Pre-operative echocardiogram in hip fracture patients with cardiac murmur- an audit

<p>Abstract</p> <p>Background</p> <p>All hip fracture patients with a cardiac murmur have an echocardiogram as a part of their preoperative work-up in our unit. We performed a retrospective audit to assess the impact of obtaining a pre-operative echocardiogram on the management of hip fracture patients.</p> <p>Methods</p> <p>All hip fracture patients (N = 349) between 01/06/08 and 01/06/09 were included in the study. 29 patients had pre-operative echocardiogram (echo group). A computer generated randomised sample of 40 patients was generated from N, 'non-echo' group. Data was obtained from medical records and the Hospital Information Support System (HISS). The groups were compared using Student's t test. Approval was obtained locally from the clinical governance department for this project.</p> <p>Results</p> <p>Age and gender distribution were similar in both groups. Indication for echo was an acute cardiac abnormality in 4 cases. 25 patients had echo for no new cardiac problem (indication being cardiac murmur in 23 patients and extensive cardiac history in 2 cases). Cardiology opinion was sought in 5 cases. No patient required cardiac surgery or balloon angioplasty preoperatively. Patients having pre-operative echo had significant delay to surgery (average 2.7 days, range 0-6 days) compared to 'non-echo' group (average 1.1 days, range 0-3 days), (p < 0.001). There was no significant difference in length of stay (p = 0.14) and mortality at 30 days (p = 0.41) between the groups.</p> <p>Conclusion</p> <p>We have developed departmental guidelines for expediting echo requests in hip fracture patients with cardiac murmur. A liaison has been established with our cardiology department to prioritise such patients on the Echocardiography waiting list, to prevent unnecessary avoidable delay. Careful patient selection for pre-operative echocardiography is important to avoid unnecessary delay to surgery.</p

Deep learning for vehicle safety

[Extract] Over the last 30 years, technology in cars has evolved drastically with the introduction of software components to improve the driving experience. By adding traction controls, navigation systems, security systems, airbag deployment systems, and engine management systems, to name just a few, cars have become more reliable to operate, fuel-efficient, and safer for the passengers and drivers. Continuous investment is being made to further develop the technology present in the vehicle to improve the driving experience and safety. This has led to further advancements, such as infotainment systems, automatic climate controls, navigation systems, lane change monitoring, cruise control and lane detection

Mapping Remote Roads Using Artificial Intelligence and Satellite Imagery

Road building has long been under-mapped globally, arguably more than any other human activity threatening environmental integrity. Millions of kilometers of unmapped roads have challenged environmental governance and conservation in remote frontiers. Prior attempts to map roads at large scales have proven inefficient, incomplete, and unamenable to continuous road monitoring. Recent developments in automated road detection using artificial intelligence have been promising but have neglected the relatively irregular, sparse, rustic roadways characteristic of remote semi-natural areas. In response, we tested the accuracy of automated approaches to large-scale road mapping across remote rural and semi-forested areas of equatorial Asia-Pacific. Three machine learning models based on convolutional neural networks (UNet and two ResNet variants) were trained on road data derived from visual interpretations of freely available high-resolution satellite imagery. The models mapped roads with appreciable accuracies, with F1 scores of 72–81% and intersection over union scores of 43–58%. These results, as well as the purposeful simplicity and availability of our input data, support the possibility of concerted program of exhaustive, automated road mapping and monitoring across large, remote, tropical areas threatened by human encroachment

A novel add-on collimator for preclinical radiotherapy applications using a standard cell irradiator: design, construction, and validation

© 2020 American Association of Physicists in Medicine Purpose: Preclinical radiotherapy applications require dedicated irradiation systems which are expensive and not widely available. In this work, a clinical dual source 137Cs cell irradiator was adapted to deliver 1-cm diameter preclinical treatment beams using a lead and stainless steel custom-made collimator to treat one or two mice at a time. Methods: The dosimetric characteristics of all the different components of the system (including collimator, phantoms, and radiation sources) have been simulated with EGSnrc Monte Carlo methods. The collimator was constructed based on these simulations and the calculated results were verified against dosimetric measurements with MOSKin detectors, GAFchromic films, and dosimetric gels. Results: The comparisons showed an agreement, in terms of full width half maximum values, between the simulated and the measured dose cross profiles at the midline within 4% for both gel dosimetry and GAFchromic films. Out of beam dose, measured in air at the collimator midplane with MOSFET detectors was between 6% and 10% of the beam axis dose. The dimensions of the beam are constant along the vertical axis of the collimator and also the simulated and measured Percentage Depth Dose (PDD) curves show an agreement within 1%. Conclusions: The collimator design developed in this work allows the creation of a beam with the necessary characteristics for ablative radiotherapy treatments on small animals using a standard clinical cell irradiator. This collimator design will make advanced preclinical studies with ablative beams possible for all those institutions which do not have collimated preclinical irradiators available