Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

PURPOSE: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions. MATERIALS AND METHODS: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels. U-Net based network was trained followed by majority voting of the voxel wise predictions of the model to transform them into patient level stroke territory classes. Effects of bias field correction and registration to a common space were explored. RESULTS: Of the 218 patients included in this study, 141 (65%) were anterior stroke, and 77 were posterior stroke (35%) whereas 117 (53%) were male and 101 (47%) were female. The model built with original images reached 0.77 accuracy, while the model built with N4 bias corrected images reached 0.80 and the model built with images which were N4 bias corrected and then registered into a common space reached 0.83 accuracy values. CONCLUSION: Voxel wise dense prediction coupled with bias field correction to eliminate artificial signal increase and registration to a common space help models for better performance than using original images. Knowing the properties of target domain while designing deep learning models is important for the overall success of these models.

Artificial intelligence in diagnostic ultrasonography.

Artificial intelligence (AI) continues to change paradigms in the field of medicine with new applications that are applicable to daily life. The field of ultrasonography, which has been developing since the 1950s and continues to be one of the most powerful tools in the field of diagnosis, is also the subject of AI studies, despite its unique problems. It is predicted that many operations, such as appropriate diagnostic tool selection, use of the most relevant parameters, improvement of low-quality images, automatic lesion detection and diagnosis from the image, and classification of pathologies, will be performed using AI tools in the near future. Especially with the use of convolutional neural networks, successful results can be obtained for lesion detection, segmentation, and classification from images. In this review, relevant developments are summarized based on the literature, and examples of the tools used in the field are presented.

A snapshot of teleradiology practice in Turkey: the results of a survey among radiologists.

PURPOSE: This study featured a survey that offers a snapshot of various teleradiology practices in Turkey, a Group of Twenty country that has undertaken a major transformation of its health care system during the last two decades and is currently the world leader in terms of the combined number of per capita magnetic resonance imaging and computed tomography examinations performed (which represent the bulk of teleradiology services worldwide). METHODS: The study data was collected from 4736 Turkish Society of Radiology (TSR) members via an electronic platform in the web environment through a questionnaire consisting of 24 questions. The survey was conducted in a 3-month time window (March-May 2021). Statistical tools were used for the analysis of the quantitative data. RESULTS: Responses from 156 members of the TSR comprised the study data, revealing that teleradiology is used for various applications in Turkey. Almost half of the participants (49%) performed teleradiology only in the private sector. Half of the respondents (51%) stated that they reported images at home for multiple centers. Moreover, 38% of the participants had been reporting more than 50 examinations per day, and 74% of the respondents earned less than 0.50 Euro per examination they reported. The overall satisfaction with teleradiology among the teleradiologists was, on average, 4.7 out of 10 points. CONCLUSION: The results are both promising for the future (i.e., concerning the propensity for adopting new technology) and alarming for the current state of affairs (i.e., insufficient radiologist reimbursement and lack of licensing and accreditation of teleradiology service providers). Periodic surveys performed in countries with different health care systems concerning financial, technical, and medicolegal aspects might reveal an up-to-date landscape of teleradiology practices worldwide and help guide local and regional decision-makers.

Evaluation of abdominal computed tomography findings in patients with COVID-19: a multicenter study

