IntroductionBedside thoracic ultrasound (US) can rapidly diagnose pneumothorax (PTX) with improved accuracy over the physical examination and without the need for chest radiography (CXR); however, US is highly operator dependent. A computerized diagnostic assistant was developed by the United States Army Institute of Surgical Research to detect PTX on standard thoracic US images. This computer algorithm is designed to automatically detect sonographic signs of PTX by systematically analyzing B-mode US video clips for pleural sliding and M-mode still images for the seashore sign. This was a pilot study to estimate the diagnostic accuracy of the PTX detection computer algorithm when compared to an expert panel of US trained physicians.MethodsThis was a retrospective study using archived thoracic US obtained on adult patients presenting to the emergency department (ED) between 5/23/2011 and 8/6/2014. Emergency medicine residents, fellows, attending physicians, physician assistants, and medical students performed the US examinations and stored the images in the picture archive and communications system (PACS). The PACS was queried for all ED bedside US examinations with reported positive PTX during the study period along with a random sample of negatives. The computer algorithm then interpreted the images, and we compared the results to an independent, blinded expert panel of three physicians, each with experience reviewing over 10,000 US examinations.ResultsQuery of the PACS system revealed 146 bedside thoracic US examinations for analysis. Thirteen examinations were indeterminate and were excluded. There were 79 true negatives, 33 true positives, 9 false negatives, and 12 false positives. The test characteristics of the algorithm when compared to the expert panel were sensitivity 79% (95 % CI [63–89]) and specificity 87% (95% CI [77–93]). For the 20 images scored as highest quality by the expert panel, the algorithm demonstrated 100% sensitivity (95% CI [56–100]) and 92% specificity (95% CI [62–100]).ConclusionThis novel computer algorithm has potential to aid clinicians with the identification of the sonographic signs of PTX in the absence of expert physician sonographers. Further refinement and training of the algorithm is still needed, along with prospective validation, before it can be utilized in clinical practice.
Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical interpretation of a patient medical image is highly operator dependent. Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing certain pathologies. We have developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architectures, applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.
The observation of peaking in power spectra of K current noise in squid axon (Fishman, H.M, Moore, L.E., Poussart, D.J.M. 1975, J. Membrane Biol. 24:305) led to the calculation of low frequency K conduction feature in the impedance (admittance) which was confirmed (Fishman, H.M., Poussart, D.J.M., Moore, L.E. & Siebenga, E., 1977, J. Membrane Biol. 32:255). This paper analyzes two physical phenomena, one within and the other outside of the excitable membrane, that might account for the low frequency impedance (admittance) feature. The accumulation of potassium ions in a space outside the axon in conjunction with diffusion through the Schwann cell layer produces a low-frequency mode that is similar in some respects to that observed experimentally. Alternatively, a hypothetical inactivation process, with a voltage-dependent time constant, associated with conduction in potassium channels gives a better account of the data. Either or both of these phenomena could be involved in producing the low-frequency impedance behavior in the squid axon.
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