Cerebrovascular reactivity and cerebral autoregulation are two major mechanisms that regulate cerebral blood flow. Both mechanisms are typically assessed in either supine or seated postures, but the effects of body position and sex differences remain unclear. This study examined the effects of body posture (supine vs. seated vs. standing) on cerebrovascular reactivity during hyper and hypocapnia and on cerebral autoregulation during spontaneous and slow-paced breathing in healthy men and women using transcranial Doppler ultrasonography of the middle cerebral artery. Results indicated significantly improved cerebrovascular reactivity in the supine compared with seated and standing postures (supine = 3.45±0.67, seated = 2.72±0.53, standing = 2.91±0.62%/mmHg, P<0.0167). Similarly, cerebral autoregulatory measures showed significant improvement in the supine posture during slow-paced breathing. Transfer function measures of gain significantly decreased and phase significantly increased in the supine posture compared with seated and standing postures (gain: supine = 1.98±0.56, seated = 2.37±0.53, standing = 2.36±0.71%/mmHg; phase: supine = 59.3±21.7, seated = 39.8±12.5, standing = 36.5±9.7˚; all P<0.0167). In contrast, body posture had no effect on cerebral autoregulatory measures during spontaneous breathing. Men and women had similar cerebrovascular reactivity and similar cerebral autoregulation during both spontaneous and slow-paced breathing. These data highlight the importance of making comparisons within the same body position to ensure there is not a confounding effect of posture.

Objectives: Accurate diagnosis of cholesteatomas is crucial. However, cholesteatomas can easily be missed in routine otoscopic exams. Convolutional neural networks (CNNs) have performed well in medical image classification, so we evaluated their use for detecting cholesteatomas in otoscopic images.Study Design: Design and evaluation of artificial intelligence driven workflow for cholesteatoma diagnosis.Methods: Otoscopic images collected from the faculty practice of the senior author were deidentified and labeled by the senior author as cholesteatoma, abnormal noncholesteatoma, or normal. An image classification workflow was developed to automatically differentiate cholesteatomas from other possible tympanic membrane appearances. Eight pretrained CNNs were trained on our otoscopic images, then tested on a withheld subset of images to evaluate their final performance. CNN intermediate activations were also extracted to visualize important image features.Results: A total of 834 otoscopic images were collected, further categorized into 197 cholesteatoma, 457 abnormal non-cholesteatoma, and 180 normal. Final trained CNNs demonstrated strong performance, achieving accuracies of 83.8%-98.5% for differentiating cholesteatoma from normal, 75.6%-90.1% for differentiating cholesteatoma from abnormal non-cholesteatoma, and 87.0%-90.4% for differentiating cholesteatoma from non-cholesteatoma (abnormal non-cholesteatoma + normal). DenseNet201 (100% sensitivity, 97.1% specificity), NASNetLarge (100% sensitivity, 88.2% specificity), and MobileNetV2 (94.1% sensitivity, 100% specificity) were among the best performing CNNs in distinguishing cholesteatoma versus normal. Visualization of intermediate activations showed robust detection of relevant image features by the CNNs. Conclusion:While further refinement and more training images are needed to improve performance, artificial intelligence-driven analysis of otoscopic images shows great promise as a diagnostic tool for detecting cholesteatomas.