PURPOSE: To evaluate the frequency of abdominal computed tomography (CT) findings in patients with coronavirus disease-2019 (COVID-19) and interrogate the relationship between abdominal CT findings and patient demographic features, clinical findings, and laboratory test results as well as the CT atherosclerosis score in the abdominal aorta. METHODS: This study was designed as a multicenter retrospective study. The abdominal CT findings of 1.181 patients with positive abdominal symptoms from 26 tertiary medical centers with a positive polymerase chain-reaction test for severe acute respiratory syndrome coronavirus 2 were reviewed. The frequency of ischemic and non-ischemic CT findings as well as the association between CT findings, clinical features, and abdominal aortic calcific atherosclerosis score (AA-CAS) were recorded. RESULTS: Ischemic and non-ischemic abdominal CT findings were detected in 240 (20.3%) and 328 (27.7%) patients, respectively. In 147 patients (12.4%), intra-abdominal malignancy was present. The most frequent ischemic abdominal CT findings were bowel wall thickening (n = 120; 10.2%) and perivascular infiltration (n = 40; 3.4%). As for non-ischemic findings, colitis (n = 91; 7.7%) and small bowel inflammation (n = 73; 6.2%) constituted the most frequent disease processes. The duration of hospital stay was found to be higher in patients with abdominal CT findings than in patients without any positive findings (13.8 ± 13 vs. 10.4 ± 12.8 days, P < 0.001). The frequency of abdominal CT findings was significantly higher in patients who did not survive the infection than in patients who were discharged after recovery (41.7% vs. 27.4%, P < 0.001). Increased AA-CAS was found to be associated with a higher risk of ischemic conditions in abdominal CT examinations. CONCLUSION: Abdominal symptoms in patients with COVID-19 are usually associated with positive CT findings. The presence of ischemic findings on CT correlates with poor COVID-19 outcomes. A high AA-CAS is associated with abdominal ischemic findings in patients with COVID-19.

TSR guidelines for the practice of teleradiology: 2021 update.

This update of Turkish Society of Radiology's (TSR) guidelines for the practice of teleradiology is intended to provide a reference framework for all parties involved in delivering imaging services away from the immediate vicinity of the patient. It includes relevant definitions and general principles, features organizational modes and qualifications of the practicing parties, lists technical issues, and addresses such management and legal aspects as archiving and documentation, security and privacy, reliability, responsibilities, quality inspection and improvement, reimbursement and accountability.

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance are hard to interpret. This makes comparative analysis a necessary tool towards interpretable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal semantic segmentation tasks has been rarely discussed. In order to expand the knowledge on these topics, the CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Abdominal organ segmentation from routine acquisitions plays an important role in several clinical applications, such as pre-surgical planning or morphological and volumetric follow-ups for various diseases. These applications require a certain level of performance on a diverse set of metrics such as maximum symmetric surface distance (MSSD) to determine surgical error-margin or overlap errors for tracking size and shape differences. Previous abdomen related challenges are mainly focused on tumor/lesion detection and/or classification with a single modality. Conversely, CHAOS provides both abdominal CT and MR data from healthy subjects for single and multiple abdominal organ segmentation. Five different but complementary tasks were designed to analyze the capabilities of participating approaches from multiple perspectives. The results were investigated thoroughly, compared with manual annotations and interactive methods. The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0.98 ± 0.00 / 0.95 ± 0.01), but the best MSSD performance remains limited (21.89 ± 13.94 / 20.85 ± 10.63 mm). The performances of participating models decrease dramatically for cross-modality tasks both for the liver (DICE: 0.88 ± 0.15 MSSD: 36.33 ± 21.97 mm). Despite contrary examples on different applications, multi-tasking DL models designed to segment all organs are observed to perform worse compared to organ-specific ones (performance drop around 5%). Nevertheless, some of the successful models show better performance with their multi-organ versions. We conclude that the exploration of those pros and cons in both single vs multi-organ and cross-modality segmentations is poised to have an impact on further research for developing effective algorithms that would support real-world clinical applications. Finally, having more than 1500 participants and receiving more than 550 submissions, another important contribution of this study is the analysis on shortcomings of challenge organizations such as the effects of multiple submissions and peeking phenomenon.

How to perform an excellent radiology board examination: a web-based checklist.

BACKGROUND: Board exams are now considered as means of quality procedures that aim to keep the professional knowledge and skills of the physicians at the highest level. In addition, for an assessment to be scientifically valid, it has to be done within defined standards. Although there are different sources in this field, there is a need for a resource that details the steps required for the examinations to be performed perfectly, brings descriptions of the reasons for the procedure and associates the steps with assessment standards. Experts with national and international experience both in radiology and medical education contributed to the preparation of this checklist. RESULTS: The guide includes 174 elements to consider before, after the exam order and examination. From the perspective of assessment standards, it has been observed that the steps to be considered before the exam have a greater impact on the validity and reliability of the exam. The standard in which the questions are most associated was validity with 117 (67.24%) questions. CONCLUSIONS: We think that our guide, which will be accessible in the web environment, will be useful to the teams with a development goal or just start the exam, the candidates who will take the exam and the examiners.

Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis.

Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis BACKGROUND/AIMS To compare radiological scoring systems, clinical scores, serum C-reactive protein (CRP) levels and the neutrophil-lymphocyte ratio (NLR) for predicting the severity and mortality of acute pancreatitis (AP). MATERIALS AND METHODS Demographic, clinical, and radiographic data from 80 patients with AP were retrospectively evaluated. The harmless acute pancreatitis score (HAPS), systemic inflammatory response syndrome (SIRS), bedside index for severity in acute pancreatitis (BISAP), Ranson score, Balthazar score, modified computed tomography severity index (CTSI), extrapancreatic inflammation on computed tomography (EPIC) score and renal rim grade were recorded. The prognostic performance of radiological and clinical scoring systems, NLR at admission, and serum CRP levels at 48 hours were compared for severity and mortality according to the revised Atlanta Criteria. The data were evaluated by calculating the receiver operator characteristic (ROC) curves and area under the ROC (AUROC). RESULTS Out of 80 patients, 19 (23.8%) had severe AP, and 9 (11.3%) died. The AUROC for the BISAP score was 0.836 (95%CI: 0.735-0.937), with the highest value for severity. With a cut-off of BISAP ≥2, sensitivity and specificity were 68.4% and 78.7%, respectively. The AUROC for NLR was 0.915 (95%CI: 0.790-1), with the highest value for mortality. With a cut-off of NLR >11.91, sensitivity and specificity were 76.5% and 94.1%, respectively. Of all the radiological scoring systems, the EPIC score had the highest AUROC, i.e., 0.773 (95%CI: 0.645-0.900) for severity and 0.851 (95%CI: 0.718-0.983) for mortality, with a cut-off value ≥6. CONCLUSION The BISAP score and NLR might be preferred as early determinants of severity and mortality in AP. The EPIC score might be suggested from the current radiological scoring systems.

Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis

SUMMARY Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis BACKGROUND/AIMS To compare radiological scoring systems, clinical scores, serum C-reactive protein (CRP) levels and the neutrophil-lymphocyte ratio (NLR) for predicting the severity and mortality of acute pancreatitis (AP). MATERIALS AND METHODS Demographic, clinical, and radiographic data from 80 patients with AP were retrospectively evaluated. The harmless acute pancreatitis score (HAPS), systemic inflammatory response syndrome (SIRS), bedside index for severity in acute pancreatitis (BISAP), Ranson score, Balthazar score, modified computed tomography severity index (CTSI), extrapancreatic inflammation on computed tomography (EPIC) score and renal rim grade were recorded. The prognostic performance of radiological and clinical scoring systems, NLR at admission, and serum CRP levels at 48 hours were compared for severity and mortality according to the revised Atlanta Criteria. The data were evaluated by calculating the receiver operator characteristic (ROC) curves and area under the ROC (AUROC). RESULTS Out of 80 patients, 19 (23.8%) had severe AP, and 9 (11.3%) died. The AUROC for the BISAP score was 0.836 (95%CI: 0.735-0.937), with the highest value for severity. With a cut-off of BISAP ≥2, sensitivity and specificity were 68.4% and 78.7%, respectively. The AUROC for NLR was 0.915 (95%CI: 0.790-1), with the highest value for mortality. With a cut-off of NLR >11.91, sensitivity and specificity were 76.5% and 94.1%, respectively. Of all the radiological scoring systems, the EPIC score had the highest AUROC, i.e., 0.773 (95%CI: 0.645-0.900) for severity and 0.851 (95%CI: 0.718-0.983) for mortality, with a cut-off value ≥6. CONCLUSION The BISAP score and NLR might be preferred as early determinants of severity and mortality in AP. The EPIC score might be suggested from the current radiological scoring systems.

