Skull X-Ray Assessment of Head Injuries: A Decision Analytic Approach

1984 ◽  
Vol 23 (03) ◽  
pp. 135-138 ◽  
Author(s):  
J. I. Balla ◽  
A. S. Elstein
Keyword(s):  
Head Injuries ◽  
Ct Scanning ◽  
Cost Effective ◽  
X Rays ◽  
Intracranial Haematoma ◽  
X Ray ◽  
Neurological Signs ◽  
Cost Effective Method ◽  
Head Injured ◽  
Injured Patients

SummaryWe demonstrate the value of skull x-rays in the assessment of head injured patients. With the use of the simple decision analytic technique of 2 x 2 tables and literature derived figures, it is shown that in patients with a normal neurological examination following head injuries, skull x-rays are a cost-effective method of detecting preventable complications. The positive predictive value (PVP) of skull x-rays in these patients is. 03, in contrast to .001 of the group as a whole. Such cases should then have a CT scan to detect early cases of intracranial haematoma (ICH). The presence of abnormal neurological signs has a high PVP of .2 for complications; in these patients, skull x-rays do not improve the detection of ICH and CT scanning alone should be carried out.

scholarly journals Finding fossils in Malapa breccia – medical CT scanning or micro-CT scanning?

2017 ◽  
Vol 113 (11/12) ◽  
Author(s):  
Jacqueline S. Smilg
Keyword(s):  
Computed Tomography ◽  
Lower Energy ◽  
Ct Scanning ◽  
Cost Effective ◽  
High Energy ◽  
X Rays ◽  
Micro Ct ◽  
X Ray ◽  
Mechanical Preparation ◽  
X Ray Computed

Computed tomography (CT) imaging of fossils has revolutionised the field of palaeontology, allowing researchers to gain a better understanding of fossil anatomy, preservation and conservation. Micro focus X-ray computed tomography (μXCT) has been far more extensively used for these purposes than medical CT (XCT) – mostly because of the exquisite detail that the μXCT scanning modality, using slices of micron thicknesses, can produce. High energy X-rays can potentially penetrate breccia more effectively than lower energy beams. This study demonstrates that lower energy beams produce superior images for prioritising breccia for preparation. Additionally, XCT scanners are numerous, accessible, fast and relatively cost-effective when compared to μXCT scanners – the latter are not freely available, scanning times are much longer and there are significant limitations on the size and weight of scannable objects. Breccia blocks from Malapa were scanned at high and lower energy and images were analysed for image quality, artifact and certainty of diagnosis. Results show that lower energy images are deemed superior to higher energy images for this particular application. This finding, taken together with the limitations associated with the use of μXCT for the imaging of the large breccia from Malapa, shows that XCT is the better modality for this specific application. The ability to choose fossil-bearing breccia, ahead of manual mechanical preparation by laboratory technicians, would allow for the optimal use of limited resources, manual preparatory skills as well as the curtailment of costs.

scholarly journals Standardized Measurements of Radiographic Films of the Frontal Sinuses: An Aid to Identifying Unknown Persons

2000 ◽  
Vol 79 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Fernando De Andrade Quintanilha Ribeiro
Keyword(s):  
Frontal Sinus ◽  
Cost Effective ◽  
X Rays ◽  
X Ray ◽  
Cost Effective Method ◽  
Effective Manner ◽  
Reproducible Method ◽  
Frontal Sinuses ◽  
The Asymmetry

Many methods have been used to identify unknown persons. The most common is the study of fingerprints, which is one of the few methods in which data can be stored and retrieved in a precise and cost-effective manner. The asymmetry of the frontal sinuses has stimulated several attempts to identify persons by analyzing measurements of the sinuses obtained from plain x-ray films. However, an objective and reproducible method has not been achieved. The objective of this paper is to report on a method of making standardized measurements of the frontal sinuses on radiographs and storing the resultant information in a computer databank. To test the system, the author made new measurements on 100 x-rays and entered the results into a computer databank that contained information on earlier measurements of the same 100 x-rays and on 400 others. With the aid of the databank, the author was able to rapidly and accurately match each new measurement with the earlier measurement. The author concludes that such a system is a simple and cost-effective method of establishing the identification of unknown persons for whom frontal sinus x-rays exist.

