An increased RDI appears to be an important variable for predicting the presence of complete obstruction and CCC during OSA. Scanning during apneic episodes, using low-dose volumetric CT combined with portable PSG provided better anatomic and pathologic findings of OSA than did scans performed during the awake state.

DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assay still has limitations in the time consumed for manual scoring and possible variability between scorers. This study proposed a novel automated foci scoring method using a deep convolutional neural network based on a You-Only-Look-Once (YOLO) algorithm to quantify γ-H2AX foci in peripheral blood samples. FociRad, a two-stage deep learning approach, consisted of mononuclear cell (MNC) and γ-H2AX foci detections. Whole blood samples were irradiated with X-rays from a 6 MV linear accelerator at 1, 2, 4 or 6 Gy. Images were captured using confocal microscopy. Then, dose–response calibration curves were established and implemented with unseen dataset. The results of the FociRad model were comparable with manual scoring. MNC detection yielded 96.6% accuracy, 96.7% sensitivity and 96.5% specificity. γ-H2AX foci detection showed very good F1 scores (> 0.9). Implementation of calibration curve in the range of 0–4 Gy gave mean absolute difference of estimated doses less than 1 Gy compared to actual doses. In addition, the evaluation times of FociRad were very short (< 0.5 min per 100 images), while the time for manual scoring increased with the number of foci. In conclusion, FociRad was the first automated foci scoring method to use a YOLO algorithm with high detection performance and fast evaluation time, which opens the door for large-scale applications in radiation triage.

Background There are no studies comparing the morphologic changes of lumbar spines between supine axial-loaded and 90° standing magnetic resonance imaging (MRI) examinations of patients with spinal stenosis. Purpose To determine whether axial-loaded MRI using a compression device demonstrated similar morphology of intervertebral disc, dural sac, and spinal curvature as those detected by 90° standing MRI in individuals with suspected spinal stenosis. Material and Methods A total of 54 individuals suspected of having spinal stenosis underwent both axial-loaded and standing MRI studies. The outcome measures included seven radiologic parameters of the lumbar spine: measures of the intervertebral disc (i.e. cross-sectional area [DA], disc height [DH], and anteroposterior distance [DAP]), dural sac (cross-sectional area [DCSA]), spinal curvature (i.e. lumbar lordosis [LL] and L1-L3-L5 angle [LA]), and total lumbar spine height (LH). Results For agreement between the two methods, intraclass correlation coefficient (ICC) ≥ 0.8 was found for all seven radiologic parameters. Supine axial-loaded MRI underestimated LL but remained correlated (ICC = 0.83) with standing MRI. Minor differences between the two methods (≤5.0%) were observed in DA, DCSA, DAP, LA, and LH, while a major difference was observed in LL (8.1%). Conclusion Using a compression device with the conventional supine MRI to simulate weight-bearing on the lumbar spine generated MRI morphology, which was strongly correlated with those from a standing MRI.

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CT scanning at the ends of inspiration and expiration helped identify patients with an RDI higher than 30 based on measurement of the MCA. Low-dose volumetric CT can be a useful tool to help the clinician rapidly identify patients with severe OSA and decide on the urgency to obtain a full-night polysomnographic study and to start treatment.