Citations

Objective: Pulmonary air trapping is critical for diagnosing and prognostication of various lung diseases. Expiratory CT imaging serves as an accessible method to assess air trapping, which correlates with small airway disease outcomes. Air trapping manifests as mosaic attenuation on inspiratory chest CT that is difficult for visual estimation. The primary aim of this study was to develop an automated tool to quantify mosaic attenuation on inspiratory CT and air trapping on paired expiratory CT. Secondary aims included comparing CT-derived parameters with PFT measurements and dyspnea scores. Methods: This retrospective analysis of noncontrast chest CTs from 2 academic hospitals was conducted between January 1, 2018, and December 31, 2019. Patients with paired inspiratory and expiratory CT chest scans and PFTs performed on the same day were included. A chest radiologist manually annotated lung parenchyma in a reference cohort. Several histogram-based metrics were computed from lung parenchymal CT values, with the maximum peak position showing the strongest correlation with manually determined thresholds. This threshold, derived from the histogram peak, was applied in the adaptive thresholding process to quantify mosaic attenuation and air trapping. Results: We analyzed 267 patients (65.5% female, median age 68). Most exhibited normal physiological patterns (44.0%). Patients with elevated residual volume (RV) by PFTs (28.1%) had significantly higher inspiratory CT mosaic attenuation (1629.6 vs. 1311.5 mL, P <0.01) and expiratory CT air trapping volumes (1413.7 vs. 886.2 mL, P <0.01). Correlation analyses demonstrated strong relationships between CT-derived mosaic attenuation and air trapping measures and RV. The correlation with PFT parameters was even stronger in subgroup analyses in patients with obstructive PFT patterns. These models had good predictive ability for an abnormal RV (AUC of 0.92, sensitivity of 72.4%, and specificity of 92.0%) and clinical utility based on good correlation with the mMRC dyspnea score ( r =0.71; 95% CI: 0.65-0.77). Conclusions: This automated adaptive thresholding on inspiratory and expiratory chest CT scans showed a high correlation of lung volume and air trapping parameters with PFTs, revealing that measures of lung function have a complex interplay with air trapping.

Background We automated subarachnoid hemorrhage volume (SAHV) calculation with artificial intelligence (SAHVAI) and created 3‐dimensional volumetric images (SAHVAI‐3D) using noncontrast head computed tomography data for patients with aneurysmal SAH. We also defined 4‐dimensional SAHVAI (SAHVAI‐4D), representing SAHV over time. We aimed to compare automated SAHVAI values and computational times to manual SAHV measurement methods, explore the potential of imaging biomarkers to identify at‐risk brain regions for delayed cerebral ischemia and explore potential insights in future neurotherapeutic interventions for patient recovery after SAH. Methods A training set of 10 consecutive patients with aneurysmal SAH was used to manually compute SAHV, SAHVAI‐3D, and SAHVAI‐4D, including 92 noncontrast computed tomography scans (182 slices each). The SAHVAI deep learning algorithm generated automated SAHV values in cubic centimeters. A 3‐dimensional SAH brain map was created for each patient for the SAHVAI and manual evaluations. Blood volumetric outputs were analyzed and compared to neurologic outcomes at discharge, including delayed cerebral ischemia events, symptomatic vasospasm, and areas with the thickest SAH blood concentration. Results SAHVAI quantified SAHV in a mean of 6.7 seconds per scan, significantly faster than the manual method, which took >60 minutes per scan (Fisher exact test, P <0.001). SAHVAI demonstrated an accuracy of 99.8%, Dice score of 0.701, false‐positive rate of 0.0005, and negative predictive value of 0.999. The mean absolute error between SAHVAI and manual methods was 5.67 mL. SAHVAI‐3D brain map and total SAHV at admission were inversely associated with Glasgow Coma Scale (R 2 = 0.23, P = 0.017) and directly associated with length of hospital stay (R 2 = 0.175, P = 0.004), especially in regions with dense blood concentration. Conclusion SAHVAI‐3D and SAHVAI‐4D brain mapping techniques represent innovative imaging biomarkers for SAH. These advancements enable rapid evaluation and targeted interventions, potentially improving patient care in SAH management.

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; −2000, 2000) in Bland–Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.