A Study of the Effect of Extended Hounsfield Unit Range and Voxel Size on Defect Detection in Friction Stir Welds

2020 ◽  
Author(s):  
Ahmad M. R. Baydoun ◽  
Ramsey F. Hamade
Keyword(s):  
Gamma Rays ◽  
Dissimilar Materials ◽  
Hounsfield Unit ◽  
Ct Scanning ◽  
Ultrasonic Waves ◽  
X Rays ◽  
Internal Defects ◽  
Voxel Size ◽  
X Ray ◽  
Friction Stir

Abstract Friction stir welding (FSW) is a novel welding method that is garnering attention, in part, due to its ability to join dissimilar materials. One of the challenges in producing dissimilar friction welded joints is ensuring the welds are defect-free. Nondestructive testing (NDT) methods such as ultrasonic waves, gamma rays, X-rays, and X-ray CT, are gaining popularity as a method to detect internal defects in FSW joints. In this study, dissimilar AA1050-AA6061-T6 FSW lap welds are Manufactured and then examined using an NDT X-ray CT technique. The effects of two critical X-ray CT scanning parameters (voxel size and Hounsfield unit (HU)) on the detection of internal defects are investigated. The samples are scanned via X-ray CT at two different voxel sizes (2.457 E−02 and 1.420 E−03 mm3) and two HU ranges (12-bit and 16-bit depth). The generated Digital Imaging and Communications in Medicine (DICOM) images are segmented based on a proper HU threshold found via the Otsu thresholding method. The findings show that Small voxel size (higher resolution) improves the ability of detecting internal defects and improves the effectiveness of the thresholding process. Higher HU range results in a wider separation between detected material peaks, thus enhancing the effectiveness of the thresholding process as well.

scholarly journals 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S424-S425
Author(s):  
Dan Ding ◽  
Anna Stachel ◽  
Eduardo Iturrate ◽  
Michael Phillips
Keyword(s):  
Language Processing ◽  
Cost Effective ◽  
The United States ◽  
Surveillance Data ◽  
X Rays ◽  
Healthcare Facilities ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity ◽  
Time Critical

Abstract Background Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations. Results The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities. Disclosures All authors: No reported disclosures.

Prediction tree for severely head-injured patients

1991 ◽  
Vol 75 (2) ◽  
pp. 251-255 ◽  
Author(s):  
Sung C. Choi ◽  
Jan P. Muizelaar ◽  
Thomas Y. Barnes ◽  
Anthony Marmarou ◽  
Danny M. Brooks ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Predictive Accuracy ◽  
Head Injuries ◽  
Pupillary Response ◽  
Prognostic Indicators ◽  
Critical Threshold ◽  
Content Type ◽  
Intermediate Outcomes ◽  
Head Injured ◽  
Injured Patients

✓ Prediction tree techniques are employed in the analysis of data from 555 patients admitted to the Medical College of Virginia hospitals with severe head injuries. Twenty-three prognostic indicators are examined to predict the distribution of 12-month outcomes among the five Glasgow Outcome Scale categories. A tree diagram, illustrating the prognostic pattern, provides critical threshold levels that split the patients into subgroups with varying degrees of risk. It is a visually useful way to look at the prognosis of head-injured patients. In previous analyses addressing this prediction problem, the same set of prognostic factors (age, motor score, and pupillary response) was used for all patients. These approaches might be considered inflexible because more informative prediction may be achieved by somewhat different combinations of factors for different patients. Tree analysis reveals that the pattern of important prognostic factors differs among various patient subgroups, although the three previously mentioned factors are still of primary importance. For example, it is noted that information concerning intracerebral lesions is useful in predicting outcome for certain patients. The overall predictive accuracy of the tree technique for these data is 77.7%, which is somewhat higher than that obtained via standard prediction methods. The predictive accuracy is highest among patients who have a good recovery or die; it is lower for patients having intermediate outcomes.

scholarly journals Genome-wide screen of Saccharomyces cerevisiae null allele strains identifies genes involved in selenomethionine resistance

2008 ◽  
Vol 105 (46) ◽  
pp. 17682-17687 ◽  
Author(s):  
Jessica Bockhorn ◽  
Bharvi Balar ◽  
Dongming He ◽  
Eden Seitomer ◽  
Paul R. Copeland ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Null Allele ◽  
Cost Effective ◽  
Anomalous Dispersion ◽  
Gene Encoding ◽  
X Ray ◽  
X Ray Crystallography ◽  
Genome Wide ◽  
Cost Effective Method ◽  
Strain Collection

Selenomethionine (SeMet) is a potentially toxic amino acid, and yet it is a valuable tool in the preparation of labeled proteins for multiwavelength anomalous dispersion or single-wavelength anomalous dispersion phasing in X-ray crystallography. The mechanism by which high levels of SeMet exhibits its toxic effects in eukaryotic cells is not fully understood. Attempts to use Saccharomyces cerevisiae for the preparation of fully substituted SeMet proteins for X-ray crystallography have been limited. A screen of the viable S. cerevisiae haploid null allele strain collection for resistance to SeMet was performed. Deletion of the CYS3 gene encoding cystathionine gamma-lyase resulted in the highest resistance to SeMet. In addition, deletion of SSN2 resulted in both increased resistance to SeMet as well as reduced levels of Cys3p. A methionine auxotrophic strain lacking CYS3 was able to grow in media with SeMet as the only source of Met, achieving essentially 100% occupancy in total proteins. The CYS3 deletion strain provides advantages for an easy and cost-effective method to prepare SeMet-substituted protein in yeast and perhaps other eukaryotic systems.

