Digital imaging provides an effective means to electronically acquire, archive, distribute, and view medical images. Medical imaging display stations are an integral part of these operations. Therefore, it is vitally important to assure that electronic display devices do not compromise image quality and ultimately patient care. The AAPM Task Group 18 (TG18) recently published guidelines and acceptance criteria for acceptance testing and quality control of medical display devices. This paper is an executive summary of the TG18 report. TG18 guidelines include visual, quantitative, and advanced testing methodologies for primary and secondary class display devices. The characteristics, tested in conjunction with specially designed test patterns (i.e., TG18 patterns), include reflection, geometric distortion, luminance, the spatial and angular dependencies of luminance, resolution, noise, glare, chromaticity, and display artifacts. Geometric distortions are evaluated by linear measurements of the TG18-QC test pattern, which should render distortion coefficients less than 2%/5% for primary/secondary displays, respectively. Reflection measurements include specular and diffuse reflection coefficients from which the maximum allowable ambient lighting is determined such that contrast degradation due to display reflection remains below a 20% limit and the level of ambient luminance (Lamb) does not unduly compromise luminance ratio (LR) and contrast at low luminance levels. Luminance evaluation relies on visual assessment of low contrast features in the TG18-CT and TG18-MP test patterns, or quantitative measurements at 18 distinct luminance levels of the TG18-LN test patterns. The major acceptable criteria for primary/ secondary displays are maximum luminance of greater than 170/100 cd/m2, LR of greater than 250/100, and contrast conformance to that of the grayscale standard display function (GSDF) of better than 10%/20%, respectively. The angular response is tested to ascertain the viewing cone within which contrast conformance to the GSDF is better than 30%/60% and LR is greater than 175/70 for primary/secondary displays, or alternatively, within which the on-axis contrast thresholds of the TG18-CT test pattern remain discernible. The evaluation of luminance spatial uniformity at two distinct luminance levels across the display faceplate using TG18-UNL test patterns should yield nonuniformity coefficients smaller than 30%. The resolution evaluation includes the visual scoring of the CX test target in the TG18-QC or TG18-CX test patterns, which should yield scores greater than 4/6 for primary/secondary displays. Noise evaluation includes visual evaluation of the contrast threshold in the TG18-AFC test pattern, which should yield a minimum of 3/2 targets visible for primary/secondary displays. The guidelines also include methodologies for more quantitative resolution and noise measurements based on MTF and NPS analyses. The display glare test, based on the visibility of the low-contrast targets of the TG18-GV test pattern or the measurement of the glare ratio (GR), is expected to yield scores greater than 3/1 and GRs greater than 400/150 for primary/secondary displays. Chromaticity, measured across a display faceplate or between two display devices, is expected to render a u',v' color separation of less than 0.01 for primary displays. The report offers further descriptions of prior standardization efforts, current display technologies, testing prerequisites, streamlined procedures and timelines, and TG18 test patterns.

Assessment of display performance for medical imaging systems: executive summary of AAPM TG18 report.

Diagnosis and Initial Management of Nonmalignant Diseases Related to Asbestos

The aim of this communication is to provide some evidence linking the overweight/obesity and their impacts on different dimensions of health. We reviewed the related studies published from 1990 up till now through PubMed Central/Medline, which provide evidence linking obesity with health related issues. It is a risk factor for metabolic disorders and leads to serious health consequences for individuals and burden for the health care system as a whole. Literature search showed that it is related to at least 18 co-morbidities which are attributable to overweight and obesity. Moreover obese individuals more often suffer from significant joint pains, disorders and it also has social as well as psychological impairments. It is high time that countries facing the problems of obesity initiate some intervention measures to monitor and control this growing epidemic.

/pdf/health-impacts-of-obesity-1j2cq0ed82.pdf

Health impacts of Obesity.

