Recollections of My Research in Developing the Heart-Lung Machine at Jefferson Medical College

A personal memoir written by Dr. Bernard J. Miller about his introduction to and interest in medical research as well as his experiences working on the heart-lung machine. He focuses specifically on his working relationship with John H. Gibbon, Jr., the development of a viable oxygenator and ventilator, and early testing of the machine on animal

Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer

Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the simple convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets.Comment: 6 pages, 14 figure

Focal Spot, Spring 1979

https://digitalcommons.wustl.edu/focal_spot_archives/1023/thumbnail.jp

Extracorporeal membrane oxygenation simulation-based training: methods, drawbacks and a novel solution

Introduction: Patients under the error-prone and complication-burdened extracorporeal membrane oxygenation (ECMO) are looked after by a highly trained, multidisciplinary team. Simulation-based training (SBT) affords ECMO centers the opportunity to equip practitioners with the technical dexterity required to manage emergencies. The aim of this article is to review ECMO SBT activities and technology followed by a novel solution to current challenges. ECMO simulation: The commonly-used simulation approach is easy-to-build as it requires a functioning ECMO machine and an altered circuit. Complications are simulated through manual circuit manipulations. However, scenario diversity is limited and often lacks physiological and/or mechanical authenticity. It is also expensive to continuously operate due to the consumption of highly specialized equipment. Technological aid: Commercial extensions can be added to enable remote control and to automate circuit manipulation, but do not improve on the realism or cost-effectiveness. A modular ECMO simulator: To address those drawbacks, we are developing a standalone modular ECMO simulator that employs affordable technology for high-fidelity simulation.Peer reviewe

Nurses\u27 Alumnae Association Bulletin - Volume 18 Number 1

Alumnae Notes Central Dressing Room Committee Reports Digest of Alumnae Association Meetings Graduation Awards - 1952 Greetings from Miss Childs Greetings from the President Marriages Modern Trends in Orthopaedic Surgery Necrology New Arrivals Physical Advances at Jefferson Hospital - 1953 Staff Activities - 1952-1953 Student Activities The Artificial Heart Lung Machin

216 Jewish Hospital of St. Louis

https://digitalcommons.wustl.edu/bjc_216/1121/thumbnail.jp

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Barnes Hospital Bulletin

https://digitalcommons.wustl.edu/bjc_barnes_bulletin/1137/thumbnail.jp