Use of Intraoperative Ultrasonography to Improve the Diagnostic Accuracy of Exploratory Burr Holes in Patients with Traumatic Tentorial Herniation

Neurosurgery ◽  
1989 ◽  
Vol 24 (3) ◽  
pp. 345-347 ◽  
Author(s):  
Brian T. Andrews ◽  
Joshua B. Bederson ◽  
Lawrence H. Pitts
Keyword(s):  
Head Injury ◽  
Brain Stem ◽  
Ct Scanning ◽  
Intraoperative Ultrasonography ◽  
Burr Hole ◽  
Ultrasound Images ◽  
Frontal Pole ◽  
Subdural Hematomas ◽  
Head Injured ◽  
Injured Patients

Abstract Seventeen head-injured patients with signs of brain stem compression at admission underwent emergency bilateral burrhole exploration before computerized tomographic (CT) scanning. After exploration of the epidural and subdural spaces, real-time ultrasonography was performed intraoperatively to identify intraaxial hematomas. Epidural or subdural hematomas were identified surgically in 11 patients (65%) and immediately evacuated through a craniotomy; in 2 patients, bilateral subdural hematomas were removed. Ultrasonography showed no evidence of intracerebral mass lesions in 14 (82%) of the 17 patients, demonstrated extensive contusions of the temporal lobe in 2 patients (prompting partial lobectomy in both cases), and revealed a small intraparenchymal hematoma deep within the dominant hemisphere, which was not removed, in 1 patient. The sensitivity of ultrasound images for identifying intraparenchymal lesions was evaluated postoperatively by CT or autopsy. In 15 patients (88%), the results of ultrasonography were confirmed. In 2 (12%), CT scans showed small but significant lesions at the frontal pole missed by ultrasonography; one patient had a residual subdural hematoma, and the other a small intraparenchymal hemorrhage. These results confirm that patients with clinical evidence of brain stem compression soon after head injury often have extraaxial hematomas that can be readily identified by burr-hole exploration. Although intraparenchymal hematomas are rare immediately after head injury, they can ususally be identified by intraoperative ultrasonography. This simple technique can reduce the risk of missing intractranial hematomas during emergency burr-hole exploration and improve intraoperative decision making in this population of severely head-injured patients.

Development of a Bayesian Network for the Prognosis of Head Injuries using Graphical Model Selection Techniques

1999 ◽  
Vol 38 (01) ◽  
pp. 37-42 ◽  
Author(s):  
G. C. Sakellaropoulos ◽  
G. C. Nikiforidis
Keyword(s):  
Model Selection ◽  
Bayesian Network ◽  
Analytical Approach ◽  
Graphical Model ◽  
Head Injuries ◽  
Sources Of Information ◽  
Laboratory Findings ◽  
Learning Techniques ◽  
Head Injured ◽  
Injured Patients

Abstract:The assessment of a head-injured patient’s prognosis is a task that involves the evaluation of diverse sources of information. In this study we propose an analytical approach, using a Bayesian Network (BN), of combining the available evidence. The BN’s structure and parameters are derived by learning techniques applied to a database (600 records) of seven clinical and laboratory findings. The BN produces quantitative estimations of the prognosis after 24 hours for head-injured patients in the outpatients department. Alternative models are compared and their performance is tested against the success rate of an expert neurosurgeon.

scholarly journals Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

scholarly journals Mechanical and bioactivity assessment of wollastonite/PVA composite synthesized from bentonite clay

Cerâmica ◽  
2019 ◽  
Vol 65 (374) ◽  
pp. 246-251
Author(s):  
L. A. Adams ◽  
E. R. Essien ◽  
E. E. Kaufmann
Keyword(s):  
Compressive Strength ◽  
Bone Regeneration ◽  
Biomedical Applications ◽  
Bentonite Clay ◽  
Cost Effective ◽  
Biological Properties ◽  
X Ray ◽  
Silica Source ◽  
Cost Effective Method ◽  
Matrix Configuration

Abstract Glass/polymer composites can mimic the natural structure of bone by possessing a fiber-matrix configuration which provides appropriate physical and biological properties. Wollastonite ceramics are known for their promising bioactivity and biocompatibility when applied in bone regeneration. Polyvinyl alcohol (PVA) has various attractive properties including biocompatibility and degradability which may be exploited as a polymer matrix in composites for biomedical applications. Therefore, a cost-effective method of preparing wollastonite/PVA composites is desirable by starting from bentonite clay as a silica source for the glass, instead of traditional alkoxysilanes. The composite prepared was characterized by mechanical testing, scanning electron microscopy, X-ray diffractometry and Fourier-transform infrared spectroscopy to evaluate its compressive strength, morphology, phase composition and bioactivity, respectively. Results obtained revealed for the composite a compressive strength of 0.3 MPa, the ability to induce apatite on its surface when immersed in a simulated body fluid for 7 days and desirable controlled degradation. Hence, this method can be up-scaled for preparation of wollastonite/PVA composite commercially for possible use in bone regeneration.

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