RESUMO Comparação dos sistemas de escores radiológicos, escores clínicos razão neutrófilo/linfócito e níveis séricos de proteína C-reativa para determinação da gravidade e mortalidade em casos de pancreatite aguda OBJETIVO Comparar sistemas de escores radiológicos, escores clínicos, os níveis séricos de proteína C-reativa (PCR) e a razão neutrófilo/linfócitos (RNL) como métodos de previsão de gravidade e mortalidade em casos de pancreatite aguda (PA). MATERIAIS E MÉTODOS Dados demográficos, clínicos e radiográficos de 80 pacientes com PA foram avaliados retrospectivamente. Os valores de Harmless Acute Pancreatitis Score (HAPS), Síndrome da Resposta Inflamatória Sistêmica (SIRS), Índice de Gravidade na Pancreatite Aguda à Beira do Leito (BISAP), escore de Ranson, escore de Balthazar, Índice Modificado de Gravidade por Tomografia Computadorizada (CTSI), escore de Inflamação Extrapancreática em Tomografia Computadorizada (EPIC) e grau renal foram registrados. O desempenho prognóstico dos sistemas de escores clínicos e radiológicos e RNL no momento da internação e os níveis séricos de PCR após 48 horas foram comparados quanto à gravidade, de acordo com os critérios de Atlanta revisados e mortalidade. Os dados foram avaliados pelo cálculo das curvas ROC e da área sob a curva ROC (AUROC). RESULTADOS De 80 pacientes, 19 (23,8%) tinham PA grave e 9 (11,3%) morreram. A AUROC para o escore BISAP foi de 0,836 (95%CI: 0.735-0.937), com o valor mais alto de gravidade. Com um valor de corte de BISAP ≥ 2 , a sensibilidade e a especificidade foram de 68,4% e 78,7%, respectivamente. A AUROC para o a RNL foi de 0,915 (95%CI: 0.790-1), com o valor mais alto de mortalidade. Com um valor de corte de RNL > 11,91, a sensibilidade e a especificidade foram de 76,5% e 94,1%, respectivamente. Entre os sistemas de escore radiológico, o EPIC apresentou o maior valor de AUROC, 0,773 (95%CI: 0.645-0.900) para gravidade e 0,851 (95%CI: 0.718-0.983) para mortalidade com um valor de corte ≥6. CONCLUSÃO O escore BISAP e a RNL podem ser preferíveis como determinantes precoces de gravidade e mortalidade na PA. O escore EPIC pode ser sugerido entre os atuais sistemas de escores radiológicos.

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage.

BACKGROUND: DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. PURPOSE: To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context-based reversible methods. MATERIAL AND METHODS: A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1-DUAL and 20 T2-SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography-angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well-established reversible binary compression methods PNG, JPEG-LS, JPEG-XR, CCITT-G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context-based (3D-RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. RESULTS: Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT-AAA, 56.7 to 0.3 MB for CT-liver. Moreover, compared to the performance of well-established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D-RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi-level images, contributes to compression performance. Although the performance of well-established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D-RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. CONCLUSION: Adaptive and context-based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re-produce visualizations for better teleradiology practices preserving all interaction mechanisms.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging. METHODS: A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results. RESULTS: The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE). CONCLUSION: Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.

How high is the inter-observer reproducibility in the LIRADS reporting system?

PURPOSE: To investigate the reproducibility of LIRADS v2014 and contribute to its widespread use in clinical practice. MATERIAL AND METHODS: This retrospective, single-centre study was conducted between January 2010 and October 2015. A total of 132 patients who had dynamic magnetic resonance imaging (MRI)/computed tomography (CT) images in the Picture Archiving and Communication Systems (PACS) with liver nodule were included in the study, 37 of whom had histopathology results. Five radiologists who participated in the study, interpreted liver nodules independently on different PACS stations according to the LIRADS reporting system and its main parameters. RESULTS: We determined that level of inter-observer agreement in the LR-1, LR-5, and LR-5V categories was higher than in the LR-2, LR-3, and LR-4 categories (κ = 0.522, 0.442, and 0.600 in the LR-1, LR-5, and LR-5V categories, respectively; κ = 0.082, 0.298, and 0.143 in the LR-2, LR-3, and LR-4 categories, respectively). The parameter that we observed to have the highest level of inter-observer agreement was venous thrombus (κ = 0.600). CONCLUSIONS: Our study showed that LIRADS achieves an acceptable inter-observer reproducibility in terms of clinical practice although it is insufficient at intermediate risk levels. We think that the prevalence of its use will be further increased with training related to the subject and the assignment of numerical values that express the probability of malignancy for each category and including the ancillary features in the algorithm according to clearer rules.

Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data.

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data. The Grayscale Softcopy Presentation State extension of the Digital Imaging and Communications in Medicine (DICOM) standard resolves this issue for two-dimensional (2D) data by introducing an extensive set of parameters, namely 2D Presentation States (2DPR), that describe how an image should be displayed. 2DPR allows storing these parameters instead of storing parameter applied images, which cause unnecessary duplication of the image data. Since there is currently no corresponding extension for 3D data, in this study, a DICOM-compliant object called 3D presentation states (3DPR) is proposed for the parameterization and storage of 3D medical volumes. To accomplish this, the 3D medical visualization process is divided into four tasks, namely pre-processing, segmentation, post-processing, and rendering. The important parameters of each task are determined. Special focus is given to the compression of segmented data, parameterization of the rendering process, and DICOM-compliant implementation of the 3DPR object. The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists. The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

StatXFinder: a web-based self-directed tool that provides appropriate statistical test selection for biomedical researchers in their scientific studies.

The improper use of statistical methods is common in analyzing and interpreting research data in biological and medical sciences. The objective of this study was to develop a decision support tool encompassing the commonly used statistical tests in biomedical research by combining and updating the present decision trees for appropriate statistical test selection. First, the decision trees in textbooks, published articles, and online resources were scrutinized, and a more comprehensive unified one was devised via the integration of 10 distinct decision trees. The questions also in the decision steps were revised by simplifying and enriching of the questions with examples. Then, our decision tree was implemented into the web environment and the tool titled StatXFinder was developed. Finally, usability and satisfaction questionnaires were applied to the users of the tool, and StatXFinder was reorganized in line with the feedback obtained from these questionnaires. StatXFinder provides users with decision support in the selection of 85 distinct parametric and non-parametric statistical tests by directing 44 different yes-no questions. The accuracy rate of the statistical test recommendations obtained by 36 participants, with the cases applied, were 83.3 % for "difficult" tests, and 88.9 % for "easy" tests. The mean system usability score of the tool was found 87.43 ± 10.01 (minimum: 70-maximum: 100). A statistically significant difference could not be seen between total system usability score and participants' attributes (p value >0.05). The User Satisfaction Questionnaire showed that 97.2 % of the participants appreciated the tool, and almost all of the participants (35 of 36) thought of recommending the tool to the others. In conclusion, StatXFinder, can be utilized as an instructional and guiding tool for biomedical researchers with limited statistics knowledge. StatXFinder is freely available at http://webb.deu.edu.tr/tb/statxfinder.

How Turkish radiology residents access information related to their profession in this social media and smartphone era.

PURPOSE: To evaluate the frequency of mobile technology and social media usage among radiology residents and their access to professional information. MATERIALS AND METHODS: A questionnaire consisting of 24 questions prepared using Google Drive was sent via e-mail to 550 radiology residents throughout the country. Of the 176 participating residents, 74 completed the survey via the internet, and 102 completed it at three different national radiology meetings. Response rates and its relationship with responses given to different questions were assessed. RESULTS: Hundred two male and 74 female residents participated in the survey. 141 (81.3%) residents thought that they had appropriate internet access in their department. The number of residents using a smartphone was 153 (86.9%). The android operating system (70, 45.8%) was the preferred operating system of respondants. Only 24 (15.7%) of the smartphone users thought that there were enough radiology related applications. "Radiology assistant" (18.9%), "Radiopedia" (7.8%) and "Radiographics" (7.8%) were the most utilized applications. Of the smartphone users, 87(56.9%) stated that they used cell phones in order to find radiological information, and the most used web pages were Google (165, 93.8%), Radiopaedia.org (129, 73.3%), Radiologyassistant.nl (135, 76.7%), and Pubmed (114, 64.8%). Social media usages were as follows: None (10, 5.7%), Facebook (139, 79%), Twitter (55, 31.3%), Google + (51, 29%) and YouTube (44, 25%). CONCLUSION: While smartphone usage rates among the residents were high, the use of radiology specific applications was not common. Social media usage was very common among residents.

Blunt trauma patient with esophageal perforation.

