers.

BACKGROUND: Evaluation of infection persistence during the two-stage exchange of the hip is challenging. Joint aspiration before reconstruction is supposed to rule out infection persistence. Sensitivity and specificity of synovial fluid culture and synovial leucocyte count for detecting infection persistence during the two-stage exchange of the hip were evaluated. METHODS: Ninety-two aspirations before planned joint reconstruction during the two-stage exchange with spacers of the hip were retrospectively analyzed. RESULTS: The sensitivity and specificity of synovial fluid culture was 4.6 and 94.3%. The sensitivity and specificity of synovial leucocyte count at a cut-off value of 2000 cells/µl was 25.0 and 96.9%. C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) values were significantly higher before prosthesis removal and reconstruction or spacer exchange (p = 0.00; p = 0.013 and p = 0.039; p = 0.002) in the infection persistence group. Receiver operating characteristic area under the curve values before prosthesis removal and reconstruction or spacer exchange for ESR were lower (0.516 and 0.635) than for CRP (0.720 and 0.671). CONCLUSIONS: Synovial fluid culture and leucocyte count cannot rule out infection persistence during the two-stage exchange of the hip.