Objectives: Only preliminary reports have evaluated the impact of telemedicine in trauma care. This study will analyze outcomes before (pre-TM) and after (post-TM) implementation of telemedicine in the management of rural trauma patients initially treated at local community hospitals (LCH) before trauma center (TC) transfer. Methods: Seven rural hospital emergency departments in Mississippi were equipped with dual video cameras with remote control capability. All trauma patients initially treated at these LCH with TC consultation were reviewed. Data included patient demographics, Injury Severity Score, institutional volume of patients, mode of transportation, length of stay in LCH, transfer time (TT), mortality, and hospital cost. Patients were grouped in the pre-TM and post-TM periods. Statistical testing was with two-sample Student's t test or Χ 2 analysis as appropriate. Results: During 5 years, 814 traumatically injured patients (pre-TM, n = 351; post-TM, n = 463) presented to the LCH. In the pre-TM period, 351 patients were transferred directly from the LCH for definitive management to the TC. In the post-TM period, 463 virtual consults were received, of which 51 patients were triaged to the TC. There were no differences in patient age, sex, or mode of transportation. When comparing post-TM with pre-TM era, patients had a higher Injury Severity Score (18 vs. 10, p < 0.001); less incidence of blunt trauma 35 (68%) versus 290 (82%),p < 0.05; a decrease in length of stay at LCH 1.5 hours versus 47 hours, p < 0.001; as well as TT LCH to TC 1.7 hours versus 13 hours, p < 0.001. After arrival to TC during the post-TM era patients received more units of packed red bed cell 13 units versus 5 units, p < 0.001 but without difference in mortality 4 (7.8%) versus 17 (4.8%), when compared with pre-TM era. Of statistical significance there was a dramatic decrease in hospital cost when comparing post-TM and pre-TM eras ($1,126,683 vs. $7,632,624, p < 0.001). Conclusion: Telemedicine significantly improved rural LCH evaluation and management of trauma patients. More severely injured trauma patients were identified and more rapidly transferred to the TC. Total TC hospital costs were significantly decreased without significant changes in TC mortality. Introduction of telemedicine consultation to rural LCH emergency departments expanded LCH trauma capabilities and conserved TC resources, which were directed to more severely injured patients.

Impact of telemedicine upon rural trauma care.

BackgroundThe performance of a deep learning (DL) algorithm should be validated in actual clinical situations, before its clinical implementation.PurposeTo evaluate the performance of a DL algorithm for identifying chest radiographs with clinically relevant abnormalities in the emergency department (ED) setting.Materials and MethodsThis single-center retrospective study included consecutive patients who visited the ED and underwent initial chest radiography between January 1 and March 31, 2017. Chest radiographs were analyzed with a commercially available DL algorithm. The performance of the algorithm was evaluated by determining the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity at predefined operating cutoffs (high-sensitivity and high-specificity cutoffs). The sensitivities and specificities of the algorithm were compared with those of the on-call radiology residents who interpreted the chest radiographs in the actual practice by using McNemar tests. If there were discordant findings between the algorithm and resident, the residents reinterpreted the chest radiographs by using the algorithm's output.ResultsA total of 1135 patients (mean age, 53 years ± 18; 582 men) were evaluated. In the identification of abnormal chest radiographs, the algorithm showed an AUC of 0.95 (95% confidence interval [CI]: 0.93, 0.96), a sensitivity of 88.7% (227 of 256 radiographs; 95% CI: 84.1%, 92.3%), and a specificity of 69.6% (612 of 879 radiographs; 95% CI: 66.5%, 72.7%) at the high-sensitivity cutoff and a sensitivity of 81.6% (209 of 256 radiographs; 95% CI: 76.3%, 86.2%) and specificity of 90.3% (794 of 879 radiographs; 95% CI: 88.2%, 92.2%) at the high-specificity cutoff. Radiology residents showed lower sensitivity (65.6% [168 of 256 radiographs; 95% CI: 59.5%, 71.4%], P < .001) and higher specificity (98.1% [862 of 879 radiographs; 95% CI: 96.9%, 98.9%], P < .001) compared with the algorithm. After reinterpretation of chest radiographs with use of the algorithm's outputs, the sensitivity of the residents improved (73.4% [188 of 256 radiographs; 95% CI: 68.0%, 78.8%], P = .003), whereas specificity was reduced (94.3% [829 of 879 radiographs; 95% CI: 92.8%, 95.8%], P < .001).ConclusionA deep learning algorithm used with emergency department chest radiographs showed diagnostic performance for identifying clinically relevant abnormalities and helped improve the sensitivity of radiology residents' evaluation.Published under a CC BY 4.0 license.Online supplemental material is available for this article.See also the editorial by Munera and Infante in this issue.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2019191225