Traumatic perforation of the esophagus due to blunt trauma is a rare thoracic emergency. The most common causes of esophageal perforation are iatrogenic, and the upper cervical esophageal region is the most often injured. Diagnosis is frequently determined late, and mortality is therefore high. This case report presents a young woman who was admitted to the emergency department (ED) with esophageal perforation after having fallen from a high elevation. Esophageal perforation was diagnosed via thoracoabdominal tomography with ingestion of oral contrast. The present report discusses alternative techniques for diagnosing esophageal perforation in a multitrauma patient.

Fully automated liver segmentation from SPIR image series.

Accurate liver segmentation is an important component of surgery planning for liver transplantation, which enables patients with liver disease a chance to survive. Spectral pre-saturation inversion recovery (SPIR) image sequences are useful for liver vessel segmentation because vascular structures in the liver are clearly visible in these sequences. Although level-set based segmentation techniques are frequently used in liver segmentation due to their flexibility to adapt to different problems by incorporating prior knowledge, the need to initialize the contours on each slice is a common drawback of such techniques. In this paper, we present a fully automated variational level set approach for liver segmentation from SPIR image sequences. Our approach is designed to be efficient while achieving high accuracy. The efficiency is achieved by (1) automatically defining an initial contour for each slice, and (2) automatically computing weight values of each term in the applied energy functional at each iteration during evolution. Automated detection and exclusion of spurious structures (e.g. cysts and other bright white regions on the skin) in the pre-processing stage increases the accuracy and robustness. We also present a novel approach to reduce computational cost by employing binary regularization of level set function. A signed pressure force function controls the evolution of the active contour. The method was applied to ten data sets. In each image, the performance of the algorithm was measured using the receiver operating characteristics method in terms of accuracy, sensitivity and specificity. The accuracy of the proposed method was 96%. Quantitative analyses of results indicate that the proposed method can accurately, efficiently and consistently segment liver images.

Towards a web-based decision support tool for selecting appropriate statistical test in medical and biological sciences.

Statistical hypothesis testing is an essential component of biological and medical studies for making inferences and estimations from the collected data in the study; however, the misuse of statistical tests is widely common. In order to prevent possible errors in convenient statistical test selection, it is currently possible to consult available test selection algorithms developed for various purposes. However, the lack of an algorithm presenting the most common statistical tests used in biomedical research in a single flowchart causes several problems such as shifting users among the algorithms, poor decision support in test selection and lack of satisfaction of potential users. Herein, we demonstrated a unified flowchart; covers mostly used statistical tests in biomedical domain, to provide decision aid to non-statistician users while choosing the appropriate statistical test for testing their hypothesis. We also discuss some of the findings while we are integrating the flowcharts into each other to develop a single but more comprehensive decision algorithm.

Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing techniques.

The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.

Development of a medical module for disaster information systems.

This study aims to improve a medical module which provides a real-time medical information flow about pre-hospital processes that gives health care in disasters; transferring, storing and processing the records that are in electronic media and over internet as a part of disaster information systems. In this study which is handled within the frame of providing information flow among professionals in a disaster case, to supply the coordination of healthcare team and transferring complete information to specified people at real time, Microsoft Access database and SQL query language were used to inform database applications. System was prepared on Microsoft .Net platform using C# language. Disaster information system-medical module was designed to be used in disaster area, field hospital, nearby hospitals, temporary inhabiting areas like tent city, vehicles that are used for dispatch, and providing information flow between medical officials and data centres. For fast recording of the disaster victim data, accessing to database which was used by health care professionals was provided (or granted) among analysing process steps and creating minimal datasets. Database fields were created in the manner of giving opportunity to enter new data and search old data which is recorded before disaster. Web application which provides access such as data entry to the database and searching towards the designed interfaces according to the login credentials access level. In this study, homepage and users' interfaces which were built on database in consequence of system analyses were provided with www.afmedinfo.com web site to the user access. With this study, a recommendation was made about how to use disaster-based information systems in the field of health. Awareness has been developed about the fact that disaster information system should not be perceived only as an early warning system. Contents and the differences of the health care practices of disaster information systems were revealed. A web application was developed supplying a link between the user and the database to make date entry and data query practices by the help of the developed interfaces.