Deep Learning for Chest Radiograph Diagnosis in the Emergency Department.

The purpose of this study was to evaluate whether radiologists perform equally well with plain radiographs or digitized images displayed on a video monitor in interpretation of difficult orthopedic trauma cases. Interpretations with film and those made from a teleradiology system with spatial resolution of 2.35 line pairs per millimeter were compared in 120 difficult cases, 60 with the selected abnormality (ie, fracture or dislocation) and 60 that were control cases. Seven senior radiology residents and one radiology fellow each interpreted 60 randomly ordered cases with the teleradiology system (1,280 x 1,024-pixel monitors) and 60 cases with the original radiographs. The overall accuracy of the readers was 80.6% for film interpretations and 59.6% for teleradiology screen readings (P < .001). Sensitivity was 78.5% for film and 48.8% for on-screen images (P < .001), and specificity was 83.2% for film and 72.3% for on-screen images (P < .025). Receiver operating characteristic analysis showed rejection of ...

Subtle orthopedic fractures: teleradiology workstation versus film interpretation.

Forty pneumonia and 40 fracture cases were matched by patient age and gender with normal cases. Pediatric cases constituted 25% of this sample. All 160 examinations were laser-digitized at a spot size of 210 microns, a contrast resolution of 12 bits, and a spatial resolution of 2.35 line pairs per millimeter. The digitized images were transmitted 5 miles over a dedicated telephone line at a T1 rate from Francis Scott Key Hospital to Johns Hopkins Hospital (both in Baltimore, Md). At Johns Hopkins Hospital, eight radiologists interpreted the original analog radiograph and corresponding soft-copy display (1,280 x 1,024 pixels). Findings, confidence ratings, and image quality were reported for each reading. A receiver operating characteristic (ROC) analysis was conducted to compare observer performance under the analog and digital reading conditions. The overall sensitivity of the analog method was 89% versus 78% for the digital method (P < .001), while the specificity values were 96% and 92%, respectively (...

Receiver operating characteristic analysis of fracture and pneumonia detection: comparison of laser-digitized workstation images and conventional analog radiographs.

Comparison of "B" readers' interpretations of chest radiographs for asbestos related changes.

Radiologists can comprehensively report diagnostic radiographs by computer with a speed approaching that of dictation. This is the main mode of radiographic reporting used at the Johns Hopkins Hospital. Support functions include information storage, retrieval, statistics, and billing. Costs are comparable to stenography. The system can be run from a large time-sharing computer or dedicated minicomputer. A commercial standalone version will soon be available.

The Johns Hopkins radiology reporting system.

A laboratory evaluation of a microcomputer-based teleradiology system is described. The system used state-of-the-art hardware, and provided for contrast and brightness control by the consulting radiologists. The transmitted, digitized images were compared with conventional radiographs. The radiologists' scores for findings, impressions, and confidence levels were significantly lower for radiographic images viewed on the teleradiology system. However, the quality of the images provided by the teleradiology system was high enough to warrant further study, especially since microcomputer-based teleradiology offers the advantage of providing access to radiology services for rural areas and small hospitals.

Teleradiology: an evaluation of a microcomputer